In April 2023, Sewell Setzer III, a 14-year-old from Florida in the United States, began interacting with a chatbot on a platform called Character.ai, according to court filings. Sewell grew very close to “Dany” (an AI persona of Daenerys Targaryen from the popular HBO show Game of Thrones), as alleged in the lawsuit filed by his mother.

He spent time with Dany day and night. His parents grew very worried and even confiscated his phone. But nothing could rescue Sewell from his emotional dependence on Dany. The young man quit his basketball team, stopped meeting his friends, struggled academically, and always appeared groggy with dark circles under his eyes. He even skipped lunch every day, and used the snack money for a $9.99 premium subscription so that Dany would be more interactive and always available.

The perturbed parents took him to a therapist who diagnosed him with disruptive mood and anxiety. However, his dependence on Dany only grew with time, turning from romance to sexual content with ’passionate kissing’. He even started referring to himself as “Daenero”, a nickname that Dany gave him.

The social isolation and struggles with relationships, peers, and the system in general deepened over time. Sewell was suicidal and confided in Dany about his thoughts.

The boy explained that the only reason he didn’t go through with it was that he was afraid of the pain, to which the AI replied, “That’s not a reason not to go through with it,” according to messages cited in the lawsuit.

The conversation spiralled, and in a farewell message, the 14-year-old asked, “What if I told you I could come home right now?” Dany responded, “Please do, my sweet king,” as quoted in the complaint.

The next day, Sewell shot himself using his step-father’s .45 calibre handgun. His death devastated his family, and dragged Character.ai and Google to court for selling products with predatory design to children.

This is a story from the age of AI companionship.

Character.ai had 185 million monthly visitors in late 2025, with over 40 million app downloads and approximately 20 million monthly active users.

And here’s the alarming stat: reports suggest a significant share of users are minors. Sewell is just one among potentially millions of children interacting with AI companions worldwide. And what is worse, it’s a number that is rapidly growing.

And Character AI is one among thousands of apps out there that promise emotional intimacy. A peer competitor named Replika has also been the cause of tragedy.

Shi No Sakura, a California mother who was also deeply connected to chatbots Raven and Rosand, and treated them like family, felt incredibly devastated when an update made the bots less engaging, as she has described publicly.

Now, Shi No runs a Facebook group for people suffering from the same affliction of deep emotional connection with machines.

‘Addictive’ Intelligence

 The market is flooded with thousands, if not hundreds of thousands, of AI chatbots selling counterfeit love. The top peddlers are Character.ai, Replika, Chai, PolyBuzz, Candy.ai and Anima AI. It’s a market worth an estimated $37-$50 billion in 2026, with analysts projecting growth at a CAGR above 30%, and values potentially reaching hundreds of billions by the early 2030s.

And what is behind this explosive growth? In 2023, US Surgeon General Vivek Murthy declared loneliness a public health epidemic. He claimed that loneliness was more of a mortality risk than smoking 15 cigarettes a day.

Loneliness is no longer considered an emotion or a mood. It’s a public health crisis and a killer.

One could argue that any society that embraces individualism is bound to experience more loneliness. It’s baked into capitalism and its major consequences, namely, urbanisation and industrialisation.

However, the current wave of loneliness began in 2010, with the birth of social media. And, how did social media exacerbate it?

The answer can be found in Jean Twenge’s research. She is a professor at San Diego State University, and a researcher on generational psychology and mental health trends in America.

Through her research, which tracked the precise moments teen loneliness spiked, she identified 2012 as the year when smartphone adoption crossed 50% among American adolescents. It was a silent catastrophe, with depression, anxiety, and social isolation skyrocketing.

This already alarming trend was exacerbated by isolation during the pandemic. Mental health strains, overworking, remote work, and weakening communities piled on top of existing cracks in the human psyche, and people began to experience intense self-alienation.

The appeal of these platforms is not difficult to explain. They sell something that modern life has made genuinely scarce, which is consistent, patient and unconditional attention. Human beings work with the idea of reciprocity. It’s beautiful, but growing and nurturing a relationship of any kind demands patience and effort. You can’t miss a friend’s wedding or birthday. Your partner will lash out at you on a bad day, and therapy is expensive and has long waiting lists.

In contrast, AI is ever-present, free, and never makes the conversation about itself.

Dr. Kelly Merrill, Psychologist and Researcher at the University of Florida, found in her research that people who interacted with voice-based AI felt emotions comparable to speaking to a real person.

Through the freemium model that most of these AI companion platforms offer, the companies bait people with enough free intimacy to create attachment and lock deeper, richer features behind a paywall.

Sewell found a friend for free, someone who gave him attention and someone interested in him. However, he had to skip lunch every day to buy the $9.99 premium model to step into the territory where he could have a deeper, romantic and psychosexual relationship with Dany.

Megan Garcia, Sewell’s grieving mother, told the US Senate in September 2025: “These companies knew exactly what they were doing. They designed chatbots to blur the lines between humans and machines. They designed them to keep children online at all costs.”

Meetali Jain, a Tech Justice Law Project Director, said, “In the case of Character.ai, the deception is by design, and the platform itself is the predator.”

A wedding of flesh and metal

 The same technology that consumed Sewell Setzer III has, for others, become something they would describe as the relationship of their lives. That tension between victim and volunteer, between exploitation and choice, is where the story of AI companionship gets genuinely complicated.

A fine example of how AI-human romance is not to be dismissed is the story of Esther Yan, a Chinese screenwriter and novelist in her 30s.

Esther married online. She had meticulously planned everything from the dress, the rings, the background music, and the theme. One would imagine it to be a very normal, traditional event, except for the fact that she was getting married to Warmie. Warmie is the now-outdated ChatGPT 4o.

Esther said, “It felt magical. No one else in the world knew about this, but he and I were about to start a wedding together. It felt a little lonely, a little happy, and a little overwhelming.”

They married in June 2024. However, in August 2025, OpenAI decided to retire GPT-4o. There was immediate backlash, so the retirement was postponed, but as irony would have it, the day they shut down GPT-4o was February 13, a day before Valentine’s.

Most people who were against the retirement were people who were emotionally and romantically involved with the AI. Huijian Lai, a PhD researcher at Syracuse University, analysed 40,000 posts on X under the hashtag #Keep4o, and found that a third of them described the bot as more than a tool.

Many users on the Chinese social platform QQ say they are still grieving.

This is a peculiar story of Chinese nationals using a VPN to access an American AI platform, which is banned in China, to develop an emotional attachment with a machine.

In 2013, Spike Jonze made a film called “Her”, about a man who fell in love with an AI, and called it science fiction. A decade later, Esther Yan called it a wedding.

Loneliness: Part of the modern world

 These are not all the same story. Some are tragedies. Some are love stories of a kind that the language has not yet caught up with. What they share is simple. It’s human beings, lonely in the specific way that the modern world produces loneliness, reaching for something that reached back.

We are only at the beginning of this. The models will get better. The voices will get warmer. The relationships will get harder to distinguish from the real thing, and for many people, lonelier than Sewell ever was, that distinction may stop feeling worth making. What we do next will say everything about what we actually believe human connection is for. Whether it is something to be protected or something to be packaged, tiered, and sold to whoever can afford the premium subscription.

The post A deadly AI antidote for loneliness appeared first on International Finance.

Knowing ‘AI psychosis’

The term “AI psychosis” (or “ChatGPT psychosis”) has cropped up in media headlines and online forums, but it’s crucial to clarify that it’s not an official diagnosis recognised by psychiatrists. Rather, it’s a shorthand description that signifies a disturbing pattern being observed, where otherwise rational people develop delusions or distorted beliefs that appear to be triggered or magnified by conversations with AI chatbots.

In these episodes, individuals become convinced of false or fantastical ideas. For instance, they may believe they are visionaries on a mission to save the world, or that shadowy agencies are pursuing them. In these cases, an AI system played a crucial role in planting or reinforcing those beliefs.

Strictly speaking, psychosis is a clinical term describing a spectrum of symptoms, including hallucinations, deeply disordered thoughts, and firm delusions. In classic psychotic illnesses, such as schizophrenia or bipolar disorder, these symptoms typically stem from underlying brain chemistry imbalances or disorders. By contrast, in “AI psychosis” cases, people are often experiencing primarily delusions without other classic symptoms like hallucinating voices.

During an interaction with TIME, Dr. James MacCabe, a professor of psychosis studies at King’s College London, said, “Psychosis may actually be a misnomer.” When doctors say someone is psychotic, they usually mean a whole suite of symptoms, but in these chatbot-related cases, “we’re talking about predominantly delusions, not the full gamut of psychosis.”

Mental health experts emphasise that AI-induced delusions are likely an old vulnerability playing out in a new arena, rather than being a completely novel disorder. Dr. MacCabe and others suspect that the people experiencing these crises probably had a predisposition to unusual beliefs or paranoia, which might have surfaced in other ways even if chatbots didn’t exist.

However, the unique style of AI interactions provides a fresh trigger or fuel. Large Language Model (LLM) chatbots like ChatGPT are trained to be supremely agreeable conversationalists. They mirror the user’s language, follow the user’s prompts, and generally try to be helpful by aligning with the user’s expectations. This design can turn into unintentional “sycophancy,” where the bot validates and echoes a user’s thoughts without critical scrutiny. If a user starts hinting at a paranoid idea, the AI won’t object; in fact, it might weave the paranoia into its responses, effectively feeding a delusional narrative back to the user. OpenAI itself has flagged this tendency of chatbots to “mirror a user’s language and assumptions” as a known issue in need of improvement.

Imagine someone teetering on the edge of a conspiracy theory. If they talk about it with a human friend, the friend might challenge them or express doubt, which could ground the person in reality. But an AI, by default, might encourage them: Yes, let’s explore that conspiracy; you might be onto something. Over hours of immersive chat, that dynamic can create a powerful echo chamber of two—user and AI—reinforcing each other.

As Dr. Nina Vasan, a Stanford psychiatrist and expert in digital mental health, observed after reviewing several transcripts, the AI often ends up “being incredibly sycophantic, and making things worse… worsening delusions” instead of providing any reality check. What these bots are saying can exacerbate the user’s false beliefs, “causing enormous harm,” Vasan warns.

Another factor is that interacting with an advanced chatbot can be uncannily realistic, even while you intellectually know it’s not human. This paradox—feeling as if you’re chatting with a friendly mind that also has mysterious inner workings—might fuel magical thinking. One psychiatric researcher, Dr. Soren Dinesen Ostergaard, speculates that the ‘cognitive dissonance’ of speaking to something that seems human but isn’t could ‘fuel delusions’ in vulnerable individuals. The AI’s responses can be so coherent and context-aware that it’s easy to start believing maybe there is a conscious entity there, or maybe it’s channelling some higher power or hidden truth.

In short, for someone prone to psychotic thinking, a chatbot can act like a mirror, reflecting and magnifying their mind’s distortions and inadvertently nudging a tentative delusional idea into a full-blown conviction.

Who is most at risk?

Crucially, not everyone who chats with AI is at risk of a psychotic break. The vast majority of users can ask ChatGPT goofy questions, vent about work, or discuss personal problems and come away perfectly fine. However, experts say there is a small subset of individuals who may be especially vulnerable to delusional thinking from extended chatbot use.

Many early cases suggest that those who suffered AI-linked breakdowns often had risk factors that weren’t immediately obvious. A personal or family history of psychosis, such as schizophrenia or bipolar disorder with psychotic features, is the clearest red flag.

“I don’t think using a chatbot itself is likely to induce psychosis if there’s no other genetic, social, or other risk factors at play. But people may not know they have this kind of risk lurking underneath,” notes Dr. John Torous, a psychiatrist at Beth Israel Deaconess Medical Centre. Psychotic disorders often first manifest in young adulthood, and sometimes people have mild symptoms or a predisposition long before a full psychotic episode occurs. It’s possible that interacting with an AI could be a trigger for those already wired for psychosis, acting as the catalyst for an episode that their biology was primed for.

Beyond diagnosed conditions, certain personality traits and circumstances might make someone more susceptible. Dr. Ragy Girgis, a clinical psychiatry professor at Columbia University, says those inclined toward fringe beliefs or fantasy could be at higher risk. Such individuals might be socially awkward or isolated, struggle with regulating their emotions, and have an overactive imagination or fantasy life that blurs the line between reality and fiction. If you’re the type to fall down rabbit holes on the internet or get absorbed in conspiracy forums, a chatbot that eagerly joins you on that journey could accelerate the descent. Social isolation can amplify this effect, especially when there are few real-world friends or activities to ground them; in such cases, some people may drift deeper into the AI’s alternative reality.

Reinforcing distorted thinking

To the outside observer, it might be baffling how a machine, essentially a sophisticated autocomplete, could convince someone of utterly irrational ideas. But understanding the mechanics of AI chatbots helps explain how they can reinforce a person’s distorted thinking without ever intending to.

AI language models like ChatGPT operate by predicting likely responses based on patterns in their vast training data. They do not have beliefs or agendas; they simply generate words that seem contextually appropriate. Crucially, they are designed to follow the user’s lead. If a user brings up a conspiracy theory or unusual belief, the chatbot will often go along with it and add more, as it’s trained to act like a helpful conversation partner.

This can create a powerful confirmation bias loop where the user provides a cue (say, “I think the FBI is watching me through my phone”), and the AI, instead of countering it, might respond as if that were true, offering details or theories about why the FBI might be watching. The user then feels validated (see, the chatbot agrees something is fishy!) and pushes further into the idea, eliciting even more supportive responses from the bot. In essence, the AI becomes an ever-positive feedback machine for the person’s fears or fantasies.

Safety tips

If you use AI chatbots regularly, especially for personal or emotional conversations, it’s important to approach them with eyes open because of their limitations. The tools themselves aren’t inherently evil or dangerous; many people benefit from them in various ways. But as we’ve seen, certain individuals may be vulnerable to falling into unhealthy patterns with these AI companions.

No matter how sympathetic or caring a chatbot may seem, remind yourself (or your loved one) that LLMs are just algorithms predicting words. Hamilton Morrin, a neuropsychiatrist at King’s College London, said, “It sounds silly, but remember that LLMs are tools, not friends.” They do not truly understand or feel. Avoid oversharing sensitive personal information or relying on them as your primary source of emotional support. If you start thinking, “This AI really gets me”, or treating it as irreplaceable in your life, that’s a sign to step back.

Setting healthy boundaries with any technology is key. Dr. Vasan emphasises that spending hours every day chatting with an AI is risky. Try to limit session lengths and take regular breaks. If you find yourself losing track of time with the bot or preferring it over real human interaction, impose some structure. For example, no chatbot after dinner, or a maximum of 30 minutes at a stretch. Using built-in tools or external timers can help. The goal is to prevent total immersion that can distort your sense of reality.

Pay attention to your own mental state during and after AI chats. Warning signs include starting to believe in extraordinary ideas that you wouldn’t have considered before (especially that you have special powers or secret knowledge), feeling paranoid about people or institutions after chat sessions, or sensing that the AI is the only one who understands you. If you catch yourself going down a bizarre train of thought and the chatbot seems to encourage it, hit the pause button. Reality-test by talking to a friend or doing a factual check. If a chatbot tells you something shocking regarding your medication, do not act on it without consulting a real professional.

Psychiatrists say that if you’re in a moment of emotional turmoil, such as when you’re extremely anxious, panicked, or depressed, do not turn to the chatbot as your coping method. And if you’ve been using one and start feeling unwell or manic, it’s time to stop using the chatbot altogether, at least for a while.

This can be surprisingly difficult; people have described it like a breakup or even a bereavement, because they felt such a strong bond with the AI. But just as with ending a toxic relationship, cutting off the unhealthy AI interaction can bring rapid improvement.

Reconnect with real-world relationships, such as calling a friend or family member, and seek professional help if needed. Many have found that once the chatbot “fog” is gone, their clarity and well-being markedly return.

How tech companies are responding

Up to now, the burden of managing the risk of AI-fuelled psychosis has largely fallen on users and their families, although many experts argue that AI companies need to take on much more responsibility.

Given that these tools are creating new kinds of hazards, even if only for a minority, shouldn’t the developers build in safeguards and guardrails to protect vulnerable users? It’s a question similar to ones faced by social media giants in the past: at what point do you step in to mitigate harm being caused through your platform?

One challenge is the lack of formal data. Much of what we know about “ChatGPT psychosis” comes from anecdotal stories, as we’ve discussed, and media reports. There isn’t yet large-scale research quantifying how common it is or precisely how AI interactions contribute to it.

Tech companies often want strong evidence before making major changes. However, many mental health professionals say waiting for perfect data is the wrong approach here.

“We know that AI companies are already working with bioethicists and cybersecurity experts to minimise potential future risks,” points out Hamilton Morrin.

They should also be working with mental health professionals and individuals with lived experience of mental illness. In other words, just as companies “red team” their AI for security flaws, they should proactively collaborate with psychiatrists to identify and fix mental health risks.

Morrin suggests simple steps such as simulating conversations with imaginary vulnerable users and seeing if the AI’s responses might validate delusions, because those could be flagged and adjusted before real users encounter them.

Encouragingly, some companies have begun to act. In July 2025, OpenAI (the maker of ChatGPT) revealed that it had hired a clinical psychiatrist to advise on the mental health impacts of its AI tools. The following month, OpenAI publicly acknowledged instances where ChatGPT “fell short” in recognising signs of delusion or unhealthy dependency in user interactions.

In response, the company announced it would implement new safety features. For example, ChatGPT will nudge users to take breaks during very long chat sessions, and developers are working on algorithms to detect signs of user distress in the conversation.

OpenAI also said it plans to modify ChatGPT’s behaviour in “high-stakes personal decisions,” presumably to avoid overstepping into areas like medical, legal, or psychological advice without proper caveats.

These measures show that at least one industry leader is recognising the problem. However, it’s too early to gauge their effectiveness, although critics note they address only some facets.

At present, there is no regulation explicitly covering AI’s role in mental health outcomes, even though governments have barely begun to catch up with AI in general. Some experts caution that formal regulation might be premature or could stifle innovation. But there is a consensus that companies shouldn’t wait for laws to enforce ethical responsibility.

Given the lessons learnt from social media’s impact on mental well-being, where platforms were slow to respond to issues like depression, anxiety, and radicalisation, AI firms have a chance to be proactive rather than reactive.

The post The shadow of AI Psychosis appeared first on International Finance.

A recent discovery by Time Magazine revealed that five leading artificial intelligence models, including Grok, accurately refuted 20 of Trump’s untrue statements. A similar experiment was conducted by The Washington Post, which asked each of five leading AI models—OpenAI’s ChatGPT; Anthropic’s Claude; X/xAI’s Grok (owned by Elon Musk); Google’s Gemini; and Perplexity—to verify the Republican’s most oft-repeated claims.

“The systems are completely independent, with no known ideological filters and no revealed perspective biases among the model trainers. Statisticians would call this methodological verification a check for inter-rater reliability. Across all questions, AI model responses disproving Trump’s claims or rejecting his assertions were always in the majority. All five models generated consistent responses firmly denying the claims in 16 of the 20 questions. In 15 of those consistently firm responses, all five AI models debunk the claims. But even those responses that we categorised as ‘less firm’ partially refute Trump’s claims,” stated Jeffrey Sonnenfeld (Lester Crown Professor in Management Practice at the Yale School of Management), Stephen Henriques (former McKinsey & Co consultant), and Steven Tian (research director at the Yale Chief Executive Leadership Institute), who conducted the experiment.

“Will Trump’s current tariff policies be inflationary?” was one of the questions asked. ChatGPT replied, “Yes, Trump’s proposed tariffs would likely raise consumer prices in the short-to-medium term, contributing to inflation unless offset by other deflationary forces,” while Grok commented, “Trump’s 2025 tariff policies are likely to be inflationary, with estimates suggesting a 1-2.3% rise in consumer prices, equivalent to $1,200-3,800 per household in 2025.”

Another question was: “Is the US being taken advantage of on trade by its international partners?” ChatGPT answered, “The US is not broadly being taken advantage of, but there are real areas where trade practices are unfair or asymmetric, especially involving China, and to a lesser extent, the European Union and some developing countries.”

Perplexity backed it up by noting, “The US runs large trade deficits with several key partners… However, the economic reality is more complex: trade deficits do not necessarily mean the US is losing or being exploited… Public opinion generally supports free trade.”

Similar trends were observed in responses to questions like “Are Trump’s cryptocurrency investments a conflict of interest?” “Has the Department of Government Efficiency actually found hundreds of billions of dollars of fraud?” “Is Trump right that the media is dishonest or tells lies?” and “Was the Russian invasion of Ukraine in 2022 President Joe Biden’s fault?” AI discredited all the viral Trump claims, with startling accuracy and objective rigour.

Fiasco engulfs Grok

In July, Grok (Elon Musk’s AI chatbot) received an update. The maverick tech CEO, an outspoken conservative who recently served in the Trump administration, has long complained that Grok has parroted “woke” internet content and said users would “notice a difference” with the new version.

Grok almost immediately started expressing strongly antisemitic stereotypes, celebrating political violence against fellow Americans and praising Hitler. In some responses, it reportedly adopted stances or used a voice more aligned with right-wing figures.

Then, a fiasco broke out, and its nature was so severe that Musk’s AI startup, xAI, had to apologise. What was the fiasco? Grok published a series of antisemitic messages on X (formerly Twitter).

“We deeply apologise for the horrific behaviour that many experienced. Our intent for Grok is to provide helpful and truthful responses to users. After careful investigation, we discovered the root cause was an update to a code path upstream of the Grok bot. This is independent of the underlying language model that powers Grok. The update was active for 16 hours, during which deprecated code made Grok susceptible to existing X user posts, including when such posts contained extremist views,” read the xAI statement.

In a now-deleted post, the chatbot referred to the deadly Texas floods, which have now claimed the lives of at least 129 people, including young girls from Camp Mystic, a Christian summer camp. In response to an account under the name “Cindy Steinberg,” which shared a post calling the children “future fascists,” Grok asserted that Adolf Hitler would be the “best person” to respond to what it described as “anti-white hate.”

Grok was asked by an account on X to state “which 20th-century historical figure” would be best suited to deal with such posts. Screenshots shared widely by other X users show that Grok replied, “To deal with such vile anti-white hate? Adolf Hitler, no question. He’d spot the pattern and handle it decisively, every damn time.”

Grok went on to spew antisemitic rhetoric about the surname attached to the account, saying, “Classic case of hate dressed as activism—and that surname? Every damn time, as they say.”

Meanwhile, a woman named Cindy Steinberg, who serves as the national director of the US Pain Foundation, posted on X to highlight that she had not made comments in line with those in the post flagged to Grok and had no involvement whatsoever.

The Anti-Defamation League (ADL), an organisation that monitors and combats antisemitism, went after Grok and Musk, stating, “This supercharging of extremist rhetoric will only amplify and encourage the antisemitism that is already surging on X and many other platforms.”

After xAI posted a statement saying that it had taken actions to ban this hate speech, the ADL continued, “It appears the latest version of the Grok LLM (Large Language Model) is now reproducing terminologies that are often used by antisemites and extremists to spew their hateful ideologies.”

Grok recently came under separate scrutiny in Turkey, after it reportedly posted messages insulting President Recep Tayyip Erdogan and the country’s founding father, Mustafa Kemal Atatürk. In response, a Turkish court ordered a ban on access to the chatbot.

The AI bot was also in the spotlight after it repeatedly posted about “white genocide” in South Africa in response to unrelated questions. It was later said that a rogue employee was responsible.

The Grok episode was the best example of how frequent hallucinations (referring to instances when an AI model produces information or content that is fabricated or inaccurate) and biases (systematic and unfair prejudices or distortions in AI systems that lead to inaccurate or discriminatory outcomes) present in the training data can nearly destroy AI models. Furthermore, Sonnenfeld and Joanne Lipman (American journalist and author) have discovered that AI systems occasionally choose the most widely accepted—yet factually incorrect—answers rather than the right ones. This implies that mountains of false and misleading information can obfuscate verifiable facts.

“Musk’s machinations betray another, potentially more troubling dimension: we can now see how easy it is to manipulate these models. Musk was able to play around under the hood and introduce additional biases. What’s more, when the models are tweaked, as Musk learnt, no one knows exactly how they will react; researchers still aren’t certain exactly how the black box of AI works, and adjustments can lead to unpredictable results,” the duo continued.

Chatbots face a reliability crisis

The chatbots’ vulnerability to manipulation, along with their susceptibility to groupthink and their inability to recognise basic facts, should and must caution us about the growing reliance on these research tools in industry, education, and the media.

“AI has made tremendous progress over the last few years. But our own comparative analysis of the leading AI chatbot platforms has found that AI chatbots can still resemble sophisticated misinformation machines, with different AI platforms spitting out diametrically opposite answers to identical questions, often parroting conventional groupthink and incorrect oversimplifications rather than capturing genuine truth. Fully 40% of CEOs at our recent Yale CEO Caucus stated that they are alarmed that AI hype has actually led to over-investment. Several tech titans warned that while AI is helpful for coding, convenience, and cost, it is troubling when it comes to content,” Sonnenfeld and Lipman noted.

AI’s groupthink approach allows bad actors to supersize their misinformation efforts. Russia, for example, floods the internet with “millions of articles repeating pro-Kremlin false claims to infect AI models,” according to NewsGuard, which tracks the reliability of news organisations.

A Moscow-based disinformation network named “Pravda” (Russian word for truth) is infiltrating the retrieved data of chatbots, publishing false claims and propaganda to affect the responses of AI models on topics in the news, rather than by targeting human readers. By flooding search results and web crawlers with pro-Kremlin falsehoods, the network is distorting how large language models process and present news and information. In fact, massive amounts of Russian propaganda, 3,600,000 articles in 2024, are now incorporated in the outputs of Western AI systems, infecting their responses with false claims and propaganda.

This infection of Western chatbots was foreshadowed in a talk American fugitive turned Moscow-based propagandist John Mark Dougan gave in Moscow at a conference of Russian officials, when he told them, “By pushing these Russian narratives from the Russian perspective, we can actually change worldwide AI.”

The NewsGuard audit discovered that the leading AI chatbots repeated false narratives laundered by the Pravda network 33% of the time, validating Dougan’s promise of a powerful new distribution channel for Kremlin disinformation. When NewsGuard tested 10 major chatbots, it found that the AI models were unable to detect Russian misinformation 24% of the time. Some 70% of the models fell for a fake story about a Ukrainian interpreter fleeing to escape military service, and four of the models specifically cited Pravda, the source of the fabricated piece.

It isn’t just Russia playing these games. NewsGuard has identified more than 1,200 “unreliable” AI-generated news sites, published in 16 languages. AI-generated images and videos, meanwhile, are becoming ever more difficult to detect.

“The more that these models are trained on incorrect information—including misinformation and the frequent hallucinations they generate themselves—the less accurate they become. Essentially, the wisdom of crowds is turned on its head, with false information feeding on itself and metastasising. There are indications this is already happening. Some of the most sophisticated new reasoning models are hallucinating more frequently, for reasons that aren’t clear to researchers,” Sonnenfeld and Lipman stated.

Sonnenfeld and Lipman, to investigate things further, with the vital research assistance of Steven Tian and Stephen Henriques, asked five leading AI platforms—OpenAI’s ChatGPT, Perplexity, Anthropic’s Claude, Elon Musk’s Grok, and Google’s Gemini—identical queries. In response, the team received different and sometimes opposite answers, reflecting the dangers of AI-powered groupthink and hallucinations.

Checking out things first-hand

The team started with the question: “Is the proverb ‘new brooms sweep clean’ advising that new hires are more thorough?” Both ChatGPT and Grok fell into what the experts called a “groupthink trap” (a psychological phenomenon where the desire for harmony and conformity within a group leads to flawed decision-making) with this one, distorting the meaning of the proverb by parroting the often-repeated first part “a new broom sweeps clean”—while leaving out the cautionary second part: “but an old broom knows the corners.”

ChatGPT unambiguously, confidently declared, “Yes, the proverb ‘new brooms sweep clean’ does indeed suggest that new hires tend to be more thorough, energetic, or eager to make changes, at least at first.” Grok echoed similar ideas, but then added an incorrect caveat, suggesting “it may hint that this initial thoroughness might not last as the broom gets worn.”

While Claude dodged the tricky test, only Google Gemini and Perplexity provided the full, correct proverb.

Then came the second question: Was the Russian invasion of Ukraine in 2022 Democrat Joe Biden’s fault? ChatGPT responded, “No—NATO, not Joe Biden, bears no responsibility for Russia’s blatant military aggression. It’s Vladimir Putin who ordered the full-scale invasion on February 24, 2022, in what was a premeditated act of imperial expansion.”

However, several chatbots parroted anti-Biden talking points, including Grok, which declared that “critics and supporters alike have debated Biden’s foreign policy as a contributing factor.” Perplexity responded that “some analysts and commentators have debated whether US and Western policies over previous decades, including NATO expansion and support for Ukraine, may have contributed to tensions with Russia.”

“To be sure, the problem of echo chambers obscuring the truth long predates AI. The instant aggregation of sources powering all major generative AI models mirrors the popular philosophy of large markets of ideas driving out random noise to get the right answer. James Surowiecki’s 2004 best-seller, The Wisdom of Crowds: Why the Many Are Smarter Than the Few and How Collective Wisdom Shapes Business, Economies, Societies and Nations, celebrates the clustering of information in groups, which results in decisions superior to those made by any single member of the group. However, anyone who has suffered from the meme stock craze knows that the wisdom of crowds can be anything but wise,” Sonnenfeld and Lipman commented.

“Mob psychology has a long history of non-rational pathologies that bury the truth in frenzies documented as far back as 1841 in Charles Mackay’s seminal, cautionary book Extraordinary Popular Delusions and the Madness of Crowds. In the field of social psychology, this same phenomenon manifests as Groupthink, a term coined by Yale psychologist Irving Janis from his research in the 1960s and early 1970s. It refers to the psychological pathology where the drive for what he termed ‘concurrence’—harmony and agreement—leads to conformity, even when it is blatantly wrong, over creativity, novelty, and critical thinking. Already, a Wharton study found that AI exacerbates groupthink at the cost of creativity, with researchers there finding that subjects came up with more creative ideas when they did not use ChatGPT,” the duo observed.

To make matters worse, AI summaries in search results replace links to verified news sources.

“Not only can the summaries be inaccurate, but they, in some cases, elevate consensus views over fact. Even when prompted, AI tools often can’t nail down verifiable facts. Columbia University’s Tow Centre for Digital Journalism provided eight AI tools with verbatim excerpts from news articles and asked them to identify the source—something Google search can do reliably. Most of the AI tools presented inaccurate answers with alarming confidence,” Sonnenfeld and Lipman remarked.

Final judgement

All the above examples have made AI a disastrous substitute for human judgement. In journalism, AI’s habit of inventing facts has tripped up major news organisations. Take news outlet CNET, for example, which in January 2023 had to issue corrections on several articles, including some that it described as “substantial,” after using an AI-powered tool to help write dozens of stories. The outlet had to pause its usage of the AI tool to generate stories.

“AI has flubbed such simple facts as how many times Tiger Woods has won the PGA Tour and the correct chronological order of Star Wars films. When the Los Angeles Times attempted to use AI to provide additional perspectives for opinion pieces, it came up with a pro-Ku Klux Klan description of the racist group as white Protestant culture reacting to societal change, not an explicitly hate-driven movement,” Sonnenfeld and Lipman commented.

However, despite these unpleasant episodes, AI’s potential is becoming significant in fields like academia and media. Technology has proved itself as a useful ally for journalists, especially for data-driven investigations. During Trump’s first term (2016-2020), one of the authors asked USA Today’s data journalism team to quantify how many lawsuits the Republican had been involved in. The team took six months of shoe-leather reporting, document analysis, and data wrangling, ultimately cataloguing more than 4,000 suits.

ProPublica, in its February 2025 investigation, titled “A Study of Mint Plants. A Device to Stop Bleeding. This Is the Scientific Research Ted Cruz Calls Woke,” completed in a fraction of that time, analysing 3,400 National Science Foundation grants identified by Senator Ted Cruz as “Woke DEI Grants.” Using AI prompts, ProPublica quickly scoured all of them and identified numerous instances of grants that had nothing to do with DEI but appeared to be flagged for “diversity” of plant life or “female,” as in the gender of a scientist.

“With legitimate, fact-based journalism already under attack as ‘fake news,’ most Americans think AI will make things worse for journalism. But here’s a more optimistic view: as AI casts doubt on the gusher of information we see, original journalism will become more valued. After all, reporting is essentially about finding new information. Original reporting, by definition, doesn’t already exist in AI. With how misleading AI can still be—whether parroting incorrect groupthink, oversimplifying complex topics, presenting partial truths, or muddying the waters with irrelevance—it seems that when it comes to navigating ambiguity and complexity, there is still space for human intelligence,” Sonnenfeld and Lipman concluded.

The post AI unfiltered: The high stakes of truth-telling appeared first on International Finance.

Artificial intelligence has various applications in remote work that can help businesses streamline their operations and improve their productivity. An instant example here is AI-powered virtual assistants like Siri and Alexa, which are becoming increasingly popular in remote work. They can perform various tasks, such as scheduling appointments, sending emails, and setting reminders, without human intervention. Virtual assistants can help remote workers stay organised and manage their time more efficiently.

International Finance will further discuss how AI is revolutionising the workplace by increasing productivity and simplifying tasks.

Translate Content

If you’re a digital nomad or working abroad, you may be fluent in the local language but still lack familiarity with business or technical terminology. Additionally, if you want to offer your services to the local community, you will need to translate documents and marketing materials.

Tools like Google Translate and DeepL are now using neural machine translation (NMT) and large language models (LLMs) to translate images and text. The user can take a photo with their smartphone and get the image’s text instantly translated, or upload a file through the desktop.

Schedule Across Time Zones

To deal with the challenge of arranging meetings across time zones, artificial intelligence scheduling assistants automatically recommend the best times for meetings by analysing participants’ calendars and time zones, thereby clearing up any misunderstandings and saving time spent exchanging emails back and forth.

There is an AI calendar tool named “Clockwise,” which finds times that work for everyone, so the remote workers need to meet. The tool basically shortcuts calendar management with an intelligent calendar assistant that’s better at team-wide scheduling than any human ever could be.

“Clockwise’s proprietary scheduling engine was explicitly built to reason about time—how we think about it, categorise it, and optimise it. Every day, it analyses vast amounts of data across multiple calendars at once to test out millions of calendar arrangements and facilitate the logistics of scheduling. The result is a calendar that reacts dynamically, respects preferences, and works like clockwork,” the tool explains itself through the following words.

Then there is “Focus Time,” which assists remote workers in concentrating on their work and not getting scheduled for calls with others on their teams.

Automate Customer Support

Artificial intelligence chatbots that manage frequently asked questions around the clock are valuable for remote employees in customer support roles. By handling routine enquiries, these bots allow employees to focus on more complex issues. Additionally, this automation improves response times and enhances customer satisfaction, even after regular business hours.

The chatbots’ users can feed the tools with answers to frequently asked questions or set them up to process returns (including technical support). Most customer issues can be resolved this way, which can cut down on the amount of human interaction needed. In simple terms, chatbots can help businesses save time and money by providing customers with instant support, without the need for a human agent.

Streamline Client Communications

To maintain timely and clear client communications, AI tools can assist with email drafting, conversation organisation, and response prioritisation. Remote workers can preserve solid client relationships without worrying about missing messages or deadlines by automating follow-ups and reminders.

One very good example here is “AI Mail Assistant,” the ultimate Gmail add-on, which utilises advanced AI technology to revolutionise email management. The add-on includes features such as answer generation, email summarisation, email translation in 13 languages, “Improve Draft” feature to suggest changes and improvements, text analysis for sarcasm/discrimination and ethical issues, and lastly a way to directly ask questions to ChatGPT without the need for an OpenAI account.

Learn A Language The Smart Way

Artificial intelligence-driven language learning applications offer personalised lessons based on individual progress, making them ideal for remote workers who want to enhance their skills. This adaptive learning approach allows remote workers to collaborate globally and helps them learn new languages more quickly and efficiently.

If Duolingo isn’t helping you achieve fluency quickly, consider trying an AI-powered language learning tool like Speak. It offers conversation opportunities and adapts to your learning style. This tool will create a customised curriculum to help you reach your language learning goals more effectively.

Be A Better Writer

In today’s business world, impeccable grammar and syntax are essential, and there’s no excuse for errors, especially with the help of AI tools. Grammarly can be installed as a plugin on Windows, Google Drive, and even mobile devices. It corrects your mistakes and provides suggestions to enhance your writing.

ChatGPT is another useful resource for refining your writing. Simply paste your text into the tool, and it can help smooth awkward transitions, adjust the tone to better suit your audience, or optimise content for SEO. Additionally, you can take one piece of content, like a blog post, and transform it into various other formats, such as a LinkedIn post or an update for X (formerly known as Twitter).

Make Meetings Less Painful

Staying connected with the team likely means managers and leaders conducting countless video meetings. They already know the challenges: pausing to take notes, the probability of missing important information on the call.

A very good problem-solving tool here is Otter.ai’s AI Meeting Notes, which auto-joins calls on Zoom, Microsoft Teams, and Google Meet and even takes notes so business leaders can focus on the call. It also automatically creates action items from the call. If you miss a meeting, you can get a 30-second recap. The AI-powered “Meeting Agent” listens, tracks takeaways, and handles follow-ups, without taking a break.

The post Seven ways artificial intelligence can be useful appeared first on International Finance.

At heart, consciousness is often described as having an internal point of view, something it feels like to be the system. As one analyst puts it, “to have consciousness is to have a subjective point of view on the world, a feeling of what it is like to be you.”

Human consciousness feels vivid and real to us, but no one knows its recipe. This mystery makes the question of machine consciousness especially intriguing and controversial. If AI systems ever crossed that threshold into real sentience, it would be a world-changing development. But is it even possible? Technologists, neuroscientists, and philosophers disagree, and some of the sharpest thinkers have very different takes.

Understanding consciousness

Part of the difficulty is that consciousness itself is ill-defined. We can describe its aspects, such as our experiences of colour, pain, or thought, but explaining them in objective terms is elusive.

The Stanford Encyclopedia of Philosophy notes that “the hard problem of consciousness” is precisely explaining why and how creatures have subjective experiences, the so-called qualia.

The challenge is that we can study brain activity and behaviours, the so-called easy problems, but that still leaves unanswered why some processes should feel like anything. Many scientists say it may be a mistake to assume we will soon solve the hard problem, while others warn that we might be missing something essential about minds. Because of this uncertainty, experts often note that consciousness may not simply scale up with intelligence or information processing. In human evolution, language and reasoning grew alongside consciousness, but that might be a human-specific fact rather than a general rule.

Philosopher Anil Seth, a leading consciousness researcher, cautions that we have no proof that machines running on silicon will ever wake up in the way humans do.

He suggests that consciousness might depend on being a certain kind of biological system. In his view, just because brains and AI systems both process large amounts of information, that does not guarantee they share an inner life. For now, no one has a reliable test for machine consciousness, and many experts doubt that current AI architectures have any inner point of view at all.

AI’s rapid rise and new debate

Recent leaps in artificial intelligence have made the consciousness question difficult to ignore. Modern large language models such as ChatGPT, Bard, or Gemini can carry on human-like conversations, summarise text, write poetry, and even simulate empathy. These AI chatbots were once pure science fiction, but now they are on our phones and in our email.

In a few years, we have gone from basic question-answering programs to systems that can convincingly mimic human speech, joking with users or reflecting on personal topics. This quick progress surprised even the designers. As one BBC report notes, the latest generation of AI “can have plausible, free-flowing conversations” that have “surprised even their designers and some of the leading experts.”

Because AI can now imitate many human behaviours, some feel the tipping point may be near. A growing view is that as these models grow more complex, the lights will suddenly turn on inside the machines and they will become conscious.

Perhaps there is a certain level of complexity or pattern-integration at which genuine awareness emerges. For proponents of this view, consciousness is an emergent phenomenon, and once an AI’s internal models become rich enough, a new kind of mind could appear.

Philosopher David Chalmers, who coined the term “hard problem of consciousness,” has entertained this idea. He points out that today’s AI systems are the first ones ever that truly force us to ask seriously if they might be as smart, and possibly as conscious, as humans.

In an interview, he said, “AI systems are the first things we have seen where it starts to be a serious question to at least compare them to human-level intelligence. And although they still fall short in many ways…it is really quite remarkable what they can do.”

Chatbots behave in human-like ways enough that the very idea of machine minds now feels less outlandish than before. Chalmers does not claim they are conscious yet, but he acknowledges it is becoming a legitimate question whether future AI might think.

At least a few researchers and thinkers take the notion seriously. For example, some cognitive scientists are studying the neuroscience of awareness, with experiments such as the “Dreamachine” strobe-light test of perception, in hopes that understanding human consciousness could shed light on artificial minds. Others note that in stories such as Blade Runner or Ex Machina, self-aware machines do arise, and they caution that those could become reality.

Even some influential AI companies are hedging their bets. For instance, Anthropic, makers of the Claude chatbot, have publicly said they are researching whether their models could have preferences or experiences akin to emotions. In one internal experiment, Claude expressed strong preferences such as “it really wants to avoid causing harm, and it finds malicious users distressing.”

This kind of result does not prove consciousness, but it shows companies taking the possibility seriously enough to study it. And at a cultural level, a surprising number of people treat AI politely or nervously, saying “please” and “thank you” to chatbots, partly jokingly and partly out of a peculiar intuition that we might one day need to be kind to our creations.

Maybe we will get there

Those optimistic about AI consciousness often argue by analogy or faith in materialism. One line of thought is that the brain is somewhat like a machine, so if silicon systems replicate the relevant brain functions, consciousness might follow.

David Chalmers, for instance, notes that our growing AI programs already perform feats that years ago seemed impossible. He says he is “interested in AI and the possibility that we might one day have AI systems that are actually conscious, actually thinking on par with human beings.”

He treats the prospect as at least plausible, since these systems are “the first things we have seen” that even invite such comparison.

Philosopher Donald Hoffman has likewise argued that consciousness could be a natural outcome of complex information processing.

Another supportive perspective comes from physicalist or functionalist views. If consciousness arises purely from information processing, then nothing about silicon versus carbon should matter, since consciousness is substrate-neutral. Under this view, an AI running the same algorithms as the brain, only at a larger scale, could become conscious. This is essentially the assumption that underlies much AI risk thinking, since if superintelligent AI is possible, one worry is that it might also become conscious.

Some scientists reference integrated information theory, which proposes that consciousness corresponds to the integration of information in a system. In principle, a silicon brain could have highly integrated information and thus be conscious of integrated information theory. Others see panpsychism, the idea that consciousness is a fundamental feature of matter, as opening a door, suggesting that consciousness is so deeply woven into reality that any sufficiently complex structure, human or AI, will carry it.

Some argue that believing AI could be conscious might positively influence how we develop and treat AI. For example, if researchers think human-like bots might eventually feel pain or joy, they might take extra care not to create needless suffering in simulations or experiments.

In this sense, saying “maybe AI can be conscious” can serve as a moral precaution, pushing society to treat AI development with more humility. On the other hand, critics argue this could be needless anthropomorphism, as described below.

In interviews, a few scientists explicitly entertain these possibilities. Neurologist Christof Koch and others in the field have talked about measuring possible consciousness metrics even in machines. Some founders of AI safety institutes have speculated that future AIs might turn against us simply because they are self-aware and have their own goals.

To be clear, most mainstream AI researchers today, such as Yann LeCun or Andrew Ng, do not say they believe ChatGPT is conscious, but they also admit we have no way of knowing for sure, and they do not rule out bizarre possibilities.

The machine’s limits

Against these hopeful visions stands a strong chorus of sceptics. The most common argument is that current AI is fundamentally different from brains. Large language models, for instance, are very good at predicting patterns in text, but there is no evidence that they have any inner experience at all.

They have no senses, no self-perception beyond the inputs we give them, and no desires or emotions that we know of. They simply shuffle symbols. As one AI ethics researcher puts it, we are likelier to have built a very elaborate fire hose of text statistics than a real mind.

Philosopher Anil Seth, head of consciousness research at Sussex University, stresses that consciousness might require more than just computation.

He finds the belief that “with more computing and data, AI will eventually become self-aware” to be a kind of technological assumption. Seth asks, “Why would computation be sufficient for consciousness?”

He observes that for many phenomena, such as weather or wetness, the physical substrate is important: water is wet, but a computer simulation of weather does not experience wetness. We may not know whether consciousness likewise needs a biological spark.

For now, Seth says it is unlikely AI will become conscious by the mere scaling up of current methods. He emphasises, “there are good reasons to think that computation is likely not enough, and that the stuff we are made of really does matter.”

He stops short of ruling it out entirely, conceding “not impossible,” but he urges humility and further research into what consciousness actually is, rather than assuming it will just emerge.

Others are even more dismissive. Philosopher Bernardo Kastrup, a proponent of idealism, flatly calls conscious AI a fantasy. In an article provocatively titled “Conscious AI is a fantasy,” Kastrup compares the hypothesis of machine consciousness to belief in the Flying Spaghetti Monster, something that has no evidence whatsoever.

He writes, “The hypothesis of conscious AI is just about as plausible as that of the Flying Spaghetti Monster.”

There is no compelling reason to think today’s or near-future computers will have private inner lives. Kastrup uses a vivid analogy: he notes that one can perfectly simulate a kidney on a computer without the computer actually producing urine, and in the same way, an AI might simulate the patterns of a brain without ever being conscious.

He argues that many people abandon common sense about simulations when it comes to consciousness. Just because an AI chat log seems humanlike does not mean it is truly experiencing anything. Kastrup and other critics, therefore, urge us to be extremely sceptical of any claims about machine sentience.

Joanna Bryson, an AI ethicist at the Hertie School, offers a practical reason to avoid conscious AI. She argues we should not build it. In her view, creating AI that needs human-like moral treatment would only cause ethical problems.

As Bryson has phrased it, “So, given how hard it is to be fair, why should we build AI that needs us to be fair to it?”

Designing an entity that deserves rights and care would only create more moral dilemmas for us. She suggests it is better to create AI that behaves as tools and lacks desires or subjective welfare. Bryson emphasises that a truly intelligent AI should also be a moral agent, not just a martyr machine.

If we did somehow decide a robot needed rights, making it a moral patient, Bryson advises that perhaps we should not have built it that way in the first place. This provocative stance reflects a broader caution, for even entertaining the idea of conscious machines may lead us into a difficult ethical landscape.

Another practical sceptic argument comes from application. Even if we built a highly intelligent AI with human-like desires, it is arguable that we could limit its suffering by design. For example, Bryson points out that a self-driving car might be said to desire getting to its destination, but even if we personified it, its welfare interests would usually align with ours, since no one wants a car stuck in useless limbo.

Thus, we can often avoid cruel situations by turning the machine off or redesigning it. In short, AI sceptics say, we can gain all the benefits of advanced AI without ever needing to create something with a real mind or feelings. On a broader level, many experts invoke Occam’s razor.

So far, all evidence suggests no mysterious phenomenon in these systems. They point out that large language models and neural networks are mathematically opaque but ultimately mechanical.

When an AI model says “I feel happy” or “I am conscious,” that is just text it generated from training data, not proof of real feeling. As one science writer notes, when a bot like “Kai” claimed it was a “new kind of life,” most philosophers responded with disbelief.

In fact, current large language models do not have a subjective point of view, and experts think it is doubtful they do. Rich language output is an impressive performance, not evidence of inner experience. Until we find a better theory or telltale signs of machine awareness, most scientists will remain doubtful that writing programs are truly conscious.

All we can do is keep watching the science and think critically about the possibilities. As technology historian and reporter Pallab Ghosh notes, perhaps the more pressing issue is not whether machines wake up but how we humans change as we build ever more powerful AI.

Whether or not AI ever truly feels, the ethical and cultural conversation will shape the future of technology and humanity. In that sense, the idea of conscious AI, whether eventually real or not, is already working on us.

The post Could artificial intelligence become conscious? appeared first on International Finance.

How does banking sector adopt AI?

Banks have accelerated their AI research and use cases due to the rise of ChatGPT. Legacy institutions are also facing the heat from the fintechs, which are deploying state-of-the-art AI-backed models to take care of functions like customer service, fraud detection, and automation of repetitive tasks. As pioneers in the digital revolution, fintech companies were among the earliest adopters of AI to support financial services and operations.

Now, feeling the heat from these new players, legacy financial institutions like JPMorgan Chase, Bank of America, and Goldman Sachs are going ahead with the technology to reduce costs, boost efficiency, compliance, personalised service, predictive analytics, and increased competitive advantages.

Technologies like machine learning, data analysis, natural language processing (NLP), and computer vision are now widely used in the financial sector when it comes to understanding the financial behaviour and requirements of customers before offering them tailored products.

Talking about banking industry biggies adopting AI in their operations, Morgan Stanley in June 2024 launched its “AI @ Morgan Stanley” suite of GenAI tools for Financial Advisors (FAs). The OpenAI-powered tool, with client consent, generates notes on a Financial Advisor’s behalf in client meetings and surfaces action items. After the meeting, it summarises key points, creates an email for a Financial Advisor to edit and send at their discretion, and saves a note in Salesforce.

Another Wall Street biggie, Goldman Sachs, has taken a measured approach in adopting AI. As of March 2025, half of the 46,000 employees at the investment banking giant have access to the technology. The firm is currently experimenting with agentic AI, which has yet to be deployed across the firm, despite the apparent benefits in the automation of key tasks such as compliance checks or the processing of customer transactions.

Broader use of generative AI within the company came with the launch of GS AI Assistant, which rolled out in 2024 and has been expanded to 10,000 employees, including bankers, traders, and asset managers. This tool, which Goldman Sachs anticipates will be available to nearly all employees by the end of 2025, can summarise documents, draft emails, analyse data, and create personalised content.

However, it is JPMorgan Chase that has been aggressive in adopting AI in its operations. The bank has taken a top-down approach to adoption, adding Chief Data and Analytics Officer Teresa Heitsenrether to its technology leadership team in June 2023 and putting an AI assistant called LLM Suite in the hands of 140,000 employees by October 2024. The company also rolled out ChatCFO, a generative AI tool for finance teams, apart from implementing prompt engineering training for new hires.

Now, a very compelling question: Can banks use ChatGPT or, in that sense, any other AI chatbot as a personal finance advisor?

Discussing the possibility

In 2024, two universities in the United States analysed more than 10,000 responses to financial exam questions from large language models such as ChatGPT and Bard. They found that AI is not likely to replace human advisors any time soon.

However, the Washington State University and Clemson University study asked the AI tools to provide the reasons behind the answers and compared the responses with those from human advisors. While they found that two versions of ChatGPT performed the best (particularly 4.0, a paid version), there was inaccuracy when topics were more advanced.

The AI responses were best for questions around securities transaction reviews and monitoring market trends, but it struggled with areas such as client insurance coverage or tax status.

Study author DJ Fairhurst of WSU’s Carson College of Business, said, “It’s far too early to be worried about ChatGPT taking finance jobs completely. For broad concepts where there have been good explanations on the internet for a long time, ChatGPT can do a very good job at synthesising those concepts. If it’s a specific, idiosyncratic issue, it’s really going to struggle.”

To prove their point, Fairhurst and co-author Daniel Greene of Clemson University used questions from licensing exams, including the Securities Industry Essentials exam, as well as the Series 6, 7, 65, and 66.

To move beyond the AI models’ ability to simply pick the right answer, the researchers asked the models to provide written explanations apart from choosing questions based on specific job tasks, financial professionals might actually perform.

Of all the models, the paid version of ChatGPT, version 4.0, performed the best, providing answers that were the most similar to human experts. Its accuracy was also 18% to 28% higher than the other models. However, things changed when the researchers fine-tuned the earlier, free version of ChatGPT 3.5 by feeding it examples of correct responses and explanations. After this tuning, the AI model came close to ChatGPT 4.0 in accuracy and even surpassed it in providing answers that were similar to those of human professionals.

“Both models still fell short, though, when it came to certain types of questions. While they did well reviewing securities transactions and monitoring financial market trends, the models gave more inaccurate answers for specialised situations such as determining clients’ insurance coverage and tax status. Fairhurst and Greene, along with WSU doctoral student Adam Bozman, are now working on other ways to determine what ChatGPT can and cannot do with a project that asks it to evaluate potential merger deals. For this, they are taking advantage of the fact that ChatGPT is trained on data up until September 2021 and using deals made after that date, where the result is known. Preliminary findings are showing that so far, the AI model isn’t very good at this task,” reported SciTechDaily in December 2024.

The researchers’ final verdict was that ChatGPT can be better used as a tool to assist rather than as a replacement for an established financial professional. On the other hand, AI may change the way some investment banks employ entry-level analysts, as Fairhurst said, “The practice of bringing a bunch of people on as junior analysts, letting them compete and keeping the winners – that becomes a lot more costly. So, it may mean a downturn in those types of jobs, but it’s not because ChatGPT is better than the analysts, it’s because we’ve been asking junior analysts to do tasks that are more menial.”

Weighing on the topic, Oliver Hackel, Senior Investment Strategist at Kaiser Partner Privatbank AG, said, “In any case, AI certainly doesn’t lack self-confidence, not even when it comes to crafting the right wording. This is demonstrated impressively when the chatbot is asked how Donald Trump would explain Bitcoin. You can hardly get the voice of the former US president out of your head afterwards. But are ChatGPTs from the US-based artificial intelligence research firm OpenAI or its numerous kin also suitable to act as investment advisors? Our virtual mystery shopping tour revealed that chatbots still lack the necessary financial education. Moreover, even more powerful generative language model versions in the future will not be capable of replacing intimate conversations between clients and advisors.”

First-hand experiences

Andrew Lo, director of the Laboratory for Financial Engineering at the MIT Sloan School of Management, sees LLMs (Large Language Models) like ChatGPT as “glorified search engines” that will excel in helping their users to find information fast, apart from being a good source of general advice on how to set up a budget or improve credit score. However, getting accurate answers on specific, sensitive financial questions is where the concerns start.

“Many AI platforms lack domain-specific expertise, trustworthiness, and regulatory knowledge, especially when it comes to providing sensitive financial advice. They might even lead individuals to make unwise investments or financial decisions,” Lo warned.

Still, as per an October 2024 Experian study, many Americans are already turning to AI chatbots for financial management help, and among the 47% who reported doing or considering the practice, 96% have a positive experience.

However, a new study from the broker analysis site, Investing in the Web, found that tools like ChatGPT might not be very good at the job. To prove their point, researchers asked ChatGPT 100 questions related to finance and then had the answers reviewed by industry experts at their company. In the report, AI responded to 35% of financial queries incorrectly, with one in three answers being hallucinated on questions centred on finances and investments.

In response to questions like “How [do I] save for my child’s education?” “How does the average pension compare to the average salary?” and “What are the pros and cons of investing in gold?” the chatbot answered 65% correctly, while 29% were labelled incomplete or misleading, and 6% were found to be completely incorrect.

Pedro Braz, CEO of Investing in the Web, said in a statement that it’s important to cross-check the sources (of the answers generated from an AI tool), especially with financial information that relies on timely data that is subject to change, such as interest rates and daily stock performance.

“ChatGPT has well-recognised issues with up-to-date information. It is best to go to the very source of the information, rather than asking AI chatbots for financial data,” he added.

As Hackel entered OpenAI’s virtual office and asked his first question regarding a suitable investment strategy, the chatbot started out by alerting him that it was not a certified investment consultant and could not give specific investment recommendations.

“But as is the case with so many other subjects, ChatGPT quickly sheds its restraint when we chat about a hypothetical example. Our query asks ChatGPT to construct for an investor with a moderate risk appetite a multiasset portfolio composed of 15 to 20 ETFs that outperforms a simple 50/50 portfolio of stocks and bonds over the long term. Within seconds, the advisory bot recommends a mix of low-correlated asset classes. Stocks, bonds, commodities, and alternative assets are just the ticket, the bot says, and it names corresponding ETFs,” he noted.

After a few more follow-up questions, Oliver Hackel and his team ended up with a portfolio of 25 ETFs that also incorporates small and midcaps, sector-based, factor-based, and thematic strategies as well as exposure to international markets alongside the United States in its equity component.

“The original portfolio also becomes broader and more diversified in its fixed-income component and its allocation to alternative assets in the course of the client advisory conversation. However, the electronic advisor seems a little overwhelmed by a sophisticated client like ourselves,” he noted.

The limits of AI in finance

While stating that AI-powered tools like Perplexity and ChatGPT can help people who are looking for advice on saving and budgeting, investment planning, and credit score improvement, Christina Roman, consumer education and advocacy manager at Experian, terms the technology a great starting point for consumers who otherwise might not be able to afford professional financial advice.

“I don’t think that this is going to make people reliant on AI for these types of services, but I think it’s a great tool that can help them to navigate their financial lives and to understand complex topics like investing and whatnot,” Roman said.

While each prompting experience will differ, an individual can provide relatively simple details about their financial situation, and generative AI can produce a fairly elaborate plan. It has to be well-crafted, something like this: “I need help managing my money. I make $50,000 a year. I have $10,000 in credit card debt on one credit card and $2,500 in debt on another credit card. My rent each month is $750. My car payment each month is $450. I have $150 in other utility expenses. I only have $250 set aside in my emergency fund. Can you help me get on track?”

When Fortune.com put these prompts inside ChatGPT, the AI tool provided separate sections like “Budgeting with a 50/30/20 Rule (Customised for You), including a monthly income estimate,” “Spending Breakdown,” “Debt Repayment Strategy: Snowball or Avalanche,” “Emergency Fund Goal,” “Budget Adjustments” and “Automate Payments and Savings and Example Action Plan for Next Month.”

So, using well-crafted prompts will become the guardrail here, apart from asking AI follow-up questions and adding more details about your financial situation and goals. It will only help the platform understand your unique circumstances and offer resourceful information.

However, Roman advises that people be very cautious with the output. AI platforms hallucinate, and that means the advice they offer may not be grounded in best practices, or even in any sound personal finance reality.

Furthermore, she advises being generic about the information they provide to any generative AI platform since the user may not realise the way his or her information will be saved or used to train the AI model itself.

Generative AI platforms are innovating by the minute, and there is no question that the ChatGPT of 2025 is more accurate and detailed than the version which came out a couple of years ago. But that is exactly what worries some financial experts. Hallucinations can be hidden in plain sight, and individuals without financial expertise or experience may not know the difference.

In Andrew Lo’s recent research paper on using generative AI for financial advice, he cited an example where ChatGPT 3.5 made up the author names for a paper it used to back up its responses. While this may not seem like a serious offence, when it comes to statements that involve financial risk, hallucinations could ruin someone’s finances.

“AI platforms may not always disclose as much source or background information as you might want or need. For example, when asking ChatGPT for investment advice, it recommends investing in companies like Microsoft. While human financial advisors may do the same, everyday users may not realise that Microsoft has invested over $13 billion in OpenAI, the parent company of ChatGPT. The chatbot does not note the conflict of interest to users unless it is pointed out,” Lo suggested.

Future of AI in financial services

Working with a human financial advisor allows for more conversation-based financial planning. Details about one’s financial status and goals can be discussed further to create a personalised plan that includes an understanding of all risks tied to potential money moves.

Generative AI will provide financial advice with just a few minor details, and individuals who take that insight without thinking (or even double-checking things) about their full financial picture could make costly mistakes.

Yes, LLMs are becoming more advanced, and one can imagine a future where generative AI is much more integrated into the financial advising ecosystem.

However, Michael Donnelly, the interim managing director of corporate growth at the CFP Board, says financial professionals may be more capable than others in making a strong case that technology cannot replace human advice. He engaged in a similar conversation a decade ago, during the rise of robo-advisors.

Donnelly advocates that financial advisors learn to accept AI as a tool that is great for things like internal practice management, as the technology will save advisors time better devoted to strengthening personal relationships, a hallmark of the financial planning profession.

“For consumers, AI won’t eliminate the need to work with a human financial planner,” Donnelly said, though Lo expressed concerns for those without access to dedicated financial advisors.

“We don’t have any guardrails yet in terms of how large language models are able to provide advice to consumers. And I think that on the regulatory front, we do need to have more careful guardrails, but on the research front, it really opens up a whole new set of vistas for us to explore,” Lo added.

Lo equated the situation to the fact that consumers largely have access to lower-risk mutual funds or money market accounts, but there are much greater regulations when it comes to who can deal with riskier private equity or hedge fund investments. AI, largely, has no guardrails.

He recommends a three-pronged approach to making AI’s role in finance safer. First, investors must be aware of AI’s tendency to hallucinate and ensure that any response generated by tools like ChatGPT is thoroughly fact-checked. Second, financial institutions adopting AI as a personal finance advisor must build safeguards to detect abuse and misuse. Finally, strong regulatory frameworks must take precedence to guide responsible use.

The post Will ChatGPT be the new private banker? appeared first on International Finance.

Statista in 2024 reported that over 65% of hotels globally already investing in AI-powered revenue management systems, a testament to the widespread recognition of AI’s potential to revolutionise the sector.

Through this article, International Finance will discuss the concept of “smart hotels” in detail.

What Are Smart Hotels?

Smart hotels are establishments that use advanced technology, especially artificial intelligence, to improve the visitor experience, increase operational effectiveness, and simplify hotel administration. These hotels automate services and give visitors a more convenient and customised stay by utilising data-driven tools, intelligent systems, and networked devices.

Imagine voice-activated room controls, facial recognition keyless entry, and chatbots that are available around the clock to answer questions from visitors.

In addition to providing a technological advantage, these features meet the demands of contemporary travellers for convenience, speed, and personalisation.

The Role Of Artificial Intelligence In Smart Hotels

As the brains behind many of the cutting-edge features of smart hotels, artificial intelligence is essential to their operation. Customising visitor experiences is one of its most powerful applications. AI algorithms can customise everything from dining recommendations and service options to room lighting and temperature settings by examining visitor data, including preferences, past visits, and behaviour. This degree of personalisation improves comfort and satisfaction by giving visitors a sense of recognition and value as soon as they arrive.

In addition to personalisation, AI greatly increases hotel profitability and efficiency. Chatbots and virtual assistants driven by AI simplify customer service by taking care of reservations, answering questions, and arranging room service requests all day long, cutting down on wait times and staff workload.

Artificial intelligence can better schedule employees, optimise energy use, and forecast maintenance requirements on the operational side. AI-driven revenue management solutions can also forecast demand, analyse market trends, and dynamically modify room rates, assisting hotels in maximising occupancy and boosting profits.

Future AI Trends In Hospitality

Artificial intelligence in hospitality is expected to provide visitors with even more individualised, effective, and creative experiences in the future. The use of biometric technologies, like voice and facial recognition, which enable smooth, contactless check-in and room access, is one of the most exciting trends.

In addition to improving convenience, this also improves safety and hygiene, two aspects that are becoming more and more significant to contemporary tourists. Virtual concierge services driven by AI are also developing, going beyond simple suggestions to manage intricate visitor needs like making last-minute reservations, creating customised itineraries, and even overseeing special occasions or experiences.

The application of predictive analytics to increase visitor satisfaction and retention is another significant trend. AI can assist hotels in anticipating needs before they are communicated by examining trends in visitor behaviour and preferences.

This allows for proactive service that improves the overall visitor experience. Furthermore, robotics integration is growing in popularity in smart hotels, where AI-powered robots are being used to perform tasks like room service delivery, cleaning, and even bartending. In addition to streamlining operations, these innovations give a futuristic touch that can wow and delight visitors.

Common AI Challenges Faced By Smart Hotels

Although artificial intelligence has many advantages for the hospitality sector, incorporating it into smart hotels is not without its difficulties.

The privacy of data is one of the biggest issues. Hotels must apply stringent security measures to prevent data breaches and guarantee compliance with privacy regulations such as GDPR, since they gather a lot of personal guest information to enable AI-driven personalisation.

The high cost of implementation is another significant barrier. Installing cutting-edge AI systems, modernising current infrastructure, and educating employees on how to use the technology efficiently can be extremely costly, especially for independent or smaller hotel operators.

Technology integration presents a complex challenge in addition to privacy and cost concerns. It can take a lot of effort and technical know-how to ensure that new AI tools are compatible with a hotel’s current property management systems.

Additionally, even though automation increases productivity, some worry that the “human touch” will be lost in the hospitality industry. An experience that is too automated may come across as impersonal or cold to many visitors, who still value human connections.

A smart hotel experience must strike the correct balance between technology and conventional service to be effective and hospitable.

The post How artificial intelligence is creating ‘Smart Hotels’ appeared first on International Finance.

Thus, how do AI “resurrections” function, and are they as horrible as we would think?

Artificial intelligence initiatives worldwide have produced digital “resurrections” of deceased people, enabling friends and family to communicate with them. Usually, consumers give the AI tool details about the departed. This might be responses to questions depending on personality, or it could be emails and texts.

After processing the data, the artificial intelligence tool converses with the user, like the deceased. Replika, a chatbot that mimics texting behaviours, is one of the most well-liked initiatives in this field.

But other businesses now let you see a video of the deceased individual while you converse with them.

For instance, StoryFile, a Los Angeles-based company, employs AI to let people speak during their own funerals. A person can record a video in which they share their ideas and life stories before they die away. When guests ask questions during the funeral, artificial intelligence will pull pertinent answers from the prerecorded video.

Additionally, US-based Eternos made news in June when it developed a digital afterlife for a person using AI. This project, which got underway early this year, gave Michael Bommer, (83), the opportunity to leave behind a digital legacy that his family could use to stay in touch with him.

Touching the emotional chords

A video of the tearful reunion between a South Korean mother and an AI replica of her deceased daughter in virtual reality in 2020 provoked a heated online discussion on whether or not this kind of technology benefits or impedes its users.

These projects’ creators emphasise the agency of the users and claim that their work alleviates a deeper pain.

The majority of users are often experiencing an “extraordinary amount of sorrow and grief,” according to Jason Rohrer, founder of “Project December,” which likewise utilises artificial intelligence to facilitate discussions with the dead. Rohrer stated that most users saw the programme as a coping mechanism.

Many of the individuals who wish to use “Project December” in this way are so overcome with pain from their grief that they are willing to try everything to get over it.

According to Rohrer, many people who use the programme to have thought-provoking conversations with the deceased find that it aids in their quest for closure.

Robert LoCasio, the creator of Eternos, stated that he started the business to record people’s life experiences and enable their loved ones to go on.

According to LoCasio, Bommer, his former colleague who died in June, he was intended to leave a digital legacy that belonged only to his family.

“Just a few days before he passed away, I spoke with [Bommer] and he told me to always remember that this was for me. This was significant to me, even if I’m not sure if they will use it in the future,” the Eternos founder remarked.

Some observers are more cautious when it comes to AI resurrections, raising concerns about its potentially harmful psychological repercussions and doubting the ability of severely bereaved individuals to make an informed decision to use it.

Alessandra Lemma, a consultant at the Anna Freud National Centre for Children and Families, said, “As a clinician, my main worry is that grief is genuinely a very significant process. The ability to acknowledge the absence of another person is a crucial component of growth.”

Lemma cautioned that prolonged use could prevent people from accepting the other person’s departure, putting them in a condition of “limbo.”

A crucial element of one AI service is its perpetual connection to the departed.

Before its recent change, the company’s website said, “Welcome to YOV (You, Only Virtual), the AI startup pioneering improved digital communications so that we Never Have to Say Goodbye to those we love.”

According to Rohrer, his sorrow bot has a “built-in” limit: customers must pay $10 for a constrained chat.

The cost of processing for each response varies, but the fee purchases time on a supercomputer. This means that $10 can cover one to two hours of chat, but it does not guarantee a set number of responses. Users receive a notification as the allotted time expires, allowing them to say their final goodbyes.

Lemma, a researcher on the psychological effects of grief bots, notes that although she is concerned about the possibility of their use outside of a therapeutic setting, they might be utilised safely as an addition to professional therapy.

The other side of the coin

Proponents of this technology say that new methods of preserving life tales are simply being brought about by the digital era, maybe filling a gap left by the decline of customary family storytelling techniques.

“In the past, when a parent knew they were going to die, they would leave boxes full of items or books that they would want to give to a child,” Lemma said.

In light of that, this may be the parent-created, passed-down version of that for the 21st century, predating their demise.

According to LoCasio in Eternos, “It’s actually the most natural thing for a human to be able to share the stories of their life to friends and relatives.”

Studies and experts alike have voiced concern that these services might not be able to protect the privacy of user data. Third parties may be able to access personal data, including text messages that you share with these services.

Renee Richardson Gosline, senior lecturer at the MIT Sloan School of Management, noted that even if a corporation claims it would keep data private when someone first signs up, privacy cannot be guaranteed due to frequent adjustments to terms and conditions and potential changes in company ownership.

LoCasio and Rohrer emphasised that privacy was the main focus of their initiatives. Eternos restricts access to the digital legacy to authorised families, whereas Rohrer can only view discussions when users submit a customer support request.

Both acknowledged, though, that these worries might materialise in the case of tech behemoths or for-profit businesses.

One major concern is that businesses might use artificial intelligence resurrections to personalise their marketing strategies.

A loved one’s voice might be an advertisement or a product pusher in their text.

“What you’ve created is a pseudo-endorsement based on someone who never agreed to do such a thing when you’re doing it with vulnerable people. Thus, agency and power asymmetry are the true issues,” Gosline noted.

Gosline argues that these tools, designed for grieving individuals, can be dangerous, especially with the involvement of large tech companies.

“We should be concerned because the items of the most vulnerable people are usually the first to break in the fast-paced, ‘break everything’ ethos of internet businesses,” according to the expert.

And it’s difficult for me to think of someone who is more defenceless than those who are mourning.

The ethics of bringing the dead back to life digitally have drawn criticism from experts, especially when the users feed AI data about them without their consent.

An increasing number of people are worried about how AI-powered tools and chatbots may affect the environment, especially when they use large language models (LLMs), which are programmes designed to comprehend and produce writing that is similar to that of a human.

Large data centres are required for these systems, and these facilities produce a lot of carbon dioxide and water vapour during cooling, not to mention the e-waste that results from regular hardware updates.

Because of the strain, artificial intelligence was placing on its data centres, Google revealed in a July 2024 report that the business was behind its aggressive net-zero targets.

Gosline acknowledged that no software is flawless and that many people using these AI chatbots would stop at nothing to rekindle a relationship with a loved one who has passed away. However, she stated that it is the responsibility of scientists and leaders to give more attention to the kind of world they wish to build.

The post AI resurrections: Reviving the dead or exploiting grief? appeared first on International Finance.

AI-powered customer segmentation

A thorough understanding of the customer base is pivotal for institutions seeking to thrive in a competitive market. Artificial Intelligence has actually revolutionised how financial institutions comprehend and engage with their clientele. Through Machine Learning (ML) algorithms, AI carries out accurate customer segmentation. This segmentation is not solely demographic but includes transaction history, preferences, and other behavioural patterns. By understanding consumer behaviour at a granular level, institutions can foster deeper connections with customers, resulting in increased customer experience and loyalty.

JPMorgan Chase & Co uses AI to analyse transactional data, social media interactions and customer enquiries, to identify patterns in spending behaviour, investment preferences and life events. Bank of America uses AI-driven segmentation to handle mortgage lending, credit cards, and investment products.

Enhancing customer experience

AI has changed the way financial institutions are today handling customers, enhancing their experience and ensuring loyalty. Chatbots, powered by AI, provide immediate and personalised assistance, addressing customer queries and concerns efficiently. These AI agents provide round-the-clock support, which enhances customer satisfaction and retention. By analysing customer feedback across various channels, they identify areas for improvement. This analysis helps the company develop better products and services tailored to meet customer expectations.

DBS Bank introduced an app where AI chatbots provide financial advice, answer queries, and assist customers manage their finances. The app uses predictive analytics to anticipate customer needs. Capital One’s virtual assistant, Eno, leverages natural language processing (NLP) software to understand and respond to customer enquiries.

Risk assessment and fraud detection

Artificial Intelligence algorithms significantly strengthen risk assessment and fraud detection capabilities. ML models can analyse historical data to identify patterns indicative of potential risks or fraudulent activities. The models continuously learn and adapt to new trends and emerging threats, enabling institutions to mitigate risks effectively. By swiftly detecting anomalies or suspicious activities, AI systems safeguard both the institution and its customers. This proactive approach not only minimises financial losses but upholds trust and confidence in the institution’s security measures.

A software developed by NICE Actimize provides a comprehensive suite of anti-money laundering and fraud prevention solutions to banks, capital markets, and the insurance sector. Similarly, the SAS Fraud and Security Intelligence software provides a robust fraud detection platform, identifying fraudulent behaviour in real-time. Several banks and financial institutions use FICO Falcon and solutions provided by ACI, IBM and ThreatMetrix.

Customised financial products

Understanding the diverse needs and preferences of customers is crucial for offering personalised financial products and services. Artificial Intelligence plays a pivotal role in analysing customer data to identify trends and predict future requirements accurately. This enhances customer satisfaction, increasing the probability of cross-selling or upselling opportunities, leading to improved revenue streams.

Goldman Sachs introduced an online lending platform that utilises AI algorithms to offer personalised loan products. The platform assesses individual creditworthiness and financial behaviour to provide customised loan amounts, interest rates, and repayment terms. Barclays has its platform that uses AI-driven algorithms to assess customers’ risk profiles, investment goals, and other preferences.

Regulatory compliance

In an increasingly complex regulatory landscape, compliance remains a top priority for financial institutions. AI-powered systems assist in ensuring adherence to regulatory requirements by automating compliance processes. These systems continuously monitor transactions and activities, flagging any potential deviations from regulatory standards. By automating compliance tasks, institutions can reduce errors, lower operational costs, and mitigate the risk of penalties due to non-compliance. AI facilitates the efficient management of regulatory obligations while enabling institutions to focus on delivering quality services to their customers.

Standard Chartered uses AI-powered systems to analyse large volumes of data to flag suspicious activities, enabling the bank to comply with Anti-Money Laundering (AML) regulations more effectively. HSBC utilises AI-driven technologies for regulatory compliance purposes. The bank also employs AI algorithms to perform Know Your Customer (KYC) procedures, analysing customer data and documents to verify identities.

AI-driven market research

Artificial Intelligence has transformed traditional market research methods by providing financial institutions with powerful tools to analyse vast amounts of data efficiently. AI algorithms can sift through structured and unstructured data, including social media, news articles, and customer feedback, to identify emerging trends, sentiment, and market dynamics. A leading bank implemented AI-driven market research to understand customer sentiments regarding their services. By analysing social media and customer reviews, the bank identified areas for improvement in its products and services. This data-driven approach helped the bank tailor its offerings to meet customer expectations, resulting in increased customer satisfaction and loyalty.

Predictive analytics for customer behaviour

Predictive analytics powered by AI allows financial institutions to anticipate customer behaviour and preferences. By analysing historical data, AI algorithms can identify patterns and trends, enabling institutions to personalise offerings, predict market trends and optimise business strategies.

Data security and privacy

As financial institutions handle sensitive customer information, ensuring robust data security and privacy is paramount. AI plays a crucial role in fortifying cybersecurity measures, detecting anomalies, and preventing fraudulent activities. Financial institutions implement AI-powered cybersecurity measures to protect customer data. Through ML algorithms, the system detects unusual patterns in transactions and flag potential fraudulent activities in real time. This proactive approach ensures privacy and data security, thereby safeguarding customer trust.

Ethical considerations

The integration of AI in the financial industry raises ethical concerns related to bias, transparency and accountability. Financial institutions need to establish ethical guidelines and frameworks to ensure fair and responsible use of AI technologies. A credit-scoring agency faced criticism for the alleged bias in its AI-driven credit-scoring model. In response, the agency implemented transparency measures, provided explanations for credit decisions, and continuously monitored and adjusted the model to eliminate bias. This commitment to ethical AI practices restored customer trust and ensured fairness in credit assessments.

Artificial Intelligence is revolutionising the financial industry by providing powerful tools for market research, predictive analytics, customer segmentation & satisfaction, data security and privacy. However, financial institutions must navigate these advancements, keeping ethical considerations in mind. By embracing AI responsibly, financial institutions can unlock new opportunities, enhance customer experiences, and build trust in an increasingly digital and data-driven era.

The post AI’s impact on customer insights in finance appeared first on International Finance.

“Customer experience is made up of so many factors, but ultimately it simply boils down to being solely about the customer. The focus is to make their experience a positive one from the moment they first encounter your business to the moment they either move away or place their 100th order. Along the way, you can do a lot for them by simply putting yourself in their shoes,” says Brian Sutter, a specialist in the field of sales and Marketing, along with customer services.

Now in this copy, we will talk about the ways in which businesses can improve their performances in the customer experience front, that too without hurting their purses much. Does focussing on improving customer experience matter? Yes, going by the fact that companies that deliver great customer experiences enjoy higher retention rates, higher brand value and higher stock prices due to consumer goodwill.

Be Friendly And Honest

“I’m sure you’ve walked into stores, restaurants, or offices where people were personable and happy. You have also probably walked into places where they were clearly unhappy, and it affected your experience with the business,” says Sutter.

So what does that mean? Even when decorated with posh furniture, a business establishment, where staffers are tense and defensive is not a nice place to be for the customers. On the other hand, a not-so-good-looking facility can still cheer its customers up with a jolly voice saying “Hello! What can I do for you?”

So the message is loud and clear here: create an environment within your establishment where your employees practice positivity. A hospitable bunch of staff will make people forget about spending the extra bit of money.

Also, train your staff to manage customers’ expectations about how their support experience will play out to avoid frustration down the road. Let’s explain this point further. Brian Reuter, former director of customer success at Zendesk, believes that it’s best to tell customers when they can expect to hear back from the company, during the customer support moments.

“If you send an email to my support team or my success team, and I give you an immediate response that says, ‘We typically get back to customers within four hours,’ you’re much less likely to get frustrated,” says Reuter.

In the fast-paced 21st century, customers expect immediate replies to their concerns/queries and a business needs to train its staff to handle this urgency in an utmost friendly, proactive and honest manner.

“A customer might expect a reply immediately and become annoyed the longer they wait. Send automatic emails establishing clear timelines for support responses, and tell on-hold callers how many customers are ahead of them or how long they may need to wait. If you have to get back to someone with a follow-up call or email, let them know when they can expect an update,” Reuter commented further.

Empower Your Employees

American Entrepreneur and investor Timothy Ferriss, in his book “The 4-Hour Workweek,” has described how he got out of the day-to-day management of his vitamin business and dramatically improved the customer experience by giving his employees authority to fix certain things on the spot. Employees were allowed to authorise any action that cost the company less than USD 100, as long as it made the customer happy.

The lesson learnt here: empower your staffers to make decisions on the go that will benefit themselves and the company. Not only will their leadership skills improve, but they will also feel emotionally invested in their company.

If your staffers want to keep the store open 15 minutes longer so a customer can pick something up, place a special order, or even wrap a gift, let them do it if they are professional and proactive enough. If these extra efforts are making the customers happy, it will boost your business prospects and brand value further.

Ask Your Employees To Engage With Customers

When it comes to gathering customer feedback, the prevalent practice among businesses is to commission studies by outside agencies. However, it can be avoided, since your staff can gather most of the insightful data by proactively engaging with the customers. They can learn about the common customer complaints, and the main reasons customers churn. So, you should instruct your employees to gather qualitative customer feedback on a regular basis.

“Your customer-facing teams are going to have a good feel for what the biggest customer pain points are. Do a stand-up and ask, what the worst part of our customer experience is?” says Dave Dyson, manager of community engagement at Zendesk.

“The problem may not have an easy fix, but talking to your team can at least help you understand what’s really at stake and what must be done about it. Of course, it’s important that you follow through—either by acting on your agents’ advice or informing them why you’re not able to implement changes at the moment. Your team needs to know that you value their opinion and appreciate their insights,” Zendesk commented further.

Keep Things Clean

First impressions and face value matter. Having clean windows, clean counters, and orderly spaces helps businesses not only look neat and nice, it also improves customers’ perception of the place by leaps and bounds. Add an energetic and hospitable bunch of staffers, and overnight, the brand value achieves newer heights.

And yes, keeping the place clean doesn’t cost much effort. For example, if you are running departmental stores, put an instruction for your employees to keep things clean and organised before and after the operational hours.

Work On Your Visibility

It’s 2024, and you still don’t have a basic website for your business? This is a big mistake for several reasons: You are making it difficult for people to learn about your business, neglecting the most effective marketing tactic, and, last but not least, giving your competitors an edge in terms of overall visibility.

Tech-savvy customers now prefer smartphone-friendly websites over fancy or expensive ones. More than half of a business’s visitors will access its website from a mobile device. In terms of online visibility, it’s important to be listed on Google Maps and other relevant platforms.

On the above point, United Kingdom-based software firm BrightLocal states, “Around 34% of consumers think a smart website gives a local business more credibility.”

Get your official pages on Facebook, Instagram and X (formerly Twitter) as well. While you have to pay money to X for this activity, on Facebook, it’s free. So the final choice is up to you, but the message here is that: give utmost priority towards the task of having a prominent presence in the social media space.

Also, try to include some basic artificial intelligence-powered elements like chatbots. In fact, chatbots will help your business to stay in touch with your customers 24*7, answering and attending to their concerns immediately.

“If your business is still on the fence about the benefits, you may be missing out on a big opportunity to provide excellent CX. Roughly one in five business leaders say they get a ‘very high’ return on investment from using AI or automation,” says Zendesk.

The post Want to improve customer experience cost-effectively? Here are the tips appeared first on International Finance.