As per the study titled “AI in Asset Management Market Report 2026,” published on Research and Markets, the ongoing surge stems from tailwinds like advancements in digital asset management, the increasing complexity of investment portfolios, the proliferation of financial data, and the heightened demand for error reductions in asset tracking and analytics-driven decision-making.

“The forecast for AI in the asset management market extends this trajectory, predicting a leap to USD 21.82 billion by 2030 with a CAGR of 32.4%. Key factors fueling this growth include the rising adoption of AI-driven financial platforms, an escalating demand for real-time asset visibility, and the expansion of algorithmic investment strategies. The integration of AI with blockchain and the development of cloud-based asset management solutions contribute to this anticipated expansion. Future trends predict advancements in automated asset tracking, AI-driven portfolio optimization, and real-time investment decision support,” the report said.

The immediate need for sophisticated fraud detection solutions has become the critical element to the AI market’s growth. Integrating AI into asset management solutions enhances operational efficiency, accuracy, and real-time monitoring capabilities, especially in fraud detection. To prove its point, the study cited the 2024 incident, in which the United States Department of the Treasury reported prevention and recovery of over USD 4 billion in fraud by leveraging machine-learning AI, showcasing the significant impact of AI-powered fraud detection.

“Leading companies are enhancing their market positions through innovations such as AI supercomputing services. In March 2023, NVIDIA Corporation launched DGX Cloud, offering a high-performance AI-training-as-a-service platform. This service provides enterprises with a serverless environment optimized for AI, signifying a leap forward in AI application accessibility,” the report said.

DGX Cloud now allows organizations to instantly access NVIDIA AI supercomputing across global cloud platforms using a standard web browser. The AI-training-as-a-service platform is offering enterprise developers a serverless environment tailored for doing R&D activities related to generative AI. DGX Cloud, equipped with eight NVIDIA 80GB Tensor Core GPUs, enables organizations to operate their own AI supercomputer through a web interface while delivering 640GB of GPU memory per instance.

“In corporate strategy developments, Alarm.com acquired Vintra in April 2023, marking an expansion of its deep-learning capabilities and fortifying its position in advanced video analytics for asset management. Such acquisitions indicate a trend of strategic enhancements among major players in the market,” it added further.

Prominent companies dominating the AI in asset management market include Alphabet, Microsoft, JPMorgan Chase, and Amazon Web Services (AWS), among others. Going by the regional analysis, in 2025, North America emerged as the leading market, with significant activity recorded in Asia-Pacific, Europe, and South America as well.

“Tariffs have had a dual impact, increasing costs for imported data center hardware and promoting a shift to cloud-based platforms. This transition supports software-centric models, boosting regional fintech ecosystems,” the report observed.

“These tariffs have influenced the AI in the asset management market by increasing costs related to imported data center hardware, analytics servers, and supporting IT infrastructure. These impacts are more visible in on-premise deployments across North America, Europe, and Asia Pacific. Higher infrastructure costs have encouraged firms to reassess capital investments. At the same time, tariffs are accelerating migration toward cloud-based AI asset management platforms. This transition is supporting software-centric delivery models and strengthening regional fintech ecosystems,” it added further.

Cutting-edge technologies like machine learning, deep learning, and predictive analytics have emerged as central elements to AI applications in asset management, being employed across sectors like BFSI, healthcare, retail, and more.

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How did it come to this – especially since less than two decades back, most popular references of artificial intelligence were in sci-fi movies? Will AI revolutionise the world of financial services? And more specifically, what does it bring to the party with regards to fraud detection? Let’s separate fact from fiction and explore what lies beyond the inflated expectations.

Why now?

Many practical ideas involving AI have been developed since the late 90s and early 00s but we’re only now seeing a surge in implementation of AI-driven use-cases. There are two main drivers behind this: new data assets and increased computational power. As the industry embraced big data, the breadth and depth of data within financial institutions has grown exponentially, powered by low-cost and distributed systems such as Hadoop. Computing power is also heavily commoditised, evidenced by modern smartphones now as powerful as many legacy business servers. The time for AI has started, but it will certainly require a journey for organisations to reach operational maturity rather than being a binary switch.

Don’t run before you can walk

The Gartner Hype Cycle for Emerging Technologies infers that there is a disconnect between the reality today and the vision for AI, an observation shared by many industry analysts. The research suggests that machine learning and deep learning could take between two-to-five years to meet market expectations, while artificial general intelligence (commonly referred to as strong AI, i.e. automation that could successfully perform any intellectual task in the same capacity as a human) could take up to 10 years for mainstream adoption.

Other publications predict that the pace could be much faster. The IDC FutureScape report suggests that “cognitive computing, artificial intelligence and machine learning will become the fastest growing segments of software development by the end of 2018; by 2021, 90% of organizations will be incorporating cognitive/AI and machine learning into new enterprise apps.” 

AI adoption may still be in its infancy, but new implementations have gained significant momentum and early results show huge promise. For most financial organisations faced with rising fraud losses and the prohibitive costs linked to investigations, AI is increasingly positioned as a key technology to help automate instant fraud decisions, maximise the detection performance as well as streamlining alert volumes in the near future.

Data is the rocket fuel

Whilst AI certainly has the potential to add significant value in the detection of fraud, deploying a successful model is no simple feat. For every successful AI model, there are many more failed attempts than many would care to admit, and the root cause is often data. Data is the fuel for an operational risk engine: Poor input will lead to sub-optimal results, no matter how good the detection algorithms are. This means more noise in the fraud alerts with false positives as well as undetected cases.

On top of generic data concerns, there are additional, often overlooked factors which directly impact the effectiveness of data used for fraud management:

• Geographical variances in data.
• Varying risk appetites across products and channels.
• Accuracy of fraud classification (i.e. which proportion of the alerts marked as fraud are effectively confirmed ones).
• Relatively rare occurance of fraud compared to the huge bulk of transactions; having a suitable sample to train a model isn’t always guaranteed.

Ensuring that data meets minimum benchmarks is therefore critical, especially with ongoing digitalisation programmes which will subject banks to an avalanche of new data assets. These can certainly help augment fraud detection capabilities but need to be balanced with increased data protection and privacy regulations.

A hybrid ecosystem for fraud detection

Techniques available under the banner of artificial intelligence such as machine learning, deep learning, etc. are powerful assets but all seasoned counter-fraud professionals know the adage: Don’t put all your eggs in one basket.

Relying solely on predictive analytics to guard against fraud would be a naïve decision. In the context of the PSD2 (payment services directive) regulation in EU member states, a new payment channel is being introduced along with new payments actors and services, which will in turn drive new customer behaviour. Without historical data, predictive techniques such as AI will be starved of a valid training sample and therefore be rendered ineffective in the short term. Instead, the new risk factors can be mitigated through business scenarios and anomaly detection using peer group analysis, as part of a hybrid detection approach.

Yet another challenge is the ability to digest the output of some AI models into meaningful outcomes. Techniques such as neural networks or deep learning offer great accuracy and statistical fit but can also be opaque, delivering limited insight for interpretability and tuning. A “computer says no” response with no alternative workflows or complementary investigation tools creates friction in the transactional journey in cases of false positives, and may lead to customer attrition and reputational damage – a costly outcome in a digital era where customers can easily switch banks from the comfort of their homes.

Holistic view

For effective detection and deterrence, fraud strategists must gain a holistic view over their threat landscape. To achieve this, financial organisations should adopt multi-layered defences – but to ensure success, they need to aim for balance in their strategy. Balance between robust counter-fraud measures and positive customer experience. Balance between rigid internal controls and customer-centricity. And balance between curbing fraud losses and meeting revenue targets. Analytics is the fulcrum that can provide this necessary balance.

AI is a huge cog in the fraud operations machinery but one must not lose sight of the bigger picture. Real value lies in translating ‘artificial intelligence’ into ‘actionable intelligence’. In doing so, remember that your organisation does not need an AI strategy; instead let AI help drive your business strategy.

About Sundeep Tengur:

Sundeep Tengur is a banking fraud and financial crime specialist within the Global Fraud and Security Intelligence Practice at SAS. He provides guidance on industry best practice, educating prospective clients on various fraud modus operandi and designing end to end solutions to mitigate fraud risks. He is a Certified Financial Crime Specialist (CFCS), with many years of experience in mitigating fraud in retail banking and financial services.

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