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Blending AI & rules-based approach in finance

IFM_ AI & rules-based approach in finance
Most financial institutions want to ensure they are knowledgeable about artificial intelligence and do not fall behind because doing so can put them at a competitive disadvantage

The rise of artificial intelligence (AI), revolutionising processes across the banking sector, is one of the most exciting technological innovations in the previous decade. Leaders in the industry are eager to use artificial intelligence’s potential for understandable reasons. For instance, technology offers a lot of possibilities to automate manual tasks and increase productivity. Additionally, you can use artificial intelligence in Regtech systems to track transactions for outliers, enhancing businesses’ anti-money laundering policies and thwarting potential fraud.

Most financial institutions want to ensure they are knowledgeable about artificial intelligence and do not fall behind because doing so can put them at a competitive disadvantage. A hasty adoption of technologies has resulted, with at least 85% of financial institutions utilising artificial intelligence in some capacity. In addition, the employment of artificial intelligence alone has resulted in a shift in data handling away from conventional “rules-based” systems, which are programmed to follow specific criteria while processing data.

There is no indication that this trend will stop growing. According to surveys, banks and insurance businesses anticipate an 86% increase in AI-related projects’ expenditures by 2025. Although investing in cutting-edge technology can have a big payoff, there’s a chance that this rush to adopt artificial intelligence is leaving some critical gaps.

Potential dangers

Many firms may overlook the reality that a rules-based system still has considerable advantages because so much of the current industry hoopla is about artificial intelligence. After all, rules-based technology continues to be a cutting-edge area of data science where significant research and development are being done.

The fact that rules-based systems are far simpler to comprehend and defend to a regulator may be the most crucial factor. For example, if an organisation is requested to justify a particular choice by their systems, they can quickly show the standards used. However, the identical assignment would be significantly more challenging to complete using an AI system because their decisions are based only on their own opaque and predefined criteria. As a result, it can be difficult to justify these choices, which can make businesses difficult with authorities if the system fails or is thought unjust.

Artificial intelligence has come under fire for making prejudice against minority groups applying for loans worse. Google investigated if artificial intelligence could aid businesses in selecting borrowers, but the research was shelved because it was thought too risky from an ethical standpoint. Financial authorities in the United Kingdom have advised banks that they can only use the technology if they can put the required safeguards in place, ensuring that such prejudices are not perpetuated. This is because they are aware of the possible issues with artificial intelligence. They must also be able to describe the decision-making processes used by their technology.

Regulators are increasingly requesting more information regarding artificial intelligence, partly due to these experiences. How does it function? How frequently do you examine it? How can you be sure the findings it produces are accurate? How well does its efficacy compared to your risk profile? Such problems can be solved quickly if there are clear rules about how software can analyse data and serve as a regulatory guide. But if businesses adopting artificial intelligence want to go back and put the initial decisions into proper perspective, they will have to do a lot of tedious work. For this reason, rules are preferable to artificial intelligence in cases with the same outcome.

A combined strategy

Of course, this does not imply that financial organisations do not use artificial intelligence in various ways. Instead, it only makes the case that a combined strategy that combines artificial intelligence and rules may be more effective. For instance, very high levels of risk detection can be ensured via a solid and transparent rules-based approach, with the power of artificial intelligence being utilised in post-processes.

AI can bring significant value by automating these post-processes. Numerous manual operations that can be time-consuming and expensive can be automated by artificial intelligence, making the overall company process more efficient and less costly. The deployment of artificial intelligence has reportedly decreased operating costs for more than a third (37%) of financial services companies, and 34% more believe that artificial intelligence will eventually lower their cost base.

Ingenious use cases

The compliance gap—the period between when risk is discovered and verified—is decreased in risk management due to improved speed and automation. Combining the revolutionary power of artificial intelligence with a rules-based approach to risk detection that is completely visible, understandable, and accessible can enable compliance and risk management techniques that are more effective and sophisticated than before.

Aside from compliance and risk management, it is also true that artificial intelligence offers a wide range of additional use cases that can dramatically improve the customer experience. For example, thanks to automation, predictive analytics, and artificial intelligence, customers won’t have to fill out lengthy forms during onboarding procedures, which will lower consumer friction and increase the onboarding success rate. According to research, 32% of businesses that use artificial intelligence in this way have already mentioned improving customer happiness and service.

Artificial intelligence can also assist banks in providing clients with the best goods and services more promptly and successfully. For example, banks can stop prospective customers from becoming bogged down in protracted KYC and onboarding procedures. Instead, accounts can be expanded immediately, allowing users to take advantage of all the services that a bank may provide. In other words, the effective use of artificial intelligence can improve client experiences significantly while also increasing bank productivity.

For all these reasons, artificial intelligence stands out as one of the most revolutionary advancements in recent decades, and innovation will only increase. But despite all of its undeniable advantages, it is not a panacea. There will always be places where strict regulations are still required, highly skilled human capital is still precious, and corporate cultures and business procedures are still of utmost importance. If such components are missing, the technology will inevitably fail to realise its full potential, preventing artificial intelligence from having the transformative effects it might otherwise have.

Financial institutions can benefit from new technological developments without subjecting themselves to unnecessary regulatory pressure by combining artificial intelligence with rules-based technology, another form of innovative data science.

What is a rule-based system?

A rule-based system uses rules created by humans to store, sort, and manage data. It imitates human intelligence in doing so.

A set of facts or a data source, as well as a set of rules for modifying that data, are necessary for rule-based systems to function. Because they frequently operate along the lines of “IF X happens, THEN perform Y,” These rules are also known as “if statements” in some contexts.

A good example is automation software like ThinkAutomation. Segmenting procedures into phases automates the processes.

The date or new business event comes first. The analysis follows, where the system arbitrarily handles the data, violating its rules. Any ensuing automated follow-up procedures occur next.

How does a rule-based system operate?

Unsurprisingly, rule-based systems function according to rules. These guidelines list the events and what should happen next (or are triggered). For instance, an email with the phrase “invoice” in it might serve as a trigger. The email might then be forwarded to the finance staff as a course of action.

These laws typically take the form of if statements. “IF” describes the event that must occur, and “THEN” details the course of action. Therefore, you would need to define 100 separate rules to design a rule-based system that could handle 100 different activities. The plan would then need to be updated, and new actions would need to be added.

In other words, you can instruct a machine using rules, and the machine will follow your instructions exactly. Rule-based systems will then carry out the actions until you instruct them to stop.

But remember that if you instruct it to act wrongly, it will do so.

What does a rule-based system not do?

Rule-based systems are frequently conflated with artificial intelligence and machine learning due to their early adoption in the area. They are neither artificial intelligence nor machine learning, though.

Given how similar the two can appear, getting them mixed up is simple. Both involve devices purportedly working independently. The distinction is that artificial intelligence is autonomous and can learn and adapt. On the other hand, rule-based systems function exactly as humans direct.

In other words, rule-based systems follow the rules set by humans rather than artificial intelligence or machine learning, which determines their actions. As a result, the system doesn’t solve the problem on its own or make wise choices.

In conclusion, financial institutions are looking to adopt a rules-based approach to financial regulation that will allow them to take advantage of the benefits of artificial intelligence. This approach will help to ensure that the financial sector remains stable and efficient while also providing the opportunity for new and innovative products and services to be developed.

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