AI in Banking: Why Explainability Matters as Much as Accuracy

In today’s hyper-digital world, financial institutions are rapidly deploying artificial intelligence (AI) across their infrastructure in order to improve operations. AI is being employed in nearly every aspect of banking, from credit underwriting to risk management, and from fraud detection to fraud prevention. Yet, integrating AI into digital infrastructure alone is not sufficient.

AI models analyze an enormous body of information to produce outputs, but the reasoning behind their decisions is also often unknown, a phenomenon known as the “black box.” Because of this aspect, it is often difficult to discern the basis of the conclusions, find faults, or illicit manipulation, creating serious gaps in systems. 

Imagine a bank has deployed an AI-driven transaction monitoring solution in its infrastructure to reduce fraud. This AI model detects suspicious transactions, like unusually large money transfers or sudden activity from a dormant account. However, hackers secretly come in and change the training data and make fake fraudulent transactions look like normal transactions.

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For example, they may add many examples of large transfers labelled with the tag “safe.” Over time, AI will learn that large transfers in fact are normal, and it will stop flagging them as suspicious. In this scenario, AI will flag a transaction as “safe” or “fraudulent,” but it will not clearly give the reasoning, rules, or patterns it used to make that decision.

The same can happen in various use cases, such as credit underwriting and fraud detection. A credit scoring model that depends on real-time data feeds could fail on account of data corruption in upstream systems. If monitoring is not done consistently, AI systems can degrade over time, delivering inaccurate outcomes.

When this happens, banking professionals may struggle to justify why certain credit applications are approved or rejected, why specific transactions are flagged as fraudulent, or why customers face additional verification. This lack of transparency can lead to regulatory challenges, as financial authorities now demand clear explanations for automated decisions to ensure fairness and prevent bias.

Explainable AI provides a way to work with the challenges presented by AI by maintaining a certain level of understanding for the decision-making process. Rather than treating all AI outcomes as unquestionable, Explainable AI works to unravel the "why" behind those decisions.

This has a very extensive impact for banks and how they conduct business day-to-day. For example, if a customer is denied for a loan, Explainable AI can show if the reason was due to a low credit history, below income or too many existing liabilities. This also applies to fraud detection when analysts are alerted to a potential fraud case and get false positives; there can be a look back and get insight based on the red flags of the customer, such as an abnormal transaction pattern or corresponding geolocation.

Decisions can be improved by gaining deeper insights into the decision-making process, reducing errors, employees can close cases quickly and ensure transparency with the customers. Clearly Explainable AI is the bridge between the disparity of belief using AI models and decisions made by humans, which not only provides better banking decisions but also presents businesses with better, more reliable customer transactions.

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Explainable AI also enhances banks' ability to meet regulatory expectations. Explainable AI provides banks with auditable evidence they can deliver to regulators that explains exactly the reasoning for a decision, like denying a loan or flagging a transaction, and how it acted in a legally compliant manner. The goal is to protect against regulatory action, but very importantly, even more so, this will demonstrate that AI is being used in a responsible manner that reinforces ethical practice and public trust.

As banks move forward and embrace AI, they will need to stake their reputation on building a hybrid framework where efficient AI is tempered by human judgment and accountability. Banks will also need to adopt Explainable AI, but also systems to track, monitor, and audit these models and decision-making patterns as well as educating people, employees, and decision-makers, who can make sense of AI for the purpose of interpretation. AI will allow institutions to adopt innovative processes, while also reassuring trust in the new area of a data-driven and regulated ecosystem.

Priyabrata Pradhan is VP of Engineering at Signzy.