By Julian Kirsch, Head of Risk & Compliance at Aryza
Artificial intelligence is no longer just a back-office experiment. For banks, lenders, and investment firms, it has become an essential tool for managing risk in an increasingly complex market. From detecting fraud to sharpening credit decisions and optimising portfolios, AI is helping financial institutions shift from merely containing risk to transforming it into a source of competitive advantage.
Looking Beyond Traditional Credit Models
Conventional credit scoring has always depended heavily on historical data, such as past repayment behaviour, credit reports, and income statements. While helpful, these measures offer only a limited view of a borrower’s financial resilience. Today, however, AI is broadening that perspective. Machine learning models can incorporate real-time data like Open Banking feeds, transaction histories, and spending patterns. By analysing subtle relationships within this data, AI can deliver more accurate affordability assessments and enable quicker, more confident lending decisions.
For senior decision makers, this means loan growth no longer has to come at the expense of rising default risk, a long-standing trade-off in credit management. This means institutions can make responsible lending decisions that protect customers while still unlocking growth opportunities.
Spotting Fraud and Financial Stress Early
Fraud and borrower distress rarely become evident openly. The initial signs are often hidden among millions of data points, too complex for manual oversight. However, AI is designed to address this challenge. Algorithms can identify sudden spikes in transaction volumes, unexpected shifts in repayment behaviour, or unusual geolocation patterns, e.g. clues that might indicate fraud or early financial stress.
From a compliance perspective, early detection is about more than capital protection; it demonstrates a commitment to treating customers fairly and giving them access to support at the right time to achieve the right outcomes for them. Detecting these warning signs early provides institutions with a technical advantage and the ability for them to take proactive measures. It safeguards capital, helps preserve reputation, and importantly, enables early engagement with customers who may need support before problems escalate.
Predictive Power for Portfolios and Collections
AI is also reshaping how entire portfolios are managed. Predictive analytics can forecast which areas of a receivables portfolio are most likely to become delinquent or which investment positions pose emerging risks. With this insight, finance leaders can rebalance exposure, adjust capital buffers, or modify investment strategies well before market shifts happen.
In collections, AI can help teams to adopt the most effective engagement strategies, prioritising outreach where success is most likely and tailoring repayment options to each customer’s circumstances. This is where risk management and customer outcomes align most clearly by using AI responsibly to reduce losses while giving customers realistic, personalised repayment paths. The result is a more efficient operation and an enhanced customer experience, demonstrating that risk control and relationship management can work hand in hand.
The Non-Negotiables
The opportunities AI provides are balanced by the responsibilities it requires. Regulators expect transparency regarding how automated decisions are made and call for safeguards to prevent bias.
Boards and senior executives, therefore, need clear governance frameworks, including processes for validating models, regular bias testing, and detailed audit trails. This isn’t just about ticking compliance boxes. Strong oversight reassures investors and customers alike that AI decisions are fair and explainable.
How Risk Management Can Give a Strategic Edge
Handled responsibly, it does more than just cut losses. It gives institutions the ability to scale lending confidently, predict market shifts, and respond to risks in real-time.
Risk and compliance functions are evolving, from being perceived as gatekeepers to becoming enablers of safe innovation. For finance leaders, the message is clear: AI-powered risk management is no longer optional. Those who invest not only in the technology but also in the ethical and regulatory frameworks around it will be best positioned to turn risk oversight from a regulatory obligation into a genuine competitive edge.
The bottom line is that AI is transforming risk management from a defensive measure into a strategic driver of growth, and those who adopt it will lead the way in the future.