Credit needn’t chase its tail

Anyone who's been in lending knows it never feels good to say 'no.' In the end, lenders hope to help as many consumers as possible achieve their financial goals. This sentiment rings especially true in the evolving world of credit card solutioning, where AI is driving a paradigm shift. By enhancing risk management, meeting consumer expectations, and providing a competitive edge, AI-driven technologies are transforming every aspect of the credit card lifecycle—creditworthiness assessments to fraud detection, customer personalization, and overall operational efficiency.

Spreading under AI influence

One of the most significant applications of AI in credit card solutioning is in financial spreading, where data from borrower financials is systematically transferred into a bank’s analysis models. In the context of credit cards, this AI-driven automation is invaluable for underwriters when analyzing complex financials, such as balance sheets and tax filings. By automating the extraction and validation of this data, AI helps underwriters conduct thorough credit assessments with real-time insights into liquidity ratios, credit health, and borrower trends. This not only accelerates the underwriting process but also ensures that decisions are backed by comprehensive and validated financial data. Fintech platforms using AI for financial spreading are designed to compile data from disparate sources into unified dashboards. This facilitates a more seamless experience for both the underwriters and their counterparts in other bank departments.

Speed is of the AI essence

Especially in the commercial and consumer lending space, financial institutions often deal with high volumes of unstructured data, making manual processes prone to errors and delays. AI-powered solutions ingest vast amounts of historical data from thousands of companies, create standardized structures, and automate updates as new data becomes available. This automation drastically reduces the time taken for credit assessments and other financial analyses, which in turn leads to faster decision-making. So, when customers expect instant approvals, AI provides banks and fintechs with the speed and accuracy needed to deliver satisfying customer experiences. Such processing of data in real-time offers agile opportunities for banks to adapt to changing market conditions.

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In other editions of BFSI Vue

The Wolf of Invoice Street (Nov ‘24)

Paperwork and propensity—same old loan game? (Sep ‘24)

AI and APIs in insurance land (Aug ‘24)

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The ‘alternative’ path to inclusion

Traditionally, credit decisions have been made based on standardized metrics like credit scores and income, which leaves large segments of the population underserved. AI-driven models incorporate a broader spectrum of data points—ranging from bank account activity and employment history to e-commerce behaviors and even social media patterns—to paint a more accurate picture of a consumer’s financial health. The real power of AI lies in its ability to identify those borrowers that are improving their credit. So, although their FICO score dampened down just because of what happened in the past, the AI score takes into account recent positive behavior from borrowers.

For financial institutions, this approach not only improves the accuracy of credit decisions but also expands access to credit for previously underserved populations. By allowing AI to analyze alternative data, banks can offer personalized credit card solutions to customers who may not fit traditional credit profiles, including gig workers, new-to-credit individuals, and small businesses.

Fraud fighter: Rise of the machine

Hackers used to break into stores of cre­den­tials held by pay­ment facil­it­at­ors or e-com­merce sites. Nowadays, they find it easier to guess at legit 16 digit num­bers, expiry dates and CVV num­bers using the same cheap pro­cessing power that powers AI deployments everywhere. Detecting anomalies in user behavior by analyzing spending patterns, transaction histories, and even device locations in real time is now the job of machine learning. AI-driven systems are deployed to analyze vast datasets to identify patterns of fraudulent behavior, flag suspicious transactions, and prevent fraud before it occurs. AI systems continuously evolve, learning from each transaction and refining their detection capabilities. This adaptability is crucial in the cat-and-mouse game between financial institutions and fraudsters, where both parties are using sophisticated technology to outmaneuver each other. For credit card issuers, the use of AI in fraud prevention not only safeguards customers but also protects the bottom line by minimizing financial losses.

Risk it for the rewards

Regulators are increasingly scrutinizing the use of alternative data and AI models in credit decisions to ensure that these technologies do not inadvertently introduce biases that could disadvantage certain groups. To be in compliance, it is now possible to backtest lending models and ensure that they adhere to regulatory requirements. By running historical loan applications through AI models, financial institutions can assess whether their models are making fair decisions across different demographic groups. If biases are detected, AI can also help identify which variables are causing the disparities, allowing banks to adjust their models accordingly.

What if?

Questions like What would have happened if we had made different decisions? Could we have brought more consumers in and grown even more with lower risk? crop up with the widespread deployment of embedded payment products, a surge of interest in consumer loans from private capital investors and the rapid evolution of AI. Approaches that not only improve the accuracy of credit decisions and greatly impact financial inclusion, but also increase personalization and make for a more engaged and efficient customer experience at the point-of-sale are redefining the landscape.

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Also read The AI Vue

Smart AI still needs human eyes (Nov '24)

Agents are here → AI’s shaken and stirred (Sep ‘24)

Lifting the lid on LLMs (Aug ‘24)

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Decades-old legacy systems along with the some 1000 applications at play, and stringent regulatory and governance policies keep banking 'grounded', explains Dr.Ahmed Darwish, Head of Digital Delivery, Bank Al Bilad, on the industry's resistance to moving to the cloud.

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