When AI decides who gets credit

For generations, a person’s financial fate was often boiled down to a three-digit number. The credit score—a static, backward-looking snapshot of an individual’s financial history—ruled the lending world. Today, that old model is losing its power. A new era of lending is emerging, driven by artificial intelligence. AI-powered credit scoring is moving beyond the confines of traditional data, creating a more dynamic, inclusive, and, at times, controversial lending landscape. This is the new frontier of fintech, and for every bank and lender, it presents a challenge they cannot afford to ignore.

The Problem with the Old Score

The traditional credit scoring model, exemplified by FICO in the US or Experian in the UK, has a simple premise: past performance predicts future behavior. It relies on a narrow set of data points: payment history, amounts owed, length of credit history, and new credit. While effective for many, this model created a massive gap in the market.

It shut out millions of “credit invisibles”—individuals with little to no credit history. This includes young people, recent immigrants, and many members of the gig economy. A responsible young professional who pays rent and utility bills on time but has no credit card history might be denied a loan. A freelancer with a healthy income but inconsistent paychecks could be flagged as high-risk. The old system was rigid and often unfair, leaving a vast, creditworthy population underserved and unable to access the capital they needed.

A New Data-Driven Reality

AI is changing the conversation. Machine learning algorithms can process and analyze a far wider array of data points than any human or traditional model. They can leverage what’s known as “alternative data.” This includes:

• Behavioral Data: Spending patterns, savings habits, and app usage.
• Transactional Data: On-time rent and utility payments, mobile phone bill history, and bank account balances.
• Digital Footprint: E-commerce transactions and other digital signals.

This shift allows lenders to build a much more complete and dynamic picture of an applicant’s financial behavior. Instead of a single static number, a lender receives a score based on a multitude of real-time signals. The results are already proving significant.

A study revealed that a non-banking financial company using an AI-based credit model saw a 30-35% increase in loan approval accuracy and a 25% reduction in bad loans. Similarly, a UK high street bank used an AI platform to predict credit default with stunning accuracy, capturing 83% of bad debt that its traditional scoring models missed. These models don’t just reduce risk; they also empower lenders to responsibly extend credit to a wider range of customers.

For example, UK-based lender Oakbrook Finance has built its entire business around this principle, using a proprietary technology platform called O6K. They leverage Open Banking data and partner with firms like Experian to access alternative data sources. This provides a more holistic view of a customer’s finances, helping them approve loans that would have been denied under traditional scoring. In one pilot, this approach led to a potential 10% increase in acceptance rates for certain customer segments without compromising affordability. By using these new insights, Oakbrook aims to “change lending for the better” by providing fairer and more personalized access to credit.

The Elephant in the Room: The “Black Box” Problem

For all its benefits, AI in lending presents a major challenge: the “black box” problem. The very complexity that makes these models powerful also makes them difficult to understand. If an AI algorithm denies a loan, can the lender explain why?

This lack of transparency raises significant legal and ethical concerns. In the US, the Equal Credit Opportunity Act (ECOA) requires lenders to provide a specific reason for denying credit. In the UK and Europe, regulations like the GDPR give individuals the “right to an explanation” for decisions made by algorithms. When an AI model’s decision-making process is too complex to unpack, firms risk violating these core consumer protections and facing legal action.

The industry is responding with a technology called Explainable AI (XAI). XAI platforms are designed to show how an algorithm reached its conclusion. They provide a clear, auditable trail that allows human analysts to understand and, if necessary, justify the AI’s decision. This is not just a regulatory fix; it’s a way to build trust with customers and ensure that AI models are free from hidden biases.

The Future of Lending

The future of lending is not about replacing human expertise with machines. It’s about augmenting it with intelligent, data-driven systems. Financial institutions must move with purpose and caution, focusing on a few key areas:

1. Embrace Alternative Data: Expand your data sources beyond the traditional credit report to better serve the modern consumer.
2. Invest in Explainable AI: Prioritize tools and platforms that provide transparency, ensuring you can explain every credit decision.
3. Establish Clear Governance: Implement strong ethical AI frameworks. Conduct regular audits to check for algorithmic bias and ensure fairness.

AI in lending is not a trend; it’s the future. It holds the key to a more efficient and inclusive financial system, but only if the industry handles its power with care, accountability, and a commitment to transparency.