The credit scoring revolution

In the midst of geopolitical uncertainty and market volatility, inflation continues to bite at the pockets of many Brits. Inevitably, more people than ever are turning to credit to make ends meet, with recent government figures revealing that 84% of UK adults have some form of credit or loan, with 14% saying they’re using more credit than ever before.

 

Traditional credit scoring is failing the people who need it most, excluding millions of creditworthy borrowers. The FCA’s proposals to mandate data sharing are a step in the right direction to address this increasing concern - but it won’t fix a system that’s failing to use all available data to get a real-time, holistic view of a financial position.

 

 

The people the system leaves behind

Many professionals, from freelancers to entrepreneurs, face significant barriers to home ownership and other loans due to rigid affordability checks.

 

This is because traditional methods of credit scoring give lenders insufficient information that fails to capture current levels of financial stability or alternative indicators of creditworthiness, such as additional sources of income or assets. They can also be unrepresentative and biased against certain demographics.

 

What lenders need is a real-time picture of someone’s financial life. Their income patterns, spending behaviour, and current affordability.

 

Imagine a successful, self-employed entrepreneur with irregular income. They are building their business, but not their credit history, so traditional systems say ‘no’. In reality, they’re a good risk. By focusing on historical borrowing behaviour, people with thin or no credit files are often excluded.

 

Through Open Finance and developments in AI, lenders can now move beyond these constraints. By drawing on enriched transaction data and advanced categorisation technology, they’re able to make more accurate, fairer, and personalised lending decisions.

 

 

Better data, faster and fairer outcomes

Being able to understand the real-life ebb and flow of a person’s finances is what makes data enrichment so powerful.

 

By cleaning, standardising and enhancing the abundance of raw, real time transactional data, AI-powered categorisation can convert data into valuable and actionable insights. Expanding the open finance ecosystem in this manner allows lenders to find better outcomes for their customers.

 

AI-powered categorisation makes this possible at scale. Applied to enriched transaction data, it can accurately classify even complex financial activity, track how customer behaviour evolves over time, and anticipate future patterns that rigid, rule-based systems would never detect.

 

Transaction categorisation adds further depth, identifying not just what a transaction is, but why it happened and how it fits into the broader context of someone’s financial life. This transforms raw data into genuine insight, enabling lenders to spot the subtle patterns that traditional models routinely miss.

 

The benefits are wide-ranging. Smarter risk and affordability assessments mean better credit decisions and fewer defaults. A clearer picture of a customer’s cashflow, particularly at the point of applying for a loan, allows lenders to offer products that are genuinely tailored to that person’s circumstances, driving customer satisfaction and retention.

 

It also helps lenders commit to their responsibilities under Consumer Duty. Early warning signs of financial distress, like falling income, rising expenses, and reduced liquidity, can be identified before problems escalate. This empowers lenders to intervene early, offering support rather than waiting for missed payments.

 

And for fraud, enriched transaction data is one of the most powerful tools available, flagging inconsistencies and suspicious patterns that give lenders the chance to act before harm is done.

 

 

Real data for real lives

Automated enrichment and categorisation streamlines the entire lending process, which reduces the operational burden, sharpens risk management and ultimately allows lenders to say yes to more of the right people.

 

One provider already seeing the benefits is Admiral Money, which partnered with Moneyhub to deploy AI-powered affordability checks across its loan referral process.

 

The results were significant. Admiral Money approved loans for an additional 18% of applicants, and underwriting time dropped from 30 minutes to 15 minutes per application. Repayment rates and fraud detection both improved, which demonstrated that speed and rigour are not mutually exclusive.

 

Individual data points, viewed in isolation, rarely tell the full story. But enriched, categorised and contextualised, they enable lenders to see the person behind the application and make decisions that reflect reality rather than history.

 

The future of lending isn’t just about better data. It’s about finally building a system that works for the people who need it most.

 

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Dan Scholey is Chief Product Officer at Moneyhub

 

Main image courtesy of iStockPhoto.com and anyaberkut