Infinian: Credit Data is Stuck in the Past and the Consequences are Mounting

Despite a cost-of-living crisis and unpredictable economic outlook, too many lenders are forced to make credit decisions using information that belongs to another era. This outdated data is based on small samples, derived from national averages and historical surveys that fail to capture the volatility and diversity of financial realities defining life in the UK today.

That disconnect between data and reality harms consumers, distorts pricing, and drags on the wider economy. In short, affordability decisions are outdated before they are made. Borrowers are judged on figures that don’t reflect their actual costs, creditworthy customers are turned away, while others are approved for loans they can’t afford. Real-time, accurate, large-sample data is essential for fair and functional credit markets, and as an industry we must work to ensure decision-making is dragged into the modern day, to support the integrity of financial services and the aims of Consumer Duty for the good of financial services and consumer duty.  

Legacy Models Versus Modern Risks

For years, affordability models have relied on spending benchmarks from the Office for National Statistics (ONS) and other national-level datasets. ONS data, often sourced from the Living Costs and Food Survey, can lag real-world conditions by more than a year. It captures what households spent yesterday, not what they face today.

When models depend on national averages and retrospective surveys, they miss the nuances of how people earn and spend. Workers on variable incomes, renters, and those without long credit histories are most likely to be penalised. They may be financially stable, but legacy data can’t see that, leading to unnecessary declines and reinforcing the gap between those who can access affordable credit and those who can’t. Moreover, outdated data also increases the risk of false positives, meaning lenders may approve those who are likely to default.

False positives and negatives aren’t the only concerns, but also compliance – the Financial Conduct Authority’s Consumer Duty makes clear that firms must deliver “good outcomes” for retail customers, including through fair pricing and practical support. If lending decisions are based on incomplete or obsolete data, it becomes difficult to evidence that duty. The FCA’s own CONC 5.2A rules require a “reasonable assessment” of a customer’s ability to repay; data that misrepresents current affordability can’t reasonably support that test.

Legacy benchmarks, once a useful proxy, now risk embedding unfairness. They distort pricing, entrench exclusion, and hold back lending when the economy most needs momentum.

Gaining a True Perspective on Affordability

Fresher, more granular data is changing what responsible lending can look like. Real-time or high-frequency data streams from verified income flows, transaction activity, and recurring payment histories provide lenders with a comprehensive picture of affordability.

Unlike static surveys, these sources track actual behaviour. They show how a household’s disposable income shifts month to month, how energy or rent payments fluctuate, and how consistently people meet obligations. When used responsibly, this information enables lenders to make faster, more informed decisions that align with each borrower’s actual circumstances.

The payoff is fairer, more inclusive, and more responsible: three goals that don’t have to be in tension. Real-time credit intelligence can also help reduce unnecessary declines, extend access to consumers previously considered “thin-file,” and still maintain prudent risk controls. In other words, responsible lending doesn’t have to mean lending less; it means lending smarter.

It also helps lenders identify early signs of financial stress. If outgoings begin to rise faster than income, that signal appears immediately rather than months later, allowing firms to step in with tailored support before problems escalate. By closing the gap between reality and response, real-time data enables lenders to be both fairer to customers and more agile in managing their portfolios.

The Commercial Case for Better Data

Aside from the moral argument, and the benefits it will bring to compliance and consumer protection, there’s also commercial incentives to modernise credit data.

With access to better data, lenders can approve more of the right customers without increasing risk. Decision engines will become sharper, with improved acceptance rates and portfolio performance simultaneously.

Speed is another advantage. Consumers nowadays expect instant answers and laggy underwriting processes can make customers shift to faster competitors. Access to real-time credit data enables lenders to expedite these processes, thereby improving satisfaction and conversion rates. In a crowded market, those gains translate directly into loyalty and market share.

Basing decisions on current financial behaviours also reduces the need for unnecessary full-bureau checks and manual interventions, lowering the cost per decision and freeing up resources for higher-value activity.

Ultimately, modernisation is about competitiveness. Financial institutions, whether banks or fintechs, that invest in real-time credit intelligence today will be well-placed to earn trust, loyalty, and market advantage.

The Future of Fair Finance

Credit markets rely on accuracy, and accuracy in turn depends on timeliness. When the information behind lending decisions lags behind real life, fairness falters, capital is mispriced, and opportunities are lost.

Real-time, representative data allows lenders to extend credit responsibly, price risk precisely, and support customers before problems arise. It strengthens inclusion while improving overall performance.

In a world where household finances can change in weeks, lending models must keep pace with reality. Institutions that invest in live, comprehensive data today will set the benchmark for fair and effective finance in the years ahead.

Find out more at infinian.com