UK regulators can now predict when you will go broke – here’s how

The UK’s Financial Conduct Authority (FCA) has developed a powerful new way to spot consumers heading toward serious financial distress, potentially months or even years before they miss payments.

Using anonymised credit file data and advanced analytics, the regulator can now track “consumer credit journeys” across the entire market, identifying early warning patterns that traditional credit scores often miss.

The approach, detailed in a blog post published on Friday (10 April) by Alison Walters, the FCA’s Director of Consumer Finance, marks a significant shift toward proactive, data-driven regulation.

Moving beyond static indicators

For years, regulators and lenders have relied on static indicators like credit scores, delinquency rates, and payment histories. These tools are good at flagging problems once they occur, but they frequently miss the direction, speed, and persistence of financial strain.

The FCA’s new proof-of-concept changes that. Instead of looking at a single moment in time, it maps how people transition between five distinct segments of credit behaviour:

• Distress (~5% of consumers): Severe issues, such as bankruptcy or falling more than three months behind on payments.
• At Risk (~5%): Early red flags like recent missed payments, maxing out credit limits, or opening multiple new unsecured accounts.
• Secured Credit Users (~1 in 3): Stable borrowers with active mortgages and controlled credit use.
• Unsecured Credit Users (~1 in 5): Active but stable users of credit cards, personal loans, and similar products.
• Low Credit Engagement (~1 in 3): People with limited or no formal credit.

By analysing flows between these groups, the FCA can see that distress rarely strikes without warning. Most people who end up in serious trouble first spend time in the “At Risk” category, where modest stresses, such as gradually rising balances or occasional missed payments, start to compound.

The regulator is also applying survival analysis, a statistical technique commonly used in medical research to predict time-to-event outcomes, to estimate how long someone is likely to remain financially stable. Key findings include:

• Consumers in the “Low Credit Engagement” and “Secured Credit” groups tend to stay stable the longest.
• Those in the “At Risk” segment have the shortest stability periods and are most likely to slide quickly into distress.
• Specific triggers, such as recent missed payments, opening several new unsecured accounts, or rapidly increasing credit utilisation, significantly shorten the time until distress hits.

A step toward smarter regulation

The FCA has a market-wide view that no single lender possesses, allowing it to spot patterns of emerging harm across groups of consumers or particular firms.

This should enable more targeted supervision, earlier interventions with lenders, and better enforcement of rules requiring firms to support borrowers in difficulty before they fall into arrears.

Walters said that the goal is evidence-based regulation that delivers good outcomes: “We can sharpen our focus on affordability and vulnerability, separating momentary blips from ongoing strain.”

In the future, the FCA plans to incorporate richer Product Sales Data (PSD) on credit agreements, which will provide even more comprehensive tracking of how consumers move between different credit products.

This initiative builds on the FCA’s broader push under the Consumer Duty to ensure firms deliver good outcomes and treat vulnerable customers fairly.

It also aligns with earlier research showing that debt-to-income ratios and certain credit behaviours are strong predictors of future distress.

For consumers, the approach could mean earlier, more tailored support, such as forbearance options or referrals to debt advice, rather than reactive collections or defaults.

For lenders, it signals that the regulator will increasingly use sophisticated data analytics to hold them accountable for spotting and addressing risks proactively.

The FCA is now inviting collaboration with academics, tech firms, and industry to refine these techniques further.

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