Occasional Paper No. 28: Preventing financial distress by predicting unaffordable consumer credit agreements: An applied framework

In this paper, we provide theoretical and practical evidence to help develop more effective affordability rules.

Occasional Paper No. 28 (PDF)

This research is the companion paper to FCA Occasional Paper No. 20 ‘Can we predict which consumer credit users will suffer financial distress?’

The research is designed to inform the consultation on proposed changes to rules and guidance on assessing creditworthiness in consumer credit.

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Approximately one in six people with consumer credit debt suffer moderate to severe ‘financial distress’, experiencing financial difficulties or other issues such as mental health problems from the strain of repaying their debts. A challenge for regulators (and firms) is how to design affordability rules which minimise financial distress by limiting access to credit to those who cannot afford to repay, without such rules excessively restricting affordable credit access or imposing processes which unnecessarily increase the costs of borrowing.

This paper provides theoretical and practical evidence to help develop more effective affordability rules. We find it is possible to detect particular groups of consumers who have a high risk of suffering forms of financial distress. There are, however, some important limitations to these rules.

In the absence of adequate affordability rules, consumers can suffer potentially avoidable financial distress. There are strong, economic reasons for this. Lenders are incentivised to offer credit to applicants where it is expected to be profitable, irrespective of the risk of financial distress to the applicant. Consumers may take out such credit if they do not realise they are at a high risk of financial distress. The greater the ability to accurately discriminate between high and low risk applicants, the higher the likelihood that firms’ affordability rules will prevent financial distress by avoiding lending to those at high risk.

Using data on 2.4 million applications for high-cost short-term credit (payday) loans, we examine the ability to predict financial distress. We construct measures of financial distress from detailed credit reference agency (CRA) data. While much financial distress cannot be predicted, high credit risk applicants are at a substantially higher risk of the observable, objective measures of financial distress we measure in this paper, relative to other applicants. Applicants who have outstanding consumer credit debts near or above their annual net individual income (known as the DTI ratio) also have a significantly higher risk of suffering financial distress.

We find substantial differences in the total value of outstanding debts recorded on two different CRAs for the same individuals, at the same points-in-time. We also conclude that data used by lenders for predicting financial distress are unlikely to accurately estimate incomes and expenditures for some applicants. This limits the ability of lenders to predict financial distress and decide which consumers not to lend to.


Benedict Guttman-Kenney and Stefan Hunt

Benedict Guttman-Kenney works in the Behavioural Economics & Data Science Unit of the Financial Conduct Authority

Stefan Hunt is the FCA’s Head of Behavioural Economics & Data Science


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