The Financial Conduct Authority published a research blog describing a proof-of-concept that uses credit reference agency credit-file data and advanced statistical techniques to track consumers’ credit journeys and identify emerging financial distress earlier than traditional indicators such as delinquency rates and credit scores. The FCA presents the work as a way to support more targeted supervision and earlier engagement with firms, informed by a market-wide view that individual firms cannot replicate from their own data. The model assigns each person to one of five segments and tracks how individuals transition over time between Low Credit Engagement (about 1 in 3 users), Secured Credit Users (about 1 in 3), Unsecured Credit Users (about 1 in 5), At Risk (about 5%), and Distress (about 5%, including severe events such as bankruptcy or being more than three months behind on payments). Using transition analysis and “survival analysis”, the FCA finds clear flows from At Risk into Distress and some recovery back to Unsecured or Secured states, with the At Risk group having the shortest period of financial stability; recent missed payments, multiple new unsecured accounts, and increasing credit-limit utilisation are associated with moving into Distress faster. The FCA links the approach to its focus on affordability and vulnerability and to its expectations on supporting borrowers in financial difficulty, including providing support before arrears and tailoring it for customers with vulnerabilities. Next, the FCA expects to build on this work by using its product sales data on credit agreements in further data science projects, noting this dataset should offer more comprehensive coverage than credit reference agency data when fully operational. It also plans to incorporate Deferred Payment Credit, often referred to as Buy Now Pay Later, in future iterations of the analysis.