Credit reference agency Credit Kudos has launched Signal, an “explainable” Open Banking credit score to help lenders “serve more customers and reduce defaults”.

The scoring system enables lenders to move beyond the limitations of traditional credit data, allowing them to accurately score all applicants, not just those with credit history.

Signal aims to help lenders using “highly relevant” and up-to-date financial behaviour data, as well as using machine learning with “clear explainability”.

The system uses a combination of that machine learning and Open Banking-gathered transaction data to accurately predict an individual’s likelihood of repayment.

The model has been trained on transaction data and loan outcomes, collected for more than six years. It ensures the data is highly accurate and more detailed than what lenders have access to through traditional credit data.

Credit Kudos promises lenders will be able to understand the rationale for compliance purposes and the risk profile of their potential customers.

One lender having already used Signal for those previously declined for credit found that it could accept a third more applicants, while maintaining its default rate. When used for all decisions they found it could reduce overall default rates from 11.7 per cent to 9.7 per cent, whilst increasing acceptances from 17.5 per cent to 29.8 per cent.

Freddy Kelly, chief executive of Credit Kudos, said: “Credit scores based on traditional credit data are not only limited but can lead to lenders wrongly declining those who are creditworthy. Signal allows lenders to accurately assess all applicants - including those with thin files - meaning they can safely increase acceptances without increasing risk or defaults.”

Share Story: