Credit Kudos launches Open Banking credit score

The score 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 helps lenders predict risk using highly relevant and up-to-date financial behaviour data, as well as using machine learning capable of clear explanations – so lenders can understand the rationale for compliance purposes and the risk profile of their population. 

Signal uses a combination of machine learning and Open Banking-gathered transaction data to 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. 

The three key benefits are:

• Increase acceptances: With a highly accurate understanding of anyone’s financial situation, lenders can reach currently underserved customers. This includes those who have a thin credit file, are new to the country, or who have adverse credit history but are now creditworthy – which Credit Kudos estimates to be around six million.
• Reduce defaults: Signal leverages Open Banking data and insights to accurately predict someone’s creditworthiness. Using machine learning trained on more than six years of data, lenders can assess people more accurately than with traditional credit scores, which reduces the likelihood of a borrower defaulting. 
• Understand and evidence risk decisions: The Open Banking credit score has clear explainability so lenders can understand and evidence the decisions driven by machine learning, allowing them to fulfill regulatory requirements around transparency and fairness. It does this by surfacing the five features that most contributed to the person’s score.

According to ffnews.com, one lender using the Signal credit score for those previously declined found that it could accept a third more applicants, while maintaining its default rate – showing there was no additional risk to taking on more applicants that they would previously have declined based on traditional, non-Open Banking credit scores. When used for all decisions they found it could reduce overall default rates from 11.7% to 9.7%, whilst increasing acceptances from 17.5% to 29.8%.