Credit score

Credit Kudos Launches Open Banking Credit Score

Credit Kudos has launched Signal, a credit score based on Open Banking and machine learning that it says enables lenders to increase acceptance of previously rejected customers and reduce defaults.

Singal uses a combination of machine learning and transaction data collected by Open Banking to predict an individual’s repayment probability. The model was trained on transaction data and loan results collected over more than six years and allows lenders to score all applicants, not just those with a credit history.

The company says lenders can use the system to reach currently underserved customers, such as those with thin credit records, who are new to the country, or who have poor credit histories but are now creditworthy.

With increased regulatory scrutiny of machine learning-based models, Signal has a built-in explainability module, showing the five characteristics that contributed the most to a person’s score, helping lenders stay compliant with the rules of transparency and fairness.

The firm says a lender using the Signal credit score for those previously refused found it could accept a third more applicants, while maintaining its default rate. When used for all decisions, they found it could reduce overall defect rates from 11.7% to 9.7%, while increasing acceptances from 17.5% to 29.8%.

Freddy Kelly, CEO of Credit Kudos, comments: “Credit scores based on traditional credit data are not only limited, but can lead lenders to falsely deny those who are creditworthy. Our new Open Banking-powered credit score, Signal, allows lenders to accurately assess all applicants – including those with light records – meaning they can safely increase acceptances without increasing risk or payment defaults. It is highly accurate, fast and fully explainable, all essential features to help lenders make better, more informed and responsible decisions. »