Newcastle Building Society recently undertook some enhancements of its customer risk scores and wanted to understand whether applying AI-based techniques to mortgage risk modelling could yield additional benefits. That’s why it appointed Jaywing, with its considerable experience both in traditional and AI risk modelling.
Jaywing took Newcastle Building Society’s existing scorecard development samples for both Buy to Let and residential mortgages, using Archetype to create the models from scratch in just a few hours.
Archetype is unique because it enables the analyst to control and explain the model, ensuring that it adheres to exactly the same business rules as their traditional risk models. This guarantees a model which will always generate a result that is acceptable to the business. The methodology used by Archetype generates more predictive models, enabling its users to get better at identifying risk or fraud cases, to identify customers at risk of leaving for other providers, or even, to make better marketing offers.
Uplift on Newcastle Building Society’s Buy to Let bureau-devised model
On the residential model
In both cases, these comparisons are with an optimal, traditionally-generated model using the same source and outcome data. This equates to a huge improvement in the lender’s ability to identify risk, resulting in a bad debt reduction for the same level of rejects.
“Jaywing’s flexible approach and comprehensive track record in both traditional and AI risk modelling made it a sound partner for us to work with to boost the effectiveness of our existing models. Archetype’s flexibility provides us with a range of model implementation options, from deploying model code in our own decision platform to using Jaywing’s API, and we are working with Jaywing to create the most advantageous approach for us to ensure the benefits are realised ASAP.”
Dr J. Serradilla, Senior Data Modeller at Newcastle Building Society