An end-to-end deep learning approach to credit scoring using CNN + XGBoost on transaction data

Lars Ole Hjelkrem, Petter Eilif de Lange and Erik Nesset

Abstract

The performance of credit scoring models is closely linked to a bank’s profitability. Application scoring models for potential customers usually perform worse than models for existing customers. This is due to the lender not having access to the financial behavioral data of potential customers. Access to such data about potential customers could therefore increase a bank’s profitability. Open banking application programming interfaces (APIs) provide access to 90 days of historical data on potential customers’ balances and transactions. We examine the performance of credit scoring models developed using such data from a Norwegian bank. We find that traditional regression models perform poorly, while machine learning (ML) methods can provide models that perform satisfactorily based on these data alone. Further, we find that the best performing models are based on an end-to-end deep learning approach, where machine learning algorithms create explanatory variables based on non-aggregated data. These results indicate that data available through the open banking APIs can be an important data source when banks assess the creditworthiness of potential customers. In combination with end-to-end deep learning methods they have the potential to increase the performance of a bank’s application credit scoring models and thus increase the bank’s profitability.