Using Small Business Banking Data for Explainable Credit Risk Scoring


  • Wei Wang Intuit Inc.
  • Christopher Lesner Intuit Inc.
  • Alexander Ran Intuit Inc.
  • Marko Rukonic Intuit Inc.
  • Jason Xue Intuit Inc.
  • Eric Shiu Intuit Inc.



Machine learning applied to financial transaction records can predict how likely a small business is to repay a loan. For this purpose we compared a traditional scorecard credit risk model against various machine learning models and found that XGBoost with monotonic constraints outperformed scorecard model by 7% in K-S statistic. To deploy such a machine learning model in production for loan application risk scoring it must comply with lending industry regulations that require lenders to provide understandable and specific reasons for credit decisions. Thus we also developed a loan decision explanation technique based on the ideas of WoE and SHAP. Our research was carried out using a historical dataset of tens of thousands of loans and millions of associated financial transactions. The credit risk scoring model based on XGBoost with monotonic constraints and SHAP explanations described in this paper have been deployed by QuickBooks Capital to assess incoming loan applications since July 2019.




How to Cite

Wang, W., Lesner, C., Ran, A., Rukonic, M., Xue, J., & Shiu, E. (2020). Using Small Business Banking Data for Explainable Credit Risk Scoring. Proceedings of the AAAI Conference on Artificial Intelligence, 34(08), 13396-13401.



IAAI Technical Track: Emerging Papers