Agus Sudjianto

Machine Learning (ML) has quickly become ubiquitous in banking for both predictive analytics and process automation applications. However, banks in the US remain cautious in adopting ML for high-risk, regulated areas like credit underwriting due to key concerns including ML explainability and robustness. To address the inadequacy of post-hoc explainability tools in XAI for critical applications, we have developed inherently interpretable machine learning models, including Deep ReLU networks and Functional ANOVA-based ML models. Model robustness is a key requirement as models will be subjected to a continually changing environment in production. A conceptually sound model must be able to function properly – without continuous retraining – while remaining relatively invariant in a changing environment. Recently, we released the PiML (Python Interpretable Machine Learning) package as a tool to design inherently interpretable models, and to test machine learning robustness, reliability and resilience.