Likelihood Inference and Interpretable Machine Learning for Risk Analysis and Loan Decision Making

Bank loan online banking

We develop an integrated interpretable machine learning (ML) model and AI system to support efficient, consistent and informative loan decision-making. Loan decision-making, also known as underwriting, is a key process of operations management in commercial banks. New technology and ML algorithms have opened the door for more efficient loan application and approval process compared to the traditional manual process. Digital technology advancements allow the use of big data to find multitudinous ways in which relevant factors influence each other. Complex relationships among many factors can be found by advanced ML models, which can aid more accurate classification of the risk status of loan applications.

While AI systems have huge potential to revolutionise loan decision-making, it is increasingly recognised that to be successful, it is vital to explain decisions made by ML models and their inner workings. We address this concern by developing an integrated interpretable ML model and AI system to support efficient, consistent and informative loan decision-making. Factors in the ML model are selected, measured and structured on the basis of underwriter guidelines and default rules, while nonlinear relationships among the factors are explored by data analysis and likelihood inference. A unique feature of this modelling strategy is that the integrated ML model makes full use of data analysis to capture nonlinearity among relevant factors. The model can also be optimally trained to achieve high accuracy in making correct loan decisions, whilst the loan decision-making process remains highly interpretable according to the guidelines and rules embedded in the model.

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