Explainable AI for Interpretable Credit Scoring

With the ever-growing achievements in Artificial Intelligence (AI) and the recent boosted enthusiasm in Financial Technology (FinTech), applications such as credit scoring have gained substantial academic interest. Credit scoring helps financial experts make better decisions regarding whether or not to accept a loan application, such that loans with a high probability of default are not accepted. Apart from the noisy and highly imbalanced data challenges faced by such credit scoring models, recent regulations such as the right to explanation' introduced by the General Data Protection Regulation (GDPR) and the Equal Credit Opportunity Act (ECOA) have added the need for model interpretability to ensure that algorithmic decisions are understandable and coherent. An interesting concept that has been recently introduced is eXplainable AI (XAI), which focuses on making black-box models more interpretable. In this work, we present a credit scoring model that is both accurate and interpretable. For classification, state-of-the-art performance on the Home Equity Line of Credit (HELOC) and Lending Club (LC) Datasets is achieved using the Extreme Gradient Boosting (XGBoost) model. The model is then further enhanced with a 360-degree explanation framework, which provides different explanations (i.e. Evaluation through the use of functionallygrounded, application-grounded and human-grounded analysis show that the explanations provided are simple, consistent as well as satisfy the six predetermined hypotheses testing for correctness, effectiveness, easy understanding, detail sufficiency and trustworthiness. Credit scoring models are decision models that help lenders decide whether or not to accept a loan application based on the model's expectation of the applicant being capable or not of repaying the financial obligations [1]. Such models are beneficial since they reduce the time needed for the loan approval process, allow loan officers to concentrate on only a percentage of the applications, lead to cost savings, reduce human subjectivity and decrease default risk [2]. There has been a lot of research on this problem, with various Machine Learning (ML) and Artificial Intelligence (AI) techniques proposed. Such techniques might be exceptional in predictive power but are also known as black-box methods since they provide no explanations behind their decisions, making humans unable to interpret them [3]. Therefore, it is highly unlikely that any financial expert is ready to trust the predictions of a model without any sort of justification [4]. With regards to credit scoring, lenders will need to understand the model's predictions to ensure that decisions are made for the correct reasons.