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. global, local feature-based and local
instance-based) that are required by different people in different situations.
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.Comment: 19 pages, David C. Wyld et al. (Eds): ACITY, DPPR, VLSI, WeST, DSA,
CNDC, IoTE, AIAA, NLPTA - 202