  Artificial Intelligence (AI) has created the single biggest technology
revolution the world has ever seen. For the finance sector, it provides great
opportunities to enhance customer experience, democratize financial services,
ensure consumer protection and significantly improve risk management. While it
is easier than ever to run state-of-the-art machine learning models, designing
and implementing systems that support real-world finance applications have been
challenging. In large part because they lack transparency and explainability
which are important factors in establishing reliable technology and the
research on this topic with a specific focus on applications in credit risk
management. In this paper, we implement two advanced post-hoc model agnostic
explainability techniques called Local Interpretable Model Agnostic
Explanations (LIME) and SHapley Additive exPlanations (SHAP) to machine
learning (ML)-based credit scoring models applied to the open-access data set
offered by the US-based P2P Lending Platform, Lending Club. Specifically, we
use LIME to explain instances locally and SHAP to get both local and global
explanations. We discuss the results in detail and present multiple comparison
scenarios by using various kernels available for explaining graphs generated
using SHAP values. We also discuss the practical challenges associated with the
implementation of these state-of-art eXplainabale AI (XAI) methods and document
them for future reference. We have made an effort to document every technical
aspect of this research, while at the same time providing a general summary of
the conclusions.
