  This paper examines two different yet related questions related to
explainable AI (XAI) practices. Machine learning (ML) is increasingly important
in financial services, such as pre-approval, credit underwriting, investments,
and various front-end and back-end activities. Machine Learning can
automatically detect non-linearities and interactions in training data,
facilitating faster and more accurate credit decisions. However, machine
learning models are opaque and hard to explain, which are critical elements
needed for establishing a reliable technology. The study compares various
machine learning models, including single classifiers (logistic regression,
decision trees, LDA, QDA), heterogeneous ensembles (AdaBoost, Random Forest),
and sequential neural networks. The results indicate that ensemble classifiers
and neural networks outperform. In addition, two advanced post-hoc model
agnostic explainability techniques - LIME and SHAP are utilized to assess
ML-based credit scoring models using the open-access datasets offered by
US-based P2P Lending Platform, Lending Club. For this study, we are also using
machine learning algorithms to develop new investment models and explore
portfolio strategies that can maximize profitability while minimizing risk.
