Adoption of Machine Learning models in different applications in academia, medicine and even fields such as finance has skyrocketed over the years. As the adoption of these models is quickly entering into critical applications, attention needs to be paid to ensure that the models’ decisions can be explained and that they do not discriminate solely based on factors such as gender, religion and others. An important critical domain of this application is in finance particularly in loan classification where we do not want models to predict default based on biases. Since most ML models essentially function as black box models, this increases the chance that some of the models that get employed by corporations may be biased. Therefore to hold corporations accountable if biased decisions are made, it is necessary to explore the explainability of ML model in explainability and this is what our paper explores explainability through methods such as logistic rule regression model and CatBoost Model.