Predictive Analysis, Explainable AI, and Fairness Auditing: How Digital Transformation Reshapes Bank Models Using the Bank Marketing Dataset

The current research delves into the effects of predictive modeling and explainable artificial intelligence (XAI) as a transformation agent in banking decision making, emphasizing the aspects of fairness and transparency. The Bank Marketing Dataset, acquired from UCI Machine Learning Repository, is the basis of the development of predictive models for the purpose of forecasting term deposit subscriptions. We have not only performed a comparison of linear, tree-based and ensemble methods but also utilized SHAP (SHapley Additive exPlanations) for the interpretation of model predictions. Moreover, a fairness audit has taken place among the demographic groups so as to pinpoint any biases that may be present. Among the results is the discovery that ensemble models, with XGBoost being particularly singled out, have the highest accuracy in prediction; conversely, XAI tools have been the ones that have provided insulin through the insights on feature contributions. The fairness analysis has uncoved the aggregation of model outcomes disparity in relation to age, job, and marital status groups. This is where the exemplification of the digital transformation potential comes in as the banking industry would be able to not only enhance its predictability but also expertly control ethical dilemmas using technological means.