Predicting customer subscriptions to fixed-term deposit products based on machine learning approach

Abstract

In the contemporary dynamic financial milieu, financial institutions confront the exigency of comprehending and tailoring services to meet the idiosyncratic demands of individual customers, with a particular emphasis on forecasting fixed-term deposit commitments. The integration of machine learning proffers a robust framework to disentangle the intricacies inherent in customer decision-making processes. This investigation expounds upon a systematic framework encompassing data rectification, validation, and the process of feature curation, underscoring the imperative nature of a scrupulous and methodical approach. The exposition introduces an array of machine learning models, including XGBoost, Logistic Regression, Random Forest, Neural Networks, and Gaussian Naive Bayes, offering elucidation on their respective applications. Noteworthy attention is accorded to the Random Forest and Neural Networks models, with detailed explanations of their operational principles and strengths. The study underscores the criticality of conscientious data preprocessing, featuring a presentation of pertinent Python libraries and methodologies for data refinement, validation, and feature selection. The discourse culminates in a delineation of the potential of neural networks as a potent instrument in the domain of machine learning, affording insight into their intricate architecture and the iterative training process, whilst accentuating their versatility across diverse domains. In summation, this inquiry furnishes a comprehensive and pragmatic compendium on the utilization of machine learning methodologies for the prediction of customer subscriptions within the financial sector.