Innovative Machine Learning Solutions for Credit Classification of Commercial & Agricultural Vehicle Contracts

This investigation explores a variety of machine learning techniques, with the goal to refine the prediction of overdue payments in installment contracts associated with agricultural and commercial vehicles. The study focuses on a leading capital goods firm as a representative example. Motivated by the need for effective liquidity management, an assortment of machine learning-based models, encompassing Logistic Regression, Random Forest, XGBoost, and LightGBM, are built and evaluated using a comprehensive dataset to anticipate payment delays. The performance of each model is meticulously examined, and a feature importance investigation is carried out to pinpoint the key determinants. In a quest to boost prediction precision, ensemble methods, especially a voting classifier and an innovative merger of a neural network and a voting classifier (NN-VC), are utilized. The investigation affirms that the amalgamation of multiple machine learning algorithms significantly augments late payment forecasting, paving the way for improved risk management and insightful financial decision-making within the firm. Through the comparison of distinct models and the unveiling of a novel ensemble technique, the research offers a unique insight and sets the stage for future applications in risk management