Comparative Evaluation of AI Models for Financial Decision-Making: Evidence from Credit, Fraud, and Stock Forecasting

This research sets out to compare statistical methods, machine learning, and deep learning models across three critical financial domains: credit scoring, fraud detection, and stock price forecasting. The goal is to weigh their predictive accuracy against other vital factors like interpretability, computational efficiency, and regulatory fit, ultimately helping practitioners choose the right AI approach for specific decision-making contexts.