AI Powered Credit Scoring and Fraud Detection Models for Financial Technology Applications

Abstract

The ever-increasing complexity of financial technology (FinTech) ecosystems calls for sophisticated and real-time solutions for assessing credit ratings and identifying fraud. The foundation, traditional rule-based and statistical models, do not perform well regarding adaptiveness, sensitivity to a multitude of patterns, and effectiveness within noisy or unbalanced datasets. This paper proposes an integrated AI-based approach to credit scoring and fraud detection designed for FinTech systems using classification algorithms, feature engineering, and evaluation metrics. These models are developed with advanced classification algorithms and trained on diverse transactional and demographic datasets to enable accurate predictions in credit risk and anomalies detection. With the use of ensemble learning, deep neural networks, and hybrid models, extensive experiments confirm the incorporation of these models improves precision, recall, and area under the curve (AUC) compared with classical methods. Furthermore, the study analyzes feature importance, model explainability, and performance changes over different customer segments to provide useful recommendations for financial service providers. AI-based decision engines can offer instantaneous, accurate, and comprehensible risk evaluations in FinTech, proving their usefulness in practice.