Machine Learning and Artificial Intelligence in Credit Scoring and Fraud Detection: A Review of Opportunities and Challenges

This article provides a review of machine learning (ML) and artificial intelligence (AI) applications in two of the most critical areas of modern finance: credit scoring and fraud detection. Traditional statistical methods, such as logistic regression and rule-based anomaly detection, remain widely used but face significant limitations in capturing the complexity of today’s financial data. Recent advances in ML and AI, including ensemble learning, deep neural networks, and transformer-based large language models (LLMs), offer improved predictive accuracy and adaptability.

The study highlights empirical findings from the literature, such as the benchmarking of classification algorithms for credit scoring (Lessmann et al., 2015) and the application of LSTMs to financial time series (Fischer & Krauss, 2018). It also discusses recent innovations such as FinBERT (Araci, 2019) and transformer-based approaches (Sun et al., 2024), which demonstrate how unstructured text and sequential transaction data can be integrated into financial risk systems.

The article concludes by addressing key challenges including interpretability, fairness, regulatory compliance, and adversarial adaptation in fraud detection. Future directions point toward explainable AI (XAI), hybrid modeling approaches, and the integration of LLMs for more holistic and resilient financial risk management.