How AI Detects Financial Fraud: A Review of Emerging Deep Learning Methods

Financial fraud generates persistent risk and capital loss across sectors. This study investigates artificial intelligence (AI) methodologies for financial fraud detection, with emphasis on Retrieval-Augmented Generation (RAG). The review covers supervised classification, unsupervised anomaly detection, and graph-based relational modeling using deep neural networks, transformers, and hybrid architectures. Challenges include class imbalance, concept drift, and decision interpretability. We describe the RAG framework integrating retrievers and generative language models with external knowledge bases. Empirical comparisons on synthetic and real-time fraud datasets show improved F1-score, precision, and contextual reasoning in contrast to fine-tuned transformers and static classifiers. Applications include transaction monitoring, policy violation detection, account takeover analysis, and social engineering prevention. Evaluation highlights retrieval-grounded generation as an effective fraud signal augmentation mechanism. The paper concludes with architectural implications for deploying scalable, compliant, and adaptive fraud detection pipelines in multi-domain financial systems.