Artificial Intelligence in Financial Fraud Detection: A Deep Learning Perspective

Abstract

Financial fraudsters have increasingly taken advantage of digital financial services. Conventional rule-based and classical machine learning systems are not able to model the complex temporal, behavioral and relational patterns over the heterogeneous transaction data. In this paper, a deep learning–oriented study on real-world financial fraud detection schemes is conducted, integrating sequential models, convolutional structures, graph neural networks (GNNs), and hybrid ensembles to address the issue. Methods considered are temporal models (LSTM, GRU), attention-based transformers, CNN feature extractors for engineered transaction embeddings, GNNs for relational interactions (accounts, devices, merchants) and hybrid-pipeline using deep learners as well as gradient-boosted trees with XAI post-hoc explainers. Our complete end-to-end experimental pipeline includes four stages: robust preprocessing and anonymized features (2) class-imbalanced handling with combined sampling and loss-based strategies (focal-loss, class-weighted); (3) temporal plus graph model with a temporal-GNN encoder-decoder architecture, and (4) explainability using SHAP and counterfactual probe modules. Experiments on benchmark and proprietary-style datasets show that the proposed temporal-GNN hybrid achieves an average AUC-ROC of 0.976, precision at top-1% alerting of 0.42, and recall of 0.88, outperforming AUC by4-6% and recall by8-12% with competitive LSTM and LightGBM baselines. The method also decreases false-positive volume (alerts that need to be manually reviewed) by ~18% compared to gradient-boosted baselines. The research highlights deep learning’s potential to reveal multi-dimensional fraud epitasis alongside graph modelling and robust operational checks and controls.