Hybrid neuro-symbolic graph explanation framework for fraud detection in financial transaction networks

Counts differ because each database indexes a different set of publications. We treat OpenAlex as the canonical count; Google Scholar is not shown (no API, and crawling it violates its ToS).

Introduction This research focuses on the development of a hybrid neuro-symbolic system for fraud detection in rapidly expanding digital financial systems. Methods A bipartite customer-merchant graph is constructed from the Nigerian Financial Transactions dataset, and a 3-layer Graph Attention Network (FraudGAT) is used to model structural dependencies while a symbolic reasoning layer and constrained LLM prompting generate auditor-readable explanations. Results On the curated evaluation split, FraudGAT achieves AUC 0.874, AUPRC 0.771, and F1 0.609. We additionally benchmark against GCN, GraphSAGE, GIN, a graphtransformer baseline, and tabular baselines (Random Forest, XGBoost, LightGBM): tabular ensembles perform best on this sampled split, while FraudGAT remains stronger than several graph baselines and supports an interpretable detect-explain workflow. Discussions Therefore, we position the contribution as an auditable neuro-symbolic graph framework and an empirical foundation for scaling to larger, richer transaction graphs.