Graph Based Fraud Detection Dataset for Financial Transaction Networks
Description
This dataset accompanies the research study on graph-based fraud detection using Graph Neural Networks (GNNs). It provides a structured representation of financial transaction activity as a graph, where entities and transactions are represented as nodes and relationships between entities are represented as edges. The dataset is intended to support reproducible research in financial fraud detection, graph machine learning, anomaly detection, and Graph Neural Network-based classification. It contains the data structures and experimental outputs required to reproduce the graph construction and evaluate fraud detection models. The dataset includes transaction-level and graph-level information, processed node and edge representations, fraud labels, and experimental results from GraphSAGE and conventional machine-learning baseline models including Random Forest and XGBoost. To protect privacy and confidentiality, the dataset contains anonymized and/or synthetic representations of transaction activity and does not contain personally identifiable information. The accompanying research project implements graph-based fraud detection using GraphSAGE and compares its performance with conventional machine-learning approaches. The dataset is intended for academic research, benchmarking, methodological comparison, and reproducibility of the associated study.
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Steps to reproduce
Recently I was not able to detect a fraud in my organization where fraudulent activity was done very smartly without violating any business rule, our fraud system was not capable enough to highlight it since our system was reading the transactions independently rather than clustering them, which is particularly important for identifying coordinated fraud and potential fraud rings, where several apparently independent transactions may form a connected behavioral pattern. This led me to work on graph fraud AI detection. Today I applied graph methodology by providing a VAS purchase report, Credit card transactions and Monthly Eshop Device sales report to the function code I wrote and pushed it to GitHub. The results were astonishing.