Leakage Safe Graph Features for Interpretable Fraud Detection in Temporal Transaction Networks

TL;DR

This paper introduces a leakage safe, causal graph feature extraction method for temporal transaction networks, enhancing interpretability and risk analysis in fraud detection while avoiding look ahead bias.

Contribution

It proposes a novel, leakage safe protocol for extracting interpretable graph features from temporal networks for fraud detection.

Findings

Causal graph features improve interpretability in fraud detection.

Random Forest classifier achieved ROC-AUC of 0.85.

Calibrated models provide better probability reliability.

Abstract

Illicit transaction detection is often driven by transaction level attributes however, fraudulent behavior may also manifest through network structure such as central hubs, high flow intermediaries, and coordinated neighborhoods. This paper presents a time respecting, leakage safe (causal) graph feature extraction protocol for temporal transaction networks and evaluates its utility for illicit entity classification. Using the Elliptic dataset, we construct directed transaction graphs and compute interpretable structural descriptors, including degree statistics, PageRank, HITS hub or authority scores, k-core indices, and neighborhood reachability measures. To prevent look ahead bias, we additionally compute causal variants of graph features using only edges observed up to each timestep. A Random Forest classifier trained with strict temporal splits achieves strong discrimination on a held out future test period (ROC-AUC about 0.85, Average Precision about 0.54). Although transaction attributes remain the dominant predictive signal, graph derived features provide complementary interpretability and enable risk context analysis for investigation workflows. We further assess operational utility using Precision at k and evaluate probability reliability via calibration curves and Brier scores, showing that calibrated models yield better aligned probabilities for triage. Overall, the results support causal graph feature extraction as a practical and interpretable augmentation for temporal fraud detection pipelines.