# Graph-Based Financial Fraud Detection with Calibrated Risk Scoring and Structural Regularization

arXiv: https://arxiv.org/abs/2605.12782 · HTML: https://mlsanity.com/paper/2605.12782/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 18 references that are in the map.
- First posted on arXiv 12 May 2026.

## Builds on (most cited first, 4 of 4)

- [Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsters](https://mlsanity.com/paper/2008.08692/) (2020)
- [CaT-GNN: Enhancing Credit Card Fraud Detection via Causal Temporal Graph Neural Networks](https://mlsanity.com/paper/2402.14708/) (2024)
- [Dynamic Relation-Attentive Graph Neural Networks for Fraud Detection](https://mlsanity.com/paper/2310.04171/) (2023)
- [Transaction Fraud Detection via Spatial-Temporal-Aware Graph Transformer](https://mlsanity.com/paper/2307.05121/) (2023)

## Abstract (arXiv)

Financial transaction fraud prevention faces challenges such as complex relationship structures, concealed behavioral patterns, and dynamically changing data distribution. Discrimination models relying solely on independent sample features are insufficient to fully characterize the risks of group collaboration and chain transfers within transaction networks. This paper proposes a graph neural network representation learning and risk discrimination framework for financial transaction fraud prevention. It integrates transaction records and identity information into node attributes and constructs a transaction graph based on shared attributes and interaction consistency to explicitly model inter-transaction relationships. In model design, a multi-layer message passing mechanism is employed to aggregate neighborhood information, learn node embedding representations containing structural context semantics, and output transaction-level fraud probability and risk scores through a lightweight risk discrimination head. A weighted supervision objective is introduced to mitigate training bias caused by class imbalance, and structural consistency regularization constraints are combined to suppress the impact of noisy edges on representation drift, thereby improving the stability and usability of risk characterization. Experiments are conducted on a publicly available financial transaction dataset, comparing various methods in the same direction and comprehensively evaluating them under a unified evaluation protocol. The results show that the proposed method outperforms other methods in risk ranking and probability calibration quality, validating the effectiveness of graph structure modeling and representation learning collaboration in financial transaction fraud prevention.