RGH-GNN : Risk-Gated Heterogeneous Graph Neural Network for Financial Fraud Detection

Mythreyee Hari; Subin Sahayam; Vegesna S M Srinivasavarma

cs.LGOct 19, 2026

RGH-GNN : Risk-Gated Heterogeneous Graph Neural Network for Financial Fraud Detection

Authors: Mythreyee Hari, Subin Sahayam, Vegesna S M Srinivasavarma

Organizations: Department of Computer Science and Engineering, Shiv Nadar University, Chennai - 603110, Tamil Nadu, India

Abstract

Credit card fraud is a significant problem for financial institutions, resulting in substantial monetary loss each year. Fraud patterns and methods are continuously evolving, with fraudulent transactions often disguised to resemble legitimate activity. Real world transaction data for fraud detection is severely affected by class imbalance making it harder to detect these transactions. Fraudulent transactions are rarely isolated. Accounts, devices, and other common attributes are often shared across fraudulent activity, revealing relational patterns that individual transaction features alone cannot capture. Graph-based approaches model these relationships explicitly. However, standard graph neural networks aggregate neighbourhood information uniformly, which can dilute the already scarce signal carried by fraudulent transactions and limit detection of camouflaged fraud. This work presents RGH-GNN, a heterogeneous graph neural network that addresses this limitation by incorporating transaction-level risk scores, generated by a supervised LightGBM classifier as edge weights. It is used to guide message passing through a learnable risk-gated aggregation mechanism. It ensures that high-risk transactions contribute more strongly to learned representations rather than being diluted by uniform neighbourhood aggregation. RGH-GNN is evaluated on the IEEE-CIS fraud detection dataset against standard graph neural network models that are trained on the same heterogeneous graph. RGH-GNN outperforms all baselines across every evaluation metric, with an AUC-ROC score of 0.914. This demonstrates that incorporating engineered risk signals into graph-based message passing substantially improves fraud detection performance.

Figures & tables

Figure 1: Methodology Diagram RGH-GNN Pipeline

Figure 2: A subgraph sampled over 200 transactions. Account nodes (blue) are connected to clean transactions (green) and fraudulent transactions (red) using account-transaction edges. Each edge depicts a transaction made using the account it is connected to.

Figure 3: Distribution of LightGBM risk scores by class across all 590,540 transactions. Non-fraud transactions (blue) concentrate near zero (mean score 0.131) while fraud transactions (orange) accumulate near 1.0 (mean score 0.793), a margin of 0.662 between classes, with the optimal classification threshold $\tau=0.75$ separating the two populations.

Figure 4: Forward-pass architecture of the proposed RiskCONV layer

$\tau$	$g_{a}$	F1	$\tau$	$g_{a}$	F1	$\tau$	$g_{a}$	F1
0.1	1.0	0.6318	0.2	20.0	0.6355	0.4	10.0	0.6315
0.1	5.0	0.6360	0.3	1.0	0.6331	0.4	15.0	0.6376
0.1	10.0	0.6352	0.3	5.0	0.6340	0.4	20.0	0.6336
0.1	15.0	0.6359	0.3	10.0	0.6359	0.5	1.0	0.6313
0.1	20.0	0.6303	0.3	15.0	0.6373	0.5	5.0	0.6340
0.2	1.0	0.6378	0.3	20.0	0.6358	0.5	10.0	0.6315

Table 1: Grid search results for

\tau

and

g_{a}

, reporting validation F1 score. The best configuration (

\tau=0.4

g_{a}=5.0

) is shown in bold.

$w^{+}$	1	1.5	2	3	5
F1	0.6286	0.6311	0.6203	0.6250	0.6160

Table 2: Validation F1 score across the pos_weight (

w^{+}

) sweep. The best configuration (

w^{+}=1.5

) is shown in bold.

Model	AUC-ROC	AUC-PR	F1	Precision	Recall
RGH-GNN (Full)	$\mathbf{0.914\pm 0.001}$	$\mathbf{0.631\pm 0.002}$	$\mathbf{0.619\pm 0.002}$	$\mathbf{0.734\pm 0.014}$	$\mathbf{0.536\pm 0.009}$
No Gate Aggregation	$0.912\pm 0.001$	$0.624\pm 0.003$	$0.616\pm 0.003$	$0.726\pm 0.016$	$0.535\pm 0.009$
No Risk Scores	$0.885\pm 0.003$	$0.399\pm 0.017$	$0.414\pm 0.010$	$0.400\pm 0.029$	$0.432\pm 0.023$
No Heterogeneity	$0.885\pm 0.002$	$0.407\pm 0.013$	$0.420\pm 0.006$	$0.396\pm 0.010$	$0.448\pm 0.023$
Weighted GraphSAGE	$0.879\pm 0.003$	$0.401\pm 0.019$	$0.418\pm 0.015$	$0.451\pm 0.025$	$0.392\pm 0.035$
Unweighted GraphSAGE	$0.884\pm 0.001$	$0.387\pm 0.011$	$0.405\pm 0.008$	$0.375\pm 0.036$	$0.445\pm 0.035$

Table 3: Ablation study results on the IEEE-CIS fraud detection test set.

Model	Risk Scores	Heterogeneity	Gated Aggregation
RGH-GNN (Full)	✓	✓	✓
No Gated Aggregation	✓	✓	✗
No Risk Scores	✗	✓	✓
No Heterogeneity	✓	✗	✓
Weighted GraphSAGE	✓	✗	✗
Unweighted GraphSAGE	✗	✗	✗

Table 4: Component presence across ablation configurations.

Model	AUC-ROC	AUC-PR	F1	Precision	Recall
Unweighted GraphSAGE [ 15 ]	$0.884\pm 0.001$	$0.387\pm 0.011$	$0.405\pm 0.008$	$0.375\pm 0.036$	$0.445\pm 0.035$
Weighted GraphSAGE [ 15 ]	$0.879\pm 0.003$	$0.401\pm 0.019$	$0.418\pm 0.015$	$0.451\pm 0.025$	$0.392\pm 0.035$
Unweighted GIN [ 39 ]	$0.734\pm 0.002$	$0.146\pm 0.009$	$0.242\pm 0.011$	$0.182\pm 0.017$	$0.368\pm 0.026$
Weighted GIN [ 39 ]	$0.642\pm 0.031$	$0.073\pm 0.004$	$0.186\pm 0.008$	$0.130\pm 0.010$	$0.332\pm 0.021$
Unweighted GAT [ 34 ]	$0.862\pm 0.003$	$0.319\pm 0.009$	$0.363\pm 0.004$	$0.363\pm 0.036$	$0.367\pm 0.030$
Weighted GAT [ 34 ]	$0.865\pm 0.004$	$0.331\pm 0.020$	$0.372\pm 0.016$	$0.371\pm 0.024$	$0.374\pm 0.015$

Table 5: Performance comparison of baseline models against RGH-GNN on the IEEE-CIS fraud detection test set. The best performance is highlighted in bold and the second-best is underlined.

Figure 5: GNN output probability distribution for fraud transactions on the held-out test set. The dashed line marks the classification threshold at 0.94. The majority of fraud transactions accumulate near 1.0 and are correctly classified, while a cluster near 0 represents hard negatives (transactions the model confidently assigned low fraud probability despite being genuinely fraudulent.)

Financial Fraud Detection

Financial fraud detection identifies suspicious transactions, accounts, claims, and behavior to prevent losses and guide investigation.

See all 135 papers

Graph-Based Financial Fraud Detection with Calibrated Risk Scoring and Structural Regularization
May 12, 2026Yunfei Nie, Jiawei Wang, Ruobing Yan +3Graph Neural Networks Graph Representation Learning
Brandeis University, Waltham, USA · University of California, Los Angeles, USA · Georgetown University, Washington, D.C., USA +2
Temporal Heterogeneous Graph Transformer for Credit Card Fraud Detection
Sep 7, 2026Qinwen YanFinancial Fraud Detection Transformer
University of California, Los Angeles Los Angeles, California, USA
Graph-Based Fraud Detection with Dual-Path Graph Filtering
Apr 14, 2026Wei He, Wensheng Gan, Philip S. YuFinancial Fraud Detection Graph Representation Learning
School of Intelligent Systems Science and Engineering, Jinan University, Zhuhai 519070, China · Department of Computer Science, University of Illinois Chicago, Chicago 60607, USA

May 12, 2026J/K move · Enter open · S save

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

Yunfei Nie, Jiawei Wang, Ruobing Yan, Yuhan Wang+2

Brandeis University, Waltham, USA · University of California, Los Angeles, USA · Georgetown University, Washington, D.C., USA+2

Graph Neural Networks Graph Representation Learning Financial Fraud Detection

Open paper AskPDF 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.

Abstract

Financial Fraud Detection

Explore similar work

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

Temporal Heterogeneous Graph Transformer for Credit Card Fraud Detection

Graph-Based Fraud Detection with Dual-Path Graph Filtering