Multimodal Fraud Detection System: A Comprehensive Solution Integrating XGBoost, NLP, and Graph Neural Networks

This project was published by aditya-ailsinghani on GitHub on June 9, 2026 (original link: https://github.com/aditya-ailsinghani/Multimodal-Fraud-Detection). Its core is a multimodal fraud detection solution integrating XGBoost, natural language processing (NLP), and graph neural networks. Validated on 590,000 transaction data entries, it achieves an ROC-AUC of 0.9375 and an 82% fraud recall rate, providing a comprehensive solution for identifying complex fraud patterns.

Financial fraud detection is a core challenge in the risk control field. Traditional single-dimensional detection methods struggle to handle complex fraud techniques. Modern fraud involves multi-dimensional information such as transaction amounts, time patterns, device fingerprints, and email content. How to effectively integrate heterogeneous data sources and build a detection system that balances explicit rules and implicit correlations is a topic of common concern in industry and academia.

XGBoost Base Model

As the cornerstone of the system, it processes traditional structured features, automatically learns non-linear interactions, and provides feature importance analysis.

NLP Text Analysis Layer

It parses transaction-related texts (such as emails and device descriptions), converts them into vectors via text embedding, and mines hidden fraud signals.

Graph Analysis Network Layer

It builds a user-device-transaction-location heterogeneous graph based on NetworkX. Graph neural networks learn high-order neighborhood information of nodes to identify gang-related fraud patterns.

LightGBM is used as the fusion layer framework. Instead of simply concatenating multimodal features, it uses a carefully designed late fusion strategy to preserve the independent expression ability of each modality, achieve end-to-end optimization, and allow different modal features to complement each other at the decision-making level.

On the test set of 590,000 transaction data entries, the fusion model achieved excellent results:

• ROC-AUC: 0.9375 (excellent discriminative ability)
• Fraud recall rate: 82% (identifies most real fraud transactions) These metrics are highly competitive in scenarios with extremely imbalanced fraud samples, and high recall rate is crucial for business value.

The project's architecture design is transferable, and the multimodal fusion approach can be applied to:

• E-commerce platform risk control systems
• Bank credit card anti-fraud
• Insurance claim review
• Real-time risk control for payment platforms

Multimodal-Fraud-Detection demonstrates the application potential of modern machine learning in the risk control field. By integrating three technical routes—XGBoost, NLP, and graph analysis—it effectively identifies complex fraud patterns, making it a highly valuable learning case for risk control algorithm researchers and application developers.