Hybrid Multi-Layer Fraud Detection Framework: A Real-Time Risk Control System Integrating BERT, BiLSTM, and Graph Neural Networks

This article introduces an innovative multi-layer fraud detection framework that integrates BERT text analysis, BiLSTM transaction modeling, and graph neural network risk scoring to achieve real-time detection of fraudulent information, fraudulent transactions, and money laundering accounts, with an accuracy rate of over 99%. The framework adopts a multi-modal fusion design and can handle complex fraudulent behaviors in digital finance.

With the rapid development of digital finance, fraudulent behaviors are becoming increasingly complex. Traditional rule-based detection systems struggle to cope with the diversity and concealment of modern fraud methods. Fraudsters use social media, instant messaging tools, and complex financial networks to launch multi-dimensional attacks, forming a black industry chain. Financial institutions urgently need an intelligent detection system that can simultaneously process text, transaction data, and relationship networks.

The framework adopts a multi-modal fusion design concept and integrates three deep learning technologies:

1. BERT Text Analysis Module: Understand the semantic information of fraudulent SMS messages and emails, capture context-dependent relationships to identify fraudulent intent;
2. BiLSTM Transaction Modeling Module: Analyze forward and backward information of transaction sequences, establish a baseline of users' normal behavior, and detect abnormal transactions;
3. Graph Neural Network Risk Scoring Module: Construct an entity relationship graph, analyze connection patterns and capital flows, and identify money laundering networks and fraud gangs.

Multi-modal Feature Fusion

Adopt an adaptive weighted fusion mechanism to dynamically adjust the weights of each layer according to the scenario (e.g., increase BERT weight in SMS fraud cases).

Real-Time Inference Optimization

Compress BERT through knowledge distillation, accelerate BiLSTM with batch processing, and reduce GNN complexity via neighbor sampling to achieve millisecond-level detection.

Continuous Learning and Update

Integrate an online learning mechanism to automatically update the model, and an active learning module to request manual annotation of low-confidence results, forming a positive feedback loop.

This framework has been deployed in scenarios such as banks, payment platforms, and e-commerce: intercepting telecom fraud, identifying money laundering cases, and curbing fake transactions. Experimental data shows that the comprehensive accuracy rate is over 99%, the false positive rate is controlled below 0.1%, and it saves a lot of risk control labor costs.

Future optimization directions:

1. Introduce more data sources such as device fingerprints and biometrics to build comprehensive user profiles;
2. Explore federated learning to realize risk control collaboration across institutions under privacy protection;
3. Integrate large language models like GPT to improve semantic understanding and adversarial sample recognition capabilities.