Overview
Research addresses the challenge of limited rich semantic context in public financial fraud detection datasets, which often lack detailed semantics due to privacy constraints. While synthetic datasets incorporate generated semantics, they frequently compromise behavioral realism, and textual descriptions crucial for contextual reasoning remain scarce. A proposed multi-agent semantic enrichment framework generates interpretable financial semantics directly grounded in original transaction behavior. This framework simulates multimodal financial data by employing role-specialized agents and consistency refinement. Concurrently, a new multimodal financial fraud dataset, MS-FFSD, has been contributed. This dataset is enriched with both structured and textual semantics, and critically, it preserves the transaction behavior observed in real data. The utility and quality of this semantic enrichment have been systematically analyzed, demonstrating statistical fidelity and framework generalizability. Findings indicate that the integration of richer semantics enhances fraud modeling capabilities and improves context-aware large language model (LLM) reasoning. This work aims to advance multimodal financial fraud research and integrate emerging LLM and multi-agent capabilities with operational anti-fraud practices.
Research Context
Financial fraud detection relies significantly on rich semantic context to provide evidence for transaction behavior modeling and subsequent fraud reasoning. However, existing public real-world financial datasets face limitations in this regard. Privacy constraints typically restrict the inclusion of extensive semantic details. Consequently, synthetic datasets are often developed to introduce generated semantics, but this approach frequently results in a loss of behavioral realism compared to authentic transaction data. A notable scarcity of textual descriptions for contextual reasoning further complicates the development of robust financial fraud detection systems.
Approach
The research introduces a semantic enrichment framework designed to simulate multimodal financial data. This framework is grounded in original transaction behavior. The methodology encompasses two primary components:
- Multi-Agent Semantic Enrichment Framework: This framework is engineered to generate interpretable financial semantics. Its operation is grounded in original transaction behavior, utilizing role-specialized agents. A key feature of the framework is its consistency refinement mechanism, which ensures the coherence and accuracy of the generated semantics.
- Multimodal Financial Fraud Dataset (MS-FFSD) Contribution: As an outcome of this framework, a new dataset, MS-FFSD, has been developed. This dataset is characterized by its enrichment with both structured semantics and textual semantics. A critical design principle for MS-FFSD was to preserve the transaction behavior derived from real data, thereby mitigating the behavioral realism issues observed in other synthetic datasets.
Subsequent to the development, a systematic analysis was conducted to evaluate the quality and utility of the semantic enrichment process.
Findings
The systematic analysis of the semantic enrichment framework and the MS-FFSD dataset yielded several key findings:
- The framework demonstrated statistical fidelity, indicating that the generated semantics accurately reflect underlying statistical properties.
- Generalizability of the framework was observed, suggesting its potential applicability across different scenarios or datasets.
- Richer semantics, produced by the framework, were found to benefit fraud modeling processes.
- Context-aware Large Language Model (LLM) reasoning was also shown to improve with the integration of these richer semantics.
Why This Matters
This work advances multimodal financial fraud research by addressing a critical gap in semantically rich yet behaviorally realistic datasets. By bridging emerging LLM and multi-agent capabilities with operational anti-fraud practices, it offers a pathway to potentially more effective and interpretable financial fraud detection systems. The framework and dataset are publicly released at https://github.com/AI4Risk/MS-FFSD.