From Detection to Investigation: A Survey of Foundation Models and Agents for Financial Fraud and Financial Crime

Source abstract

Foundation models are extending research on financial fraud and financial crime from isolated risk detection to document understanding, relational analysis, and investigative workflows. A gap nevertheless separates model outputs from candidate evidence and actionable conclusions. This survey examines advances in six families of foundation models: language, vision, speech, temporal, graph, and tabular models. It compares their roles and limitations along two dimensions: the risk-control lifecycle and the directness of the supporting evidence. Existing studies show that pretraining can support representation reuse and the processing of unstructured information, with direct validation in some financial tasks. Cross-modal identity binding, information availability over time, report faithfulness, and case-level benefits require separate evaluation. Evidence on investigative agents spans prototypes, controlled tasks, expert assessments, and industry disclosures, which establish different aspects of workflow feasibility, task performance, and user assessment. Establishing gains in investigative efficiency and reductions in financial losses requires case-level comparative evidence. We examine entity and temporal alignment, the organization of candidate evidence, constrained tool execution, and investigative benchmark design, together with the implications of adversarial threats, privacy, fairness, and model risk governance. Research priorities should shift from expanding system functionality toward factual traceability, sufficient evidence for decisions, and reviewable actions.