Strategic Defense Against Financial Crime: A 3-Phase AI Approach

• Published date: 14 November 2025
• Subject Matter: Criminal Law, Technology, White Collar Crime, Anti-Corruption & Fraud, Crime, New Technology
• Law Firm: Ankura Consulting Group LLC
• Author: Amit Jaju and Ankush Lamba

Financial fraud is evolving faster than most organizations can respond. Understanding the implications of data in real-world contexts is far more important than simply analyzing it. In our experience, the issue is not the abundance of data, but the problem of understanding it in the context of fraud. Many artificial intelligence (AI) systems used to detect fraud are not poorly engineered; they simply fail to incorporate the critical context of the relationships between the data, behavior, and intent. Where traditional detection systems focus on fraud as a series of standalone, isolated transactions, modern systems fail to integrate the other behavioral, linguistic, and psychological patterns that older systems are designed to miss. In our view, the most critical gap in AI-enabled fraud detection is the lack of understanding and detection of aligned, coordinated, and collusive behavior. Large language models (LLM) trained with a combination of structured and unstructured data on social engineering, collusion, and hidden financial manipulation will provide fraud investigators with cutting-edge tools. The future of fraud detection is more than rapid detection ' it is the ability to provide actionable insight.

AI is now becoming an indispensable component of contemporary forensic investigations. It rapidly identifies and analyzes large and intricate datasets in order to identify suspicious behaviors that might escape the attention of human reviewers. Furthermore, advanced models not only pinpoint suspicious behavior; they can also articulate the reasoning behind the anomaly and the transparency needed for investigators and regulators. The problem, however, is that generic models are seldom sufficient. If an AI system is not customized to understand an organization's...