Forensic Crime Analytics

The goal of forensic crime analytics is to reconstruct what happened in a financial dispute, fraud case, or regulatory investigation. Unlike ongoing monitoring, forensic projects are triggered by a specific event. The output is a set of findings that supports a legal case, regulatory decision, or settlement.

The work involves collecting and reconstructing data from multiple sources, which may be incomplete or inconsistent. Common tasks include tracing fund flows through accounts, identifying transactions outside expected patterns, and quantifying financial losses. Findings must be documented clearly enough to withstand scrutiny in a legal or regulatory setting.

Python and SQL are used for data extraction and analysis. Statistical methods help identify anomalies or test hypotheses about what happened. Network analysis is used when multiple connected entities are involved, such as a group of linked accounts or shell companies. Visualisation tools are used to communicate findings to lawyers, regulators, or executives.

This type of work appears at forensic consulting firms, law firms with disputes practices, and financial regulators such as the AFM and DNB. It also appears in internal audit and compliance functions at large companies. The project connects to Financial Crime Analytics, Data Analyst, and Risk Manager roles.