Financial Crime Analytics

Financial institutions process millions of transactions every day. Most are legitimate, but a small number involve fraud or money laundering. The goal of financial crime analytics is to find those cases accurately and efficiently. Missing them has legal and financial consequences. Flagging too many generates large volumes of false alerts that investigators have to review manually, which is costly.

The daily work involves building and maintaining models that score transactions or accounts based on their risk. You work with large datasets of historical transactions, labelled cases of known fraud or suspicious activity, and network data showing how accounts are connected. A large part of the job is evaluating model performance and tuning the balance between catching more cases and reducing false positives.

The main tools are Python and SQL. Machine learning methods such as gradient boosting and neural networks are widely used for transaction scoring. Graph analysis tools are used for network-based detection of money laundering structures. Regulatory knowledge is important, since models must meet requirements set by supervisors like DNB and the Financial Intelligence Unit.

The work sits within the Banking and FinTech sectors and connects closely to the Risk Manager and Data Scientist roles. At banks, financial crime teams are large and the work is structured around regulatory obligations. At FinTech companies, the teams are smaller and the focus is often more on fraud than on money laundering compliance.