At the heart of Comarch’s AML software is an analytical AI-based engine that processes data streams and detects money laundering activities. The scope of operations being monitored is broad and covers deposits, withdrawals, purchases, fund transfers, merchant credits, payments, trading activities, investments and social media activity. The engine leverages most recent supervised and unsupervised learning techniques to discover anomalies and improve the detection breadth. Additionally, it categorizes and scores suspicious transactions according to their probability of being a real money laundering attempt. Calculated score output serves as a reference to rank new attempts. The incidents going beyond certain score value are sent to analysts for further review while the ones remaining below the value can be discarded based on historical cases. Such alert prioritization greatly speeds up the work of AML teams.
Before the AI engine can correctly analyze bits of information and draw conclusions, it needs properly prepared data. The process of getting data ready for an anti-money laundering program requires several preparatory activities. The key step of data preparation is a transformation which typically involves scaling, decomposition and aggregation. This step is also referred to as feature engineering, and, if properly carried out, can be very beneficial to the performance of a final solution. Data preparation is a broad subject that can involve a lot of iterations, explorations and analyses. The data pre-processor module is responsible for integration with data sources existing in financial institutions’ databases and the correct introductory processing and transformation of data which will be subsequently analyzed in the AI engine.
The front office module provides tools for anti-money laundering analysts to manually review and analyze suspicious activity alerts. It supports users in analytical tasks allowing to manage the alert flow, which is important in the case of a multi-stage analysis process. The tool also comes with a visualization of statistics, detected anomalies and suspicious cases arranged by categories, in a clear and understandable way for the analyst. The front office user interface is designed based on the latest UX standards and can be delivered as a standalone application or easily integrated with the existing solutions, acting as an extension to them.
The money laundering process evolves over time, and new advanced fraud patterns appear, which makes it necessary to constantly monitor the solution’s performance. As new data arrives, the algorithms that were trained based on historical data may require periodical re-training (e.g. in the case of sudden increase in the number of false alarms). The monitoring module is responsible for gathering statistics, analyzing results and warning about any unusual performance loss.