ANTI MONEY LAUNDERING
Monitor transactions with unparalleled speed and precision
Anti-Money Laundering software
Anti-Money Laundering is a fraud detection software dedicated to financial institutions obligated to monitor, investigate, and report- transactions of a suspicious or unusual nature to financial investigation units. It optimizes the existing anti-money laundering processes by significantly enhancing the effectiveness of most commonly used – and inefficient – rule-based approaches, characterized by high false-positive rates and the nability to consider complex interdependencies between various activities carried out to launder money.
Here are the most important results we have noted while cooperating with banks:
Key business advantages
Fewer false alarms, more insights
Dots connected faster and better than any human can
Cost reduction
Greater efficiency of AML departments = operating costs going down
Risk-based approach
Measures to prevent money laundering always corresponding with the risks identified by your institution
No need to replace your AML system
Quick and easy integration with banking systems
State-of-the-art technology
AI-based, self-improving solution that never goes out of date
Prioritize tasks and get a head start thanks to SARs
Terminate bogus bank accounts faster
How it works
- 1. Rule-based systems produce alerts which are categorized by analysts. These alerts form a data set which is used for training a Risk Ranking algorithm.
- 2. After the training concludes, the Risk Ranking algorithm is able to score new, never-seen-before data by the level of money laundering risk posed.
- 3. During the prediction phase, each alert gets a score assigned. Alerts below specific low risk threshold can be discarded or hibernated, while those going beyond the threshold are sent to analysts for further review.
- 4. To extend the rule-based system, the Anomaly Detection system is introduced to analyze all transactions and spot suspicious activities. It then creates additional alerts and cases for review, which reduces the risk of overlooking anything important.
- 5. The Risk Ranking algorithm is able to prioritize analysts’ work. Combined with the Anomaly Detection module, it increases the speed and precision of transaction monitoring.
Enhanced detection process with Comarch AML software
AML software analytical engine
At the heart of Comarch’s AML software is an analytical Artificial Intelligence (AI) 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, and investments. The engine leverages most recent supervised and unsupervised learning techniques to discover anomalies and improve the detection breadth.
Data pre-processing
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 the 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.
Performance monitoring
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.
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