Comarch 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 unable 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:
Money laundering detection optimization with Comarch Anti-Money Laundering
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.
In response to market trends and real business and technology issues in the compliance area, Comarch has created an AI-based anti-money laundering software (Comarch Anti-Money Laundering) that significantly increases the efficiency of suspicious transaction detection compared to rule-based methods.
Comarch Anti-Money Laundering is based on machine learning which is a state-of-the-art approach where Artificial Intelligence (AI) is used for creating models that, based on historical data, can determine the occurrence of certain events with high accuracy.
The ability to learn from past incidents is one of the most important features of Comarch’s fraud detection software. It can spot and understand relationships and similarities between data and, further down the road, learn to detect anomalies or classify and predict specific events which are commonly missed by human due to complexity and obviousness. The system’s algorithms are able to learn with or without supervision.
A rapidly growing number of new techniques and methods of money laundering is one of the reasons of higher false-negative rates. That is where Comarch Anti-Money Laundering can help by applying anomaly detection, which has the potential to identify events which do not conform to an expected pattern in a dataset, and improve the breadth of the said detection by uncovering new money laundering patterns.
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