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:
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 the 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, an 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.
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, or investments. The engine leverages most recent supervised and unsupervised learning techniques to discover anomalies and improve the detection breadth.
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 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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