Contributed by Comarch
Written by Tomasz Czech, Head of R&D, Comarch, Financial Services

Monitoring transactions for suspicious ones is a routine task requiring much knowledge and experience. But here’s where AI comes into play: it can significantly decrease the time and increase the precision of AML operations.

Up to $2 trillion dollars, representing 5% of global GDP, are laundered worldwide each year according to the United Nations Office on Drugs and Crime.

The fight against money laundering is one of top priorities of financial institutions – but it also poses a significant challenge for them. To combat the phenomenon, one needs to have a large number of human and technology resources at hand. And even then, the good guys have a hard time winning.

The deeper money gets into the financial system, the more difficult it is to identify its origin. One of the technologies that have gained recognition in recent years is deep machine learning, considered a subset of modern artificial intelligence, or AI.

Deep neural networks – algorithms that “simulate” the human brain can spot and understand relationships and similarities between data and, further down the road, learn to detect anomalies or classify and predict specific events, such as financial crimes.

Anomaly detection has the potential to identify events that do not conform to an expected pattern in a data set and improve the breadth of detection by uncovering new money laundering patterns. Once an anomaly is detected, it can get prioritized, which illustrates the probability of a real money laundering case. Cases with the highest probability can be analyzed first, which speeds up the whole process. The result is improved efficiency: fewer false alarms, time savings, and a better detection rate.

A successful implementation of such solution depends on high-quality data. The data scope covers various bank sources and publicly available data from the Internet or government databases. The more complete the image of customers and their activities, the greater the chance of detecting anomalous behaviors.

The other important aspect is AI expertise, which is obvious – but very few companies are experts in both machine learning techniques and financial services. Without interdisciplinary expert knowledge, building learning systems to detect certain types of financial fraud can be very tricky. Choosing the right technological partner is crucial.

Money laundering will not disappear overnight. But with time, deep learning can come to the rescue.

Author: Tomasz Czech, Head of R&D, Comarch , Financial Services
Tomasz.Czech@comarch.com
www.comarch.com

About Comarch
Comarch is a global powerhouse specialized in the design, implementation and integration of advanced IT services and software. With 25 years’ experience in the industry, Comarch is one of the leading software houses in Europe with over 6000 employees worldwide and more than 3000 successful projects carried out for the largest international brands.