Anti-Money Laundering (AML)
Prevent Obscure and Indirect Money Transfers and Transactions
Money laundering is the illegal process of hiding the origins of money obtained criminally. This form of illicit activity entails the concealment of the source and origins of illegally acquired funds, usually conducted through money transfers with foreign banks or legitimate businesses. Gurucul Fraud Analytics provides a comprehensive and robust platform for the detection and prevention of cyber frauds involving cross-channel anomalous activities and patterns.
The Gurucul Anti-Money Laundering solution has pre-packaged machine learning models tuned to identify patterns of placement, layering and integration. This would include abnormal prices and/or suspicious quantities of product or services being sold to a customer. Such early detection of any change in the behavior due to risky transactions or patterns, enable organizations to investigate and respond to such fraudulent activities before any significant brand damage or financial loss.
The Gurucul Anti-Money Laundering solution provides:
- A range of link analysis algorithms to associate transactions across channels and multiple accounts to an identity like customer ID or user
- 360 degree visibility into all accounts, instruments and transactions performed by the user
- Advanced machine learning algorithms to build a cross-channel behavior profile and detect any deviation based on real-time activities like abnormal SWIFT transfers, excessive funds transfers, unusual debit card usage and card not present transactions
With the range of different and disparate channels of funds transfer such as online and debit channels, it is extremely critical to look at cross-channel fraud patterns to detect and prevent frauds.
Enterprise fraud management platforms have been around for years, but many legacy platforms lack the capabilities to make critical data associations and identify anomalous behaviors of user accounts. However, recent advancements in a range of technologies from Big Data to machine learning have coalesced to help build a new kind of advanced fraud analytics product.
Gurucul Fraud Analytics uses machine learning to analyze millions of datapoints from a variety of siloed, cross-channel sources, such as a core banking system (CBS) and the SWIFT system. By linking data from these disparate systems in a Big Data system, anomalous behavior can be identified quickly. For example, it is not normal procedure for payments made from the CBS to have no corresponding activities reported as required by SWIFT. Gurucul’s Anti-Money Laundering solution powered by Fraud Analytics would catch such a mismatch of activities in real-time and raise a high priority alert to prompt immediate investigation by the bank.
Banks are highly regulated financial institutions. They all have a fiduciary responsibility to protect their depositors’ and investors’ interests and assets. An advanced fraud analytics product is a necessity to accurately detect fraud in real time and to have the opportunity to disrupt the scheme and prevent the loss.