The issue of money laundering is a critical and rising concern worldwide and is violating the financial services system in many countries. United Nations estimated that between 2 and 5 percent of global GDP or $1.6 to $4 trillion is laundered every year globally. Thus, it is imperative for countries and financial institutions to have Anti-Money Laundering systems in place to combat these malpractices.

An effective Anti-Money Laundering (AML) system sets up rules, regulations, and procedures to prevent the disguising of illegal sources of large funds generated by criminal activities such as corruption, tax evasion, drug peddling, human trafficking etc. The laundered money is usually processed through various legitimate banking channels in rapid succession in order to hide the original source of information.

Many financial institutions use AML tools that are mostly based on rule engines where alerts are generated against a transaction whenever it violates any of the pre-defined rules. Alerts raised by such a tool then go through rigorous investigation to decide if the transaction should be prevented and/or reported to the authorities. Recent developments in technology and innovation have enabled AI & ML technologies to be leveraged in the AML space to contribute to the effectiveness of the traditional AML tools as well as prevent such crimes on time.  

ABG has a dedicated team of professionals working in the fields of AML, AI & ML and provides services in the following spaces:

• Reduction of false positive alerts: An AML rule engine usually proves to be inefficient as it generates a high number of false positive alerts. This results in financial institutions incurring additional costs along with the investment of time to conduct investigations. Thus, one major area of concern as well as improvement in the field of AML is to find possible ways of reducing false positive alerts. Software Integrators powered by ML have come up with adequate solutions to combat the issue of excessive false positive alerts. Using the technique of supervised and unsupervised analysis of past and present customer information and behavior, data scientists have designed an array of ML approaches to limit the number of false positive alerts.
• Detecting changes in customer behavior: AML rule engines are usually reactive and raise alert(s) when a particular customer transaction violates a rule and whenever a suspicious incident takes place. ML can be utilized to set up a monitoring system to detect any significant deviation in the behavior of the customers so that the necessary investigations can be conducted in advance.
• Analysis of external and unstructured data: In order to implement a risk-based approach for AML and KYC (know your customer), financial institutions need to consider and explore a vast volume of external data covering areas such as the professional, social, and political profiles of the customers. Such information can be obtained from data sources including traditional and social media, news archives, blogs and other open-source data.  AI and ML helps financial institutes to acquire, sort out and extract relevant information and analyze the customer data from a large volume of information which is often unstructured.
• Maintaining risk profiles and enhanced due diligence: AI tools can be used to automate the creation and updation of customer risk profiles and to identify high-risk customers based on the profile values. AI can also be used to identify high-risk customers in the CDD process.
• AML investigation: In many situations, mere alerts are not enough to complete the investigation and a set of linked entities corresponding to every movement of the fund is required to be investigated. AI can help analyze the mass transactional data to establish such links and present them in a graphical manner to aid in the investigation.

AI & ML technologies in tandem with the traditional AML tools have proved to be of great benefit for financial institutions to provide effective solutions. These have enabled system integrators with useful and time effective technologies in order to fast-track their investigations and effectively detect any suspicious activity. Many financial institutions are adopting these new solutions to stay ahead of their competitors and to efficiently prevent any mishaps while complying with the regulations and being cost-effective at the same time. 

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