Now that we know what Anti Money Laundering (AML) is, let us now see what AML can do.
AML capabilities include fraud detection, screening and onboarding clients, identifying suspicious or anomalous transactions, risk assessment, data integration, investigation, alert in case management, and behavioral analytics.
Fraud Detection and Analytics: Initial vetting during onboarding acts as a deterrent for most fraudsters. However, there are chances that the user may misuse the account afterwards. An AML software can build customer/user profile incorporating details like balance, frequency, and types of transactions and match it with predefined patterns to identify fraudulent transactions in real-time. In addition, some of the software can learn patterns drawn from historical cases to detect fraudulent behavior and raise appropriate warnings. Analytics plays a big part in flagging suspicious transactions. However, fraud detection and analytics are also in a constant race with the criminals to keep one step ahead of each other. Dealing with a massive number of unproductive alerts is a tedious and resource-intensive task. Also, a machine may miss important aspects of human behavior. This problem can be solved by applying advanced techniques like Machine Learning (ML) and Artificial Intelligence (AI) along with algorithms, statistical models, and analytics to spot patterns and make inferences.
Screening and Onboarding Clients: Customer identification is a critical part of the overall KYC process. The company must verify the accuracy of information furnished by the customer. Inaccurate data will nullify all KYC, AML, and CDD processes. The lapse may also result in company failing to comply with regulations and thus risking punishment from the regulators. During this process, all past transactions of the customer are scrutinized. Suspicious transactions, if detected, are investigated further. If any transactions are criminal, the organization must decide if they want to onboard a client involved in misdeeds. Along with transactions, other details being scrutinized include the business the customer works in, accuracy of the documents submitted by the customers, and whether the customer has political links (politically exposed person or PEP). The customer’s account is opened when they clear all these criteria. However, many companies continue to perform these checks at regular intervals after onboarding to safeguard themselves against any violations.
Now, AML software helps organizations onboard customers from various countries having difference compliance norms without fear of noncompliance, as the software checks the process for any violations and flags violations, if they occur.
Identifying Suspicious or Anomalous Transactions: As stated above, the organization’s work is not done after the customer is onboarded. With increase in financial crimes, transaction monitoring has become even more essential. Regular scanning allows organizations to build a customer’s profile based on transaction history. Thus, any anomalous transaction is spotted. Apart from identifying suspicious transactions, transaction monitoring helps organizations in building a better customer risk profile.
Risk Assessment: Technological advances are handing crooks newer ways to launder money. It is a race between the good and bad guys. To avoid this issue, organizations can resort to AML risk assessment. Organizations pinpoint business areas having more scope for money laundering and people wanting to finance nefarious activities like terror. The process is known as spotting Key Risk Indicators or KRIs. The KRIs help organizations assess the risk associated with business relationships and transactions and take decisions in the organization’s best interests.
Data Integration: Data that needs to comply with AML laws originates from various sources such as ATM machines, point of sale systems, and the financial institution’s own mainframe. Sifting through such a massive amount of data is analogous to looking for a needle in a haystack. Organizations can also face issues concerning data or lack of it with legacy or older clients. Some data concerning these clients may not be present in the system, as earlier norms did not need that data. This may result in low-quality data, which in turn will impact the organization’s capability to curb bad transactions. Thus, cleaning up of the data is essential to help guide the ML engine give better outputs. With ML, good and clean data will give good results, and vice versa. But integration is just the first step. After integrating, the data needs to be compiled in a common format to enable efficient scanning by ML engine. Also, ability to add data in real time will enable organizations to detect red flagged transactions earlier and make AML compliance proactive, instead of reactive.
Behavioral Analytics: In behavioral analytics, the customer’s behavior on various platforms like eCommerce sites, internet apps, and online games to build variability. If the customer’s behavior differs from the set patterns, it will raise a red flag. For example, a person’s usual monthly expenditure is around $8,000. If the person suddenly starts spending 20-30,000 dollars, it will raise a red flag.
Behavioral analytics is important, as it greatly reduces amount of potentially false suspicious activity reports or SARs. Dealing with massive amounts of SARs has become a liability, as compliance officers can scan only a few of them. This allows genuine illegal activities and cybercrime to go undetected.
Case Management: Case management is a critical step in the AML chain. In case management, analysts look at all the red flagged cases and determine which of them represent criminal activity and thus need further investigation. Building a case by incorporating relevant data like transactions and accounts and parties involved is essential if the case is to go before the government.
Investigation: Successful case management is usually the last step in a money-laundering probe. The process begins with proper investigation. A key aspect of any money-laundering case is to ‘follow the money’ via a trail until the offence can be linked with the perpetrator or in case of corruption, money is found. The investigation obviously begins with the SAR generated by the system. After determining that the transaction is indeed suspicious, the investigator checks the information about the customer obtained via mandatory due diligence to check whether the activity is in line with the customer’s business or occupation and whether it fits the definitions of normal activities associated with the account types. If the transaction ticks all the wrong boxes, then the investigator must establish the offence and then report the same to the authorities. An AML software can help ease the burden on the investigator by flagging SARs and checking whether transactions follow the compliance norms.
Thus, having good AML software and a team of good analysts and investigators can help financial institutions fight the good fight with more vigor while ensuring they stay on the right side of the law.