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Using Data analytics to find AML Risks

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Data analytics has become indispensable for organizations across the globe. It is evident from the fact that the global big data market is poised to reach an astounding USD 420.98 billion by 2027. Big data analytics is used across multiple industries such as retail, fashion, OTT, e-commerce, healthcare, etc. Apart from these, big data analytics is also playing a crucial role in preventing financial fraud. It shouldn’t be surprising as the AML market is predicted to be worth USD 4.5 billion by 2025.

AML or anti-money laundering involves setting up policies, systems, and technologies to prevent the laundering of money. AML is primarily implemented across financial institutions and government organizations.

AML prevents money laundering by working on three fraud operations:

  1. Placement — the act of shifting the illegal money from the source to a disconnected place.

  2. Layering — the act of covering up the trail to avoid being traced.

  3. Integration — the act of disguising illegal funds as clean money.

AML Analytics aims to detect, track, resolve, and prevent such financial fraud by analyzing the collected data, forming patterns to connect pieces and people, monitoring everyday business transactions, and putting in place robust security measures for protecting the organization from such organized financial fraud.

How can Data Analytics assist AML activities?

Financial criminals are increasingly becoming proficient. Thus, financial crimes are extensively sophisticated and challenging to track. It becomes crucial for companies to step up their investigation mechanisms using machine learning algorithms, data science statistics, and business management logic to identify and counter suspicious activity as soon as possible. Here are a few ways to do this:

  1. Know Your Customer well

For fraud identification, analysts must be aware of the definition of “a Risky customer” for the company. This customer risk assessment can vary from one organization to another, but the main factors determining whether or not a customer can be risky include:

  • The ownership of a business heavily dependent on cash

  • Regular transactions to and from high-risk countries

  • The susceptibility of the customer to get involved in corrupt practices

  • A record of the customer being politically exposed in any financial issue

Suppose a customer falls into the category of being high risk. In that case, the analysts are required to run the Enhanced Due Diligence (EDD) procedure apart from a thorough character background check to confirm their suspicion through the verification of documents and an in-depth examination of their financial history.

  1. Shortlist possible fraud indicators

Here are specific indicators that can be selected through AML analytics for sending alerts to the analyst when encountered:

  • Inconsistency in necessary identifiers in the documents submitted.

  • Multiple customers with different names but identical addresses.

  • The specification of a non-civic address instead of a residential address.

  • Transaction amount exceeding the initial projected activity.

  • Regular transactions of abnormally high amounts to the same person.

  • Transactions that are typically expected to be beyond the capacity of the customer.

  • A sudden drastic change in the activities of a customer.

  • Funds transfer of similar amounts from the same customer to a single identity from different locations or cashiers to avoid suspicions from being recorded.

  • Frequent encashment or currency exchange under short notice often follows a sizable funds transfer.

  • Similar transactions from other geographical locations.

  • Multiple wired transfers are sent to a single recipient from multiple identities with matching amounts.

  • Periodic transfer of funds to overseas destinations from a customer whose financial profile is questionable.

Such red flags can be focussed upon to filter out high-risk customers from the usual lot. As advanced AML analytics can be applied to compliance programs, the customers in the high-risk nucleus will be closely scrutinized, their transactions monitored, and their behavior thoroughly investigated.

  1. Make the right decision.

AML Analytics involves implementing machine learning concepts like predictive algorithms to the given collection of data to find out which factor impacts the decision of considering a customer in the riskiest category. One such method is the Decision Tree. It resembles a real tree and contains branches, nodes, and endpoints like leaves. Through patterns and data clustering, the analyst can follow a suggestive branch to an endpoint to conclude if a combination of activities leads to a high-risk identification mark or not. From the decision tree, it can also be possible to pinpoint the most frequently occurring determinant for a high-risk customer or link one customer to another based on similar attributes like the location of source and destination, frequency of transactions, and time of funds transfer.

Based on the number of frauds discovered, it has been estimated that the total amount of money laundered in a year is between USD 800bn to USD 2tn. But by adopting the simple yet powerful combination of AML analytics and case management capabilities of an organization into its compliance strategy can save the company from losing billions. Implementing advanced data science techniques like these can help the company move one step ahead of criminals by paying attention to behavior they would not have otherwise suspected.