How to Best Use Big Data in Anti Money Laundering Compliance?

Firstly, they have to outsmart the criminals to prevent them from exploiting the system to clean their dirty money or to use the money for terrorist financing or other illegal businesses. Secondly, they have to keep up with the ever-changing legal requirements of the local governments and International governing bodies like FATF in order to ensure AML KYC compliance. This necessity to purge fraudsters and financial criminals has made the term big data anti money laundering a catch phrase in reg tech industry.

Apparently, it may seem that banks and financial institutes have a very simple task of keeping records of their client’s names, addresses, contact information, or resources of funds as part of KYC requirements. In reality, the task is a lot more complex. Criminals are becoming smarter with the use of tech. Using a fake identity, forging the documents, hiding the ultimate beneficial owners of funds, and committing fraudulent transactions are becoming easier due to tech tools.

Financial institutions which are primarily faced with the task of serving the right customers, often face resource constraints to comply with mandatory regulations to not-serve the wrong customers by identifying discrepancies in the information provided by customers or flagging the suspicious account activities to spot the money launderers. This is where Big Data comes to play its part and makes the task easier.

Role Played by Big Data in Anti-Money Laundering Compliance

Following are a few areas where companies can leverage big data to comply with anti-money laundering compliance.

KYC and Customer Due Diligence

Banks have to do KYC checks while complying with the regulations of their jurisdictions. European Union has its own set of regulations. In the United States financial institutes and companies have to comply with the Bank Secrecy Act (BSA), the US Patriot Act, and even with some additional requirements of FinCen (Financial Crime Enforcement Network). Apart from local regulations countries also have to comply with regulations of FATF. Performing customer due diligence, while complying with the legal requirements becomes quite challenging, and requires analysis of the customer’s data which is a tedious task. However, a simple application of big data tool can make it simple. Specific algorithms designed to see whether the customers’ data comply with the regulations, instead of manually checking for each customer make the task easier.

Also Read: Geographic Targeting Order- How FinCEN Fines Non-Financial Businesses for AML Violations

Transaction Monitoring

KYC as an anti-money laundering measure is an ongoing process. Once a customer has been on-boarded, the next step is to monitor the transactions of high risked customers to check for any suspicious activity. Again, financial institutions and even financial crime experts can’t just manually tell whether a transaction is fraudulent or not. For example, if a customer disguises a fraudulent exchange of funds as some legitimate transaction of buying or selling software then the right thing would be to investigate whether that software costs the same price or not. For this purpose, available data related to transactions of similar products is analyzed and then compared with the transactions.

Also Read: KYC Ongoing Monitoring in Cross-Border Transactions

Risk Assessments

For financial institutions, a continuous risk assessment of their customers is necessary to ensure that they are not facilitating the flow of illegitimate funds. For this purpose, they need to monitor the patterns of transactions and detect any anomalies. Banks and companies that deal with hundreds of thousands of customers, can detect such irregularities manually. They already have the data of customers transferring and receiving money. Here, all they need is a system that uses this data effectively to point out the exact transactions which seem suspicious and then flags them.

Reporting of Suspicious Activity

Finally, after detecting anomalies and irregular patterns in the transaction behaviors of the customers, big data tools help accurate and timely reporting of suspicious activities. To better use big data for anti-money laundering, financial institutes can use integrated software or similar products that help filter, clean and organize the customers’ data to check for compliance.

How Does Big Data Actually Work?

For financial institutions, it is only the data that their customers provide to them or the data they access during the course of the provision of services. However, in order to perform due diligence and enhanced due diligence companies and financial crime fighters can use additional data as well. For example, by using the device locations, IP address, phone’s IMEI, account log-in patterns, or other features of the devices that customers use, one can get information about possible fraudulent activity on an account.

Big data also refers to the user-generated data on social media platforms available in the form of text, audio, videos, and even other forms of media. However, accessing this data is one thing, but identifying relevant data, cleaning it, and then analyzing this data for patterns and suspicious activities is another thing. This can be beyond the scope of an institute’s resources which is primarily focused on providing financial services. The good thing is that in the past couple of years, many KYC AML vendors have emerged that offer financial institutions tailored solutions to manage and analyze customers’ data for compliance with anti-money laundering regulations.