The use of Artificial Intelligence (AI) technology has made it easier for businesses to handle repetitive tasks effectively. It has eliminated the room for random human error and reduced the timeline for task completion. Big data plays a crucial role in training AI algorithms to do the job. Businesses from different industries are using the power of AI and big data to improve operations and customer experience. The compliance world has also adopted this powerful technology to handle key challenges including Anti Money Laundering (AML) requirements. Read on to learn how big data and AI can improve the AML compliance process.
What is AML compliance?
Money laundering is one of the major compliance-related risks that businesses are prone to. This illegal practice can have grave financial and legal consequences for any business that fails to address the same. AML compliance can be explained as legal guidelines and procedures that need to be followed by organisations to discourage money laundering efforts. This helps to ensure that no money laundering operations are taking place. Financial institutions are more prone to this risk given the nature of their business activities. Know Your Customer (KYC) is a baseline measure put in place by financial institutions to filter high-risk applicants.
Understanding AML
In the modern financial world, AML and KYC go hand in hand. No institution can accept customers without adhering to the KYC identification process. It is a part of their AML check. So, what exactly does AML entail? Well, AML is used to develop standard controls that must be followed by different organisations to keep a check over money laundering activities. It helps to identify, avoid and report suspicious behaviour related to money laundering. The goal is to prevent criminals from hiding the source of their money in any financial transaction.
Here is an example of a common AML practice in the United States. Financial institutions are required to report any financial transaction over USD10,000. Now, many a time, criminals deploy some countermeasures to bypass the AML checks. In this instance, many criminals deposit several small amounts to stay below the USD 10,000 benchmark. These amounts could be deposited from multiple bank accounts to avoid scrutiny. This money laundering practice is known as ‘layering’.
How AI and big data can assist with AML requirements?
Manually identifying patterns related to money laundering practices can be very challenging. It might also lead to a high number of false flags and this can deteriorate the customer experience. AML analytics can be a game-changer for financial institutions. Big data and AI can be customized to automate and upgrade the compliance procedure for AML. It can also help financial institutions to identify any advanced money laundering techniques such as layering. Let’s explore how AI and big data are assisting with AML analytics and compliance requirements.
- Customer risk scoring
Performing due diligence while onboarding a customer should be a key component of the risk management program for financial institutions. This helps to rule out the possibility that a customer has a high-risk profile. The use of big data and machine learning techniques can help to provide real-time risk scoring and alter the rules to counter the evolving methods of money laundering.
- Reduce false positives
False-positive cases are quite high with manual monitoring and flagging of money-laundering related activities. Big data and AI can help to reduce the count of false positives. False positives don’t just cost time and money but also causes inconvenience to loyal customers. Deploying AI tools can help to overcome this challenge. Machine learning algorithms leverage advanced AML analytics to detect patterns related to risk and fraud.
- Transaction monitoring
At the core of all AML policies lies monitoring transactions. Using big data and AI along with compliance rules can help to flag any suspicious transaction from customers. Keeping a track of all transactions being conducted by customers daily is hard. The level of time and resources needed to flag suspicious activity is hard to fathom. AI tools can be leveraged to analyse the vast amount of data and spot fraudulent transaction patterns.
- Financial reporting
Compliance laws in different countries demand businesses to report any suspicious activity identified by systems put in place internally. Organisations need to investigate the flagged activity and file a report if it isn’t an actual financial crime. AI-based AML analytics can easily help with financial reporting.
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