How Big Data Can Improve AML Compliance Processes

Big Data + AML Compliance

Anti-money laundering (AML) refers to the laws, regulations and processes that businesses must comply with to help stop financial crime. Criminals use money laundering to attempt to hide illicit funds such as income from drug dealing, human trafficking and terrorist financing. Businesses that move money must comply with AML regulations by having an effective AML compliance program in place.

Traditionally, AML compliance has been an expensive, tedious and burdensome process for banks and other financial services. But with the advent of big data and new technologies that use artificial intelligence, this process can be streamlined and more effective than ever. Data analytics combined with machine learning allow businesses to fine-tune transaction monitoring rules so they can catch more suspicious activity and reduce false positives. And advanced case management tools make investigation and reporting easier than ever.

Let’s take a closer look at how big data can help banks and other financial institutions streamline AML processes.

What is AML and Why Is It Needed?

Anti-money laundering is a key strategy for stopping financial crime by making it difficult to deposit and access the proceeds from illegal activity. A warehouse full of cash sounds like a great problem to have, but it’s actually a major headache for criminals.

In the United States, anti-money laundering started with the Bank Secrecy Act, a set of related laws that began with the Currency and Foreign Transactions Reporting Act of 1970. This act was primarily aimed at stopping organized crime by preventing them from depositing large quantities of cash without it being reported to the government.

But as criminals evolved, regulations had to as well. For example, financial institutions are required to report cash deposits over $10,000, so criminals try to avoid scrutiny by making several smaller deposits, a practice called structuring. Criminals also try to hide the source of funds by sending money through several accounts before depositing it into the final account, or by setting up shell companies to hide who truly owns a property. This practice is called layering.

Agencies such as FinCEN, FATF and OFAC continually expand and refine the AML laws to help the financial industry stay ahead of the criminals. And it’s no longer just banks who must comply: any business that enables customers to move money, including online marketplaces, cryptocurrencies, fintechs and gaming platforms are required to have an effective AML program in place or risk facing massive fines.

Compliance Week reports that regulators levied $10.4 billion in global fines and penalties in 2020, for a total of $46.4 billion since 2008. And 2021 is shaping up to be a record year for AML fines with over $200 million in penalties in the first two months of the year alone.

In addition to fines (as well as prison sentences for stakeholders), businesses have a lot to lose when they end up in the headlines because of money laundering activity. Customers are put at risk when a firm does business with criminals, and the hit to the company’s reputation can cause customers to leave in droves.

This is why financial crime management hinges on having an end-to-end AML compliance program that protects businesses and their legitimate customers through the entire customer journey from onboarding through ongoing monitoring. Regulators want to see that your AML program includes Know Your Customer (KYC) and fraud detection as well as transaction monitoring, watchlist screening and regulatory reporting.

But even with all these checks in place, how can you ensure you’re staying ahead of the criminals if they just keep evolving? This is where big data comes into the picture.

Understanding Big Data and Analytics

Big data refers to the large volumes of information that are too vast to process by traditional means. By using big data analytics, businesses can spot patterns and extract valuable insights from these data sets. For example, you can increase operational efficiency by identifying and fixing recurring issues.

Big data is ubiquitous in business, and a variety of data sources are available to answer questions in an instant. This means you can perform risk assessment in real time right when your users are creating their account. It transforms what used to be a manual task and enables automation of risk management and advanced reporting.

How Big Data Can Improve AML Programs

Big data is an ideal tool for AML compliance because it can be tailored to automate and improve many AML compliance processes. AML mandates are (by design) rigid and inflexible, but to evolve and stay ahead of criminals, you need to be able to spot emerging patterns. Let’s look at how big data analytics enable the insights and automation needed in an effective AML compliance program.

Customer Risk Scoring

When onboarding a new customer, firms must perform due diligence as part of their risk management program to ensure the customer isn’t at a high risk of engaging in money laundering. Effective AML programs provide risk scoring in real time and use machine learning to adjust rules over time to help prevent criminals from slipping through.

Reducing False Positives

Big data is also an important tool in reducing false positives where a legitimate customer is flagged as high risk or fraudulent. The alerts created by AML programs are usually up to 90% false positives, which costs companies a great deal of time and money.

Machine learning uses advanced analytics to identify the risk and fraud detection patterns as well as any data quality issues that led to the false positives. The company can then adjust the rules to maximize the catch rate of criminals while allowing legitimate customers through.

KYC

Know Your Customer (KYC) is a broad term that includes ensuring your customers are not at risk of participating in money laundering and that they are not on watchlists such as Politically Exposed Persons (PEPs) or sanctions lists. Once you’ve onboarded a customer, it’s important to ensure they remain a legitimate customer. Machine learning and big data can make KYC methods more effective by identifying patterns in user behavior and providing ongoing screening.

Transaction Monitoring

Transaction monitoring is at the heart of most AML programs. It examines the transactions of existing customers and uses AML compliance rules to flag suspicious activity. By analyzing the vast amounts of data that flow through the business, AML transaction monitoring systems can use machine learning to spot unusual transactions and reduce false positives by refining the rules.

Case Management and Reporting

When transaction monitoring systems identify suspicious activity, the business must investigate it and then report it if it does appear to be financial crime. In the U.S., for example, businesses must fill out suspicious activity reports (SAR) and send them to FinCEN. Case management and reporting are time consuming, so reducing false positives is critical to the success of any AML compliance program.

Using Big Data To Meet AML Requirements

Stopping money laundering is an important step in preventing financial crime. Yet complying with AML regulations is a burden for most financial institutions. Big data automation and analytics can dramatically ease this burden, increase organizational efficiency and save companies time and money.

Jumio’s AML solutions use advanced technology to streamline the entire AML compliance process:

For more information on how Jumio’s AML solutions and big data can help you meet your regulatory compliance requirements, please contact us.