Criminals use the process of money laundering to hide their unlawful funds they derive from activities like drug deals, terrorist financing, and human trafficking. Therefore, a retail, regional, or commercial bank must comply with Anti-Money Laundering regulations to have AML compliance in place. This is where AML analytics come to be of help. They are related to the processes, laws, and regulations that a financial business should comply with to mitigate money laundering and financial crime.
Earlier, AML compliance used to be a tedious and expensive process for financial institutions. However, advanced technologies and big data analytics that use AI have streamlined the process and made it more efficient than ever. Banks can fine-tune their transaction monitoring and catch any suspicious activities using data analytics and machine learning while reducing false positives.
Let’s take a closer look at how advanced analytics help combat money laundering.
Scoring Customer Risk
Whenever a bank onboards a new customer, they must perform diligence and assess risk to ensure that they are not at a high risk of money laundering. An effective AML program must score a customer’s risk in real-time and use ML to adjust rules and prevent criminals.
Minimizing False Positives
AML analytics are essential tools for minimizing false positives where legitimate customers are flagged for fraud or high risk. AML programs may create false positives, which ultimately cost the financial institution a significant amount of money and time. Advanced analytics use machine learning to identify fraud and detect risk patterns while mitigating false positives. The system adjusts rules to catch criminals and allow legitimate clients through.
KYC (Know Your Customer) is an effective method of ensuring that customers do not participate in money laundering and are not present on the watchlists. Big data and machine learning improve KYC efficiency by identifying user behavior patterns and providing real-time screening.
Monitoring transactions is the most crucial component of most Anti-Money Laundering programs. It aims at monitoring each existing customers’ transactions and using compliance rules to identify any suspicious activity. AML monitoring systems analyze all data flowing through a business and use ML to spot unusual transactions. They also refine rules to reduce any false positives.
Managing and Reporting Cases
Once the monitoring system identifies suspicious activity, the bank must investigate and report it if it appears to be involved in money laundering. Since managing and reporting cases are time-consuming, minimizing false positives is crucial for success with the AML compliance program.
Preventing money laundering is a significant step in stopping financial crime. Still, AML compliance is a heavy burden for most banks and financial institutions. AML analytics can significantly ease this burden, save the company’s money and time, and increase efficiency. These solutions use high-end technology to streamline the process and ensure that the customer being onboarded provides a genuine identity. They check any customer against watchlists and detect suspicious activities using advanced ML and AML rules.
Advanced analytics make case management, investigation and reporting more straightforward while reducing false positives. Contact an AML analytics provider now to meet regulatory compliance requirements and stay safe.