AML monitorin

Money laundering is an ever-growing challenge for financial institutions. Money laundering cases can malign a bank’s reputation, even if it wasn’t involved in the scandal. Banks are responsible for verifying their customers and ensuring they aren’t involved in money laundering activities. Did you know criminals launder between USD 800 million and 2 trillion every year? Financial institutions are obligated to implement Anti-Money Laundering (AML) schemes as per the regulations. Banks might be fined or penalized by the regulatory authorities upon ignoring AML monitoring policies. Read on to understand how banks are improving their AML monitoring processes with Artificial Intelligence (AI).

What Exactly is AML Monitoring?

Before discussing the role of AI, it is essential to understand the basics of AML monitoring. It is the process of monitoring financial transactions and customer behavior by a financial services firm. Financial institutions monitor the activities of their customers to ensure they aren’t involved in money laundering activities. Several laws, regulations, and techniques are implemented to identify financial crimes, like money laundering and terrorist funding. 

AML monitoring is also essential to stay compliant with the regulations. Regulatory authorities might slap banks with fines or penalties upon ignoring AML policies. The ultimate aim of AML monitoring is to prevent the circulation of illicit funds in the financial system.  

AML monitoring is an ongoing and continuous process. Banking customers make payments daily, and there is a need to monitor those transactions. Also, the rise of Fintech platforms has increased the hassle for financial institutions. The number of transactions has increased, and financial institutions must monitor them continuously. 

Banks aren’t the only financial services firms that indulge in AML monitoring. Trading firms, NBFCs, insurance companies, and other organizations also focus on AML policies to remove the risk of fraud. 

Besides AML monitoring, financial institutions also depend on KYC, customer due diligence, watchlist screening, and record-keeping to verify the authenticity of customers. Let us delve deeper and understand the role of AI in AML monitoring.

Demystifying the Role of AI in AML Monitoring

Gone are the days when AML experts manually scanned documents to identify fraudulent activities. AI, ML, and other new-age technologies are the next frontier in financial crime prevention. Here’s how AI is useful in AML monitoring:

Automation of AML Processes

AI is already in action for automating AML tasks, like character recognition, document translation, and risk scoring. Natural Language Processing (NLP), Optical Character Recognition (OCR), and other subsets of AI are used to automate mundane tasks. For instance, employees within a bank do not have to scan KYC documents manually. They can rely on OCR technology to scan documents in real time. Similarly, NLP is used to extract data from the documents and feed them into the system for further analysis. Employees can use their skills and time for complex financial crimes, as mundane tasks are already automated.

Enhanced Customer Due Diligence

Customer due diligence is the first step towards preventing money laundering activities. When a new customer applies for a financial service, the bank must verify their identity, address, and other details. It is why banks and other financial services firms indulge in KYC (Know Your Customer) verification. AI has automated KYC verification processes for banks. AI-led systems are being used for risk scoring. When the risk score of a customer is high during due diligence, the bank can deny providing services to them, thus preventing future hassles.

Money Laundering Pattern Detection with AI

Before the introduction of AI, financial services firms used rule-based systems to prevent money laundering activities. The rule could be anything decided by the financial institution. For instance, the rule-based system might generate a red flag when a transaction value is more than a pre-decided value. However, these rule-based systems failed to match the increasing complexity of financial crimes. Since the financial data is dynamic, we need AI for advanced pattern discovery. AI-led systems can identify new money laundering patterns in real-time.

Anomaly Detection and Real-Time Monitoring

AI can identify unusual patterns in real-time, thus allowing AML experts to make informed decisions. For instance, AI can identify when a customer makes payments from a location far from their usual location. Similarly, other irregular financial activities are identified in real time by AI-led monitoring systems. The proactive nature of AI-led AML systems allows financial institutions to prevent financial crimes from happening. 

In a Nutshell

Besides the aforementioned processes, AI helps with customer segmentation, behavioral analysis, reducing false positives, and regulatory compliance. With AI, financial services firms can identify anomalies in customer transactions in real-time. Not to forget, they can deal with the dynamic nature of financial crimes. So, it is advisable to deploy AI-led AML monitoring systems for improved efficiency!

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