The battle against money laundering is becoming a significant issue for all financial institutions globally as criminal tactics become more sophisticated. As a result, AML (Anti-Money Laundering) procedures must be implemented. Because AML entails dealing with a large quantity of client data, AI and Machine Learning are being used to detect and identify money laundering operations.

AI can complete AML duties faster than a human employee, and it can adapt to new risks and detect new money laundering tactics thanks to machine learning. It allows financial firms to adapt to changing regulatory conditions swiftly.

When a client’s transaction data is included in an AML program, AI and machine learning models evaluate the behavior to develop future forecasts and perceptions about that consumer.

How might artificial intelligence and machine learning help in the battle against financial criminals and money launderers?

Perceptions of Customers

Customer Due Diligence (CDD) and Know Your Customer (KYC) systems can now be completed faster and with greater depth and reach, thanks to AI systems. The financial institution can use AI to automate its CDD and KYC processes.

Create a factual profile of the customer by efficiently identifying and collecting data from a broader range of external sources, such as watch lists and sanction lists.

Understand valuable owners of buyer entities faster and more efficiently by utilizing external data.

To eliminate replication and errors and increase the density of AML measures among customers, collect and reconcile customer data across internal systems.

Enhance suspicious activity reports automatically with relevant data from customer risk levels or data from external sources.

Data That Isn’t Structured

Aside from developing client risk profiles, there are a few more critical measures to take. The AML process includes finding and analyzing unstructured data as part of monitoring transactions, screening PEP, screening sanctions, and monitoring media. Every financial institution must make an effort to use unstructured data to recognize their professional, social, and political lives by examining a variety of external sources such as public archives, media, social networks, and so on. 

AI can assist the institution in recognizing this unstructured data in these situations. Following the collection and analysis of data, AI helps the institution prioritize and categorize information to aid risk management.

Detecting and Reporting Suspicious Activity

By producing reports and automatically filling them with accurate information, AI can help with suspicious activity reporting. SARs are required to go through an internal reporting process after submitting notices to the authority. 

Artificial intelligence (AI) can simplify the SAR process by generating automated reports with accurate data and converting that data into an understandable, standardized language, reducing bureaucratic red tape. AI improves the speed and efficiency of an institution’s AML reporting by utilizing standardized language and terminology.

Minimization Of Background Noise

Because the AML system is complex and time-consuming, it is advantageous to incorporate AI into an AML system, which increases speed and efficiency. The level of noise or false positives resulting from incomplete or inadequate data or over-sensitivity of AML steps is one of the major roadblocks in the process. In these situations, AI systems play a critical role by significantly reducing the amount of noise generated during the AML process. AI helps financial institutions better understand their customers’ transaction patterns and eliminates false and invalid alerts, which saves money for the institutions and inconveniences customers. 

AI and machine learning tools enable AML employees to prioritize and direct the most critical money laundering alerts by reducing noise. As a result, AI contributes more effectively to the fight against financial crime.

AI’s Limitations

To stay on top of the rising threat posed by financial criminals and money launderers, new AI and machine learning models are frequently rushed into the market without proper training. As a result, there is a lot of skepticism about AI and Machine Learning. As a result, banks must keep in mind that experimenting with AI yields diminishing returns. They should deliver actionable insights and value by working on strategic, production-ready AI micro-projects alongside human teams.