AI and ML — Redesigning Existing Anti-money Laundering Practices

Law enforcement agencies are taking all possible measures to mitigate the risk of crime in the finance sector, and so AI-powered solutions have emerged.

Law enforcement agencies’ efforts against money laundering have led the financial sector to move towards AI-driven approaches. The average annual amount of laundered money has reached $1.87 trillion?

Just like human interaction, money laundering has evolved too. Leaving behind the old tactics of transferring funds offshore, now they put money in the banking system. The funds are circulated in the financial system and get cleaned through a network of transactions. Ultimately, black money is legalized with the supporting document and no connection with the original sources.

FinTech — Introducing AI and ML

Traditional AML systems can’t shield the financial system, they are outdated in the era of virtualization. The professionals are well-aware of this and have understood the need for AI and ML (Machine Learning) in the system. It is wrong to say that ML or AI are new in society — about every new phone is empowered with both. Machine learning has proved very beneficial, especially in reducing the number of incorrect results (False-positive and False-negative). AML and CFT practices have revolutionized because of ML and AI, particularly

• Know Your Customer
• AML Screening
• Transaction Monitoring

The aim of FinTech is to upgrade the existing systems through multiple applications of AI/ML, but not compromising on customer satisfaction and ease. The customer behaviours can be analysed and the flow of transactions can be streamlined. The regulators always force financial organizations to move forward with innovative technologies by introducing new AML requirements. The computerized data helps in auditing and examination, as they also use software for examination and scrutiny.

The applications of AI and AML are:

Bringing Down False Positives

AML systems screen customer names against criminal lists and then are given risk status as per their profile. The conventional systems can’t be trusted because sometimes they verify a criminal entity, commonly known as false positive verification. This is a compliance gap and security risk as potential money launderers have joined the system.

The AI algorithms can detect all types of customers (low-risk and high-risk) by:

• Checking the association with criminal rackets
• AI uses human-like analysis techniques that help identify an actual money launderer. The model is trained on different methods to make it mature enough to efficiently spot criminal entities

Analysing Customer Behaviour

This is one of the best advantages of machine learning, as it can analyse user behaviours and predict their movements. ML examines the user actions and activities such as transaction flows, timings and locations. It helps in decision-making while onboarding a new customer. The patterns of illegal funds transfer tell how money launderers will carry out their activities. ML and AI are used simultaneously to stay one step ahead of the criminals by making confident predictions about their next move.

Data Examination

Financial institutions have realized that a risk-based approach is an effective technique while implementing AML practices. The potential risks are assessed and then mitigated or avoided accordingly.

To identify risks, the AML software should have massive customer data and watchlists around the globe. The customer background and transaction history are analysed. The information is gathered from multiple sources like social networks, public image, society. The data tells how authentic customers are, by processing it through various algorithms.

Transaction Monitoring

Transaction monitoring are checks that verify the transactions behind received and sent. Criminals are smart enough to deceive the traditional AML systems, so effective AML compliance needs a solution that can block all attempts of illicit funds transfers. Transactions are monitored and if they raise any red flag, they are marked suspicious and reported to the law enforcement authorities. The process is known as suspicious activity reporting (SAR).

Final Thoughts

Businesses have to modify their existing systems and adopt AI-based solutions. They will help in identifying even a small amount of money laundering by low-risk customers, which is very tough for conventional systems to detect. Due to compliance gaps, businesses have been penalized. However, the cutting edge technology will help in overcoming all loopholes in the system and eliminating illegal funds from the financial system.