How Money Laundering Concerns Require New AI Monitoring Solutions

As money laundering capabilities evolve and become more complex, the financial sector is meeting those challenges with new AI monitoring solutions.
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Artificial intelligence has created a number of amazing opportunities for the financial sector. The benefits of AI are endless. Financial institutions are using AI to enhance decision-making, improve customer service, project customer needs and much more.

We have talked about the benefits of using big data and AI to improve cybersecurity. But there are other processes that could be equally important for financial institutions.

AI can solve some pressing challenges that financial institutions can’t afford to overlook. This includes the growing threat of money laundering.

AI Could Solve the Growing Threat of Money Laundering in the Near Future

Money laundering continues to be one of the biggest issues faced by financial institutions all over the world today. In fact, an estimated USD 2.4 trillion’s worth of proceeds from illegal activities are funneled through legitimate banking systems every single year, coming out clean on the other end. Making matters worse is the fact that fewer than one percent of this dirty money is detected, frozen, and seized by the authorities.

As many experts point out, the conventional AML approaches are struggling to address the growing concern of money laundering. They state that this warrants the need to invest in AI to fight money laundering in 2020.

The importance of using AI to solve this growing challenge becomes clearer after inspecting recent trends. Criminals are using AI themselves, which leaves financial institutions at a disadvantage if they don’t respond in kind. Criminals are growing more technologically savvy by the day. As such, financial institutions often find themselves lagging behind formidable adversaries who make use of the latest technologies to defraud others and stay a step ahead of the law. They would do better if they used AI as well.

Hence, employing an AI-driven effective anti-money laundering transaction monitoring system is a financial institution’s first and best line of defense against financial criminals seeking to exploit their services for unsavory purposes. Here’s how these solutions can help protect your company:

Transaction Monitoring, Defined

Transaction monitoring refers to the process of monitoring all incoming and outgoing transactions with machine learning algorithms. The goal is to analyze money going to and from a customer’s account specifically to prevent illegal activity. These include, but are not limited to, financial fraud, money laundering, terrorist financing, and evasion of sanctions. Since this task would be impossible to do manually, most financial institutions make use of software that can automate the process for them on a real-time or daily basis.

In addition to account surveillance, transaction monitoring AML software solutions are also capable of analysing a customer’s profile and historical information, which is then used to predict their behaviour and ascertain the validity of future transactions. Some of these state-of-the-art tools can also combat money laundering by using comprehensive anti-fraud, transaction filtering, and event scoring processes to tag malicious actors and prevent suspicious transactions from successfully going through.

What AI Features Should Be Included in an AML Transaction Monitoring System?

AI can help fight money laundering in a number of ways. Some of the biggest benefits of machine learning in the quest to fight money laundering are listed below.

Anti-Money Laundering

Anti-money laundering software applications allow financial institutions to keep track of all transactions in all accounts, including cash deposits, withdrawals, transfers, and ACH activity, among others. Some of these apps may also have the ability to monitor the customer, the customer’s correspondents, as well as all third parties involved in the transaction. This data is then analysed against a customer’s profile and historical information to create a 360-degree view of all of their financial activities.

To determine whether a transaction is legitimate or not, these programs may run the data against a vast library of known AML scenarios, or against rules set by the financial institution. Some AML software applications are also able to generate regulatory reports instantly, thus reducing staff costs and minimizing compliance risks.

Anti-Fraud

Anti-fraud products aim to protect financial institutions from payment fraud and card fraud across all of their products and channels, including ATM (Automated Teller Machine), POS (Point of Sale), SWIFT (The Society of Worldwide Interbank Financial Telecommunication), mobile, and online, among others. Like anti-money laundering software applications, they make use of prebuilt scenarios, artificial intelligence or AI, and machine learning to identify fraud events, recognize complicated patterns, and determine the legitimacy of transactions.

Transaction Filtering

Transaction filtering software programs enable financial institutions to vet transactions through the most up-to-date watch lists to prevent sanctioned individuals and high-risk organizations from making financial transactions through them. These watch lists can include public lists such as the European Union (EU) Financial Sanctions, United Kingdom Treasury (HMT) Financial Sanctions, United Nations (UN) Sanctions, US Consolidated Sanctions, and the United States Office of Foreign Asset Control (OFAC), among several others. Some transaction filtering applications may also support commercial lists such as Accuity, Dow-Jones, and World-Check, as well as any local or custom lists that the institution uses.

Applications that perform this function are also capable of blocking transactions that contain suspicious data such as stop key words, blacklisted countries and cities, and sanctioned Bank Identifier Codes or BICs, in addition to sanctioned individuals and organizations.

Event Scoring

Event scoring software applications make use of machine learning and algorithmic models to manage and prioritize financial crime alerts to reduce false positives when it comes to investigating red flags. They allow analysts to focus their energies on where it’s most needed instead of responding to every single alert that crosses their desks, thus improving investigation efficiency. When combined with enterprise-grade case management software, these applications allow firms to examine transactions against robust data sets quickly and expeditiously while still creating high-quality results.

Investing in a comprehensive anti-money laundering transaction monitoring system for your financial institution can be one of the best decisions that you can make. They allow you to detect money laundering and other financial crimes as they happen, instead of learning about them after the fact. Additionally, they help your financial institution remain compliant with regulators, minimize reputational risk, and maintain customer trust. The sooner that you can invest in one, the better for your company’s overall security.

AI is the Key to Fighting Money Laundering

There are a number of huge benefits of using AI in the financial sector. One of the most important applications is with fighting money laundering. The benefits listed above should help.

Ryan Kh
Ryan Kh is an experienced blogger, digital content & social marketer. Founder of Catalyst For Business and contributor to search giants like Yahoo Finance, MSN. He is passionate about covering topics like big data, business intelligence, startups & entrepreneurship. Email: ryankh14@icloud.com