Money Laundering is at an All-Time High; AI can Help Banks Tackle this Global Threat

Money Laundering, one of the most prominent financial crimes, is constantly on the rise. At the heart of this criminal activity are sophisticated money launderers with the ability to move illicit funds seamlessly through the formal financial system. Criminals continue to evolve in their laundering techniques, locating and exploiting loopholes in the system to move money.

A combination of factors have become a catalyst for the surge in money laundering activities, starting with the COVID-19 pandemic that spurred mass adoption of online banking, peer-to-peer payments and e-commerce. Then there’s the rise of cryptocurrencies and the increasing sophistication of hackers, exploiting this new asset class to launder stolen and illegal funds. Next is the growing interdependence of the world’s economies. Cyber-criminals are becoming more sophisticated and tech-savvy, while financial institutions are facing inconsistent rules and regulations to mitigate fraud, as well as a lack of effective technological solutions for fraud detection and prevention.

For financial institutions, failing to comply with Anti Money Laundering (AML) regulations results in hefty fines. According to Financial Institution Fines, between 2016-2020, global AML-related fines totaled $25.7B. In 2020 alone AML fines reached a whopping $10.6B. The main reasons that financial institutions are being fined are failing to implement adequate internal control systems, failing to establish and maintain AML policies and procedures, and deficiencies in identifying and managing money laundering risks.

Combating money laundering is an enormous task, and it comes with substantial costs and risks that include regulatory, reputational and financial crime risks. Managing these risks rests with the guardians of the financial system. When relying on manual or outdated legacy systems like traditional AML software, monitoring transactions is often fragmented and inconsistent, and is simply inadequate in dealing with the growing sophistication of bad actors. This is why the ability to effectively monitor transactions for suspicious activities has never been more critical.

Rule-based solutions are outdated 

Many financial institutions still use traditional AML solutions to detect and prevent money laundering. These solutions were designed and implemented over the last decade, before the era of massive digital payments that has radically changed both the nature of cybercrimes and the AI technologies built to combat them. Traditional technologies are often rule-based and depend on manual inspections. They are very limited in handling real-time digital transactions, for a number of reasons:

Dynamic data challenges. Rule-based systems lack a learning component, and therefore depend on manual modifications to the rule knowledge base in order to adapt to changes in the environment and evolving threats.

High false positive rate. Rules are usually tuned to a conservative risk policy, with the aim of catching every fraudulent transaction. The problem with this policy is that it leads to a high number of false positives – valid transactions which are wrongly marked as fraudulent and are therefore declined. This entails not only loss of business but also frustrated customers, often leading to churn.

False negatives. The flip side of false positives are fraudulent transactions which the system fails to flag. When the rule-based system is relaxed in order to lower the number of false positives, the holes in the fishing net become larger, and many dubious transactions are let through. A missed fraudulent transaction results in reputation loss – and in heavy fines levied by the regulators.

Manual work. Due to the high volume of false positives and false negatives, a lot of manual work is required to investigate situations which are not clear cut. Significant time and resources are spent on lengthy investigation processes, which can often take months.

Explainability. Regulators require banks to provide better explanations for declining transactions that are flagged suspicious, to ensure no bias or discrimination against certain population groups are in play. This component is lacking from most traditional monitoring solutions, resulting in compliance issues and fines.

Needless to say, the evolving payments ecosystem – and the emerging threats imposed on it – demand a more efficient and effective approach to strengthen AML efforts.

How can AI help fight money laundering? 

Realizing that AI solutions can close the gap in current AML needs, technology companies and banks are actively designing new solutions and tools to better assess high risk jurisdictions, identify suspicious fund movements, and refine the screening of Politically Exposed Persons (PEP) and sanctioned individuals and organizations. According to Deloitte, regulators are also in agreement that AI can and should be leveraged by banks to improve risk identification and mitigation. These solutions are based on Machine Learning (ML), and rely on the ability of algorithms to autonomously detect suspicious activities within massive amounts of data, in real time. Following are the strengths of AI-powered solutions in helping banks fight money laundering:

Rapid response to evolving dynamic data challenges. AI solutions are completely autonomous, and as such they do not require any manual intervention in order to monitor data streams for any changing or suspicious patterns. Unlike legacy monitoring systems, AI solutions continuously adapt to changing scenarios and use cases independently, enabling banks to keep pace with the evolving nature of financial cybercrimes.

Reducing false positives and alert storms. While rule-based systems identify any anomalous behavior as fraudulent, AI-based systems can understand the anomalous behavior in context (for instance, anomalies related to a seasonal spike). As a result, fewer valid transactions are wrongfully declined, less resources are required for manual investigations, and customer aggravation is prevented.

Reduction in false negatives. False negatives often occur due to “unknown unknowns”, when no rules have yet been established for a new type of fraud. AI can detect these anomalies on the fly, often uncovering schemes that could not have been detected with existing rule-based logic. This is a crucial capability in an era when hackers use cutting-edge technology to constantly reinvent themselves and create complex and sophisticated new schemes.

Being able to explain why certain transactions have been declined. Current regulation requires that each fraudulent/valid decision be explained. While rule-based systems don’t always provide exact and clear explanations, the latest generation of AI solutions are capable of providing full explainability for each prediction or decision made by the system.

Significant savings in resources and costs. Since confidence in rule-based systems is relatively low, many traditional systems need to be supplemented by manual inspections. AI-powered AML solutions enable the reduction of overhead dedicated to these costly investigations, while complying with stringent AML regulations and avoiding violation fines.

Harnessing the power of AI with BeyondMinds 

BeyondMinds provides AI solutions which are hyper-customized to each bank’s data, use case, sensitivity threshold, and regulatory needs. We utilize internal and external data sources and leverage advanced analytics and machine learning capabilities to effectively detect fraudulent transactions, while dramatically shortening the inspection loops and processing times of flagged transactions. BeyondMinds’ solutions analyze millions of daily transactions, triggering near real-time alerts when anomalies are identified. In addition, we can help banks segment customers by risk types. These solutions significantly reduce the number of false positives, nearly eliminate false negatives – and provide regulation-required explanations for decisions and predictions.

BeyondMinds solutions are production-grade, making it robust, scalable and trustful, and completely adjusted be deployed in real world business environments. They are designed to meet the toughest security, privacy and compliance needs of financial enterprises, making it ideal for banks dealing with sensitive data and stringent regulations.

These AI solutions can be deployed in just 10 weeks and delivered as a service, providing ongoing updates seamlessly and helping banks keep their TCO of the solution at bay.

To learn more about how we can help your enterprise protect itself against money laundering, click here.