Integrating AI and machine learning into anti-money laundering (AML) and combating the financing of terrorism (CFT) systems has become imperative for financial institutions (FIs) to safeguard their operations, customers and reputation effectively.
Sophisticated financial crimes require advanced solutions to detect and prevent fraud. Money laundering, for example, is a financial crime that often involves more serious offenses. Money laundering alone accounted for trillions of dollars that helped fund international criminal activities, including $346.7 billion in human trafficking, $782.9 billion in drug trafficking, and $11.5 billion in terrorist financing.
A recent research report by Datos Insights highlights the various types of crimes banks are addressing in their AML programs. The majority of FI compliance and AML executives surveyed are actively monitoring fraud/scams, human trafficking/child exploitation, elder abuse, international terrorism, drug trafficking and domestic terrorism. More than half of FI compliance and AML executives, surveyed by Datos Insights are monitoring cybercrime and corruption.
The role of AI in fighting financial crime
To stay one step ahead of financial crime, FIs need to incorporate AI and ML to flag transactions faster. Recent research by SAS, ACAMS and KPMG on the state of AI and machine learning in AML compliance highlights the need for FIs to deploy these technologies, as organized crime now uses AI to deceive at scale.
Spotting different money laundering patterns is challenging, as it requires examining various data points and data sources. Additionally, it requires the ability to connect them across different systems to better identify suspicious flows and patterns. Detecting money laundering is becoming increasingly difficult due to the complexity and speed of global financial transactions.
AI adoption in AML: Progress and barriers
While AI and ML offer significant advancements in detecting and preventing various financial crimes, challenges persist. Most FIs already use advanced technologies in some capacity for AML. In fact, 43% of banks surveyed for our recent research report say they are either piloting or planning to implement AI and ML in the next 12-18 months.
However, FIs have traditionally relied on rules-based systems and decision trees for AML. Using AI and ML can help identify complex suspicious patterns to reduce false positives.
Obstacles to AI implementation in AML
Most FIs struggle to incorporate newer technologies due to multiple fragmented data sources and the challenge of integrating various legacy systems. Additionally, cleaning, labeling and integrating data for AI training can be complex and time-consuming. Conducting KYC procedures is critical for banks to detect and prevent financial crime. However, challenges remain regarding ownership of this process throughout the lifecycle of the customer relationship.
The path forward: AI as a necessity, not an option
Despite these persistent challenges, incorporating AI and ML can help FIs detect financial crime more quickly and effectively. To succeed, FIs must invest in the proper infrastructure, adopt explainable AI models to improve transparency and regulatory compliance, incorporate human expertise and collaborate with regulatory authorities.
Integrating advanced technologies for detecting and preventing financial crime is no longer a futuristic concept but an imperative for FIs. As financial crimes become more sophisticated, institutions can no longer afford to delay leveraging AI’s capabilities. FIs are at the forefront of crime prevention – they must safeguard their operations, reputation and customers to ensure a secure and resilient banking environment.
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