How AI is enhancing anti-money laundering strategies for improved financial security
By Dhiren .V. Dedhia, Head – Enterprise Solutions, CrossFraud
Financial crimes have been on the rise in recent years, posing a significant threat to economies, businesses, and individuals alike. Money laundering, in particular, has become a profound global problem. According to an Enforcement Case Information Report (ECIR), India witnessed a staggering 2,283 cases of money laundering associated with various illicit activities as of November 2021. These activities encompassed bank fraud, fraudulent deeds, and siphoning of government funds.
While the ongoing digitisation of financial systems is contributing to the global interconnectedness of economies, they have also created new opportunities for criminals to exploit vulnerabilities. Money launderers have grown increasingly smart. They find loopholes and employ complex tactics to launder illicit funds. Traditional methods that involve manual monitoring for suspicious activities often struggle to keep pace with these evolving tactics. To tackle this, financial institutions have started using tech-enabled tools to keep track of any malicious activity. The need for more efficient and accurate tools to combat money laundering has driven the integration of artificial intelligence(AI) and machine learning (ML) into Anti Money Laundering (AML) processes.
These technologies have the capability to process vast amounts of data at unparalleled speeds, identifying intricate patterns and anomalies that might otherwise go unnoticed.
How does AI-Powered AML work?
Financial institutions collect massive volumes of transactional data daily, making it impractical for human experts to review each transaction for signs of money laundering manually. AI systems, on the other hand, can efficiently process this data, flagging transactions that exhibit unusual patterns or deviate from established norms.
These AI systems utilise advanced algorithms to develop customer behavior profiles, creating a baseline against which future transactions can be compared. Any deviation from the norm, such as sudden large transfers, frequent cash deposits, or transactions with high-risk jurisdictions, triggers an alert for further investigation. This allows institutions to focus their resources on genuinely suspicious activities rather than drowning in false positives.
Analysing data to recognise suspicious activities: AI algorithms excel at analysing enormous
datasets, identifying hidden patterns and correlations that could signify money laundering activities.
By examining transaction history and customer behavior, AI-enabled tools can uncover links between seemingly unrelated events.
At the same time, Machine Learning(ML) allows AI systems to improve their performance
continuously. As new data is fed into the system, it learns from outcomes and adjusts its algorithms. This makes the AI-enabled platforms effective even in identifying new money laundering tactics.
Assessing risk to avoid financial crimes: AI-enabled tools can assess the risk associated with customers and transactions in real-time. By factoring in various parameters such as transaction amounts, frequency, geographical locations, and historical data, AI systems can assign risk scores that help prioritise suspicious cases.
Similarly, AI can also contribute to Know Your Customer (KYC) processes, which are crucial for AML compliance. It achieves this by employing natural language processing, computer vision, and machine learning to authenticate identities and validate documents. This further minimises the risk of financial fraud.
Behavioral analysis to narrow down dodgy transactions: AI can be used to create individual
customer profiles based on their typical financial behavior. Any deviations from these patterns can trigger alerts for further investigation. For instance, if multiple high-value transactions are made from an account within a short span, an AI-based tool can identify the pattern and report it.
It will be analysed further in detail to rule out any suspicion. This approach is far more adaptive and accurate than traditional rule-based systems.
Strengthening financial security and regulatory compliance
Regulatory bodies play a crucial role in combatting money laundering, imposing guidelines and requirements that financial institutions must adhere to. AI-powered predictive analytics help institutions maintain compliance by identifying potential issues before they escalate into major violations. These systems assess historical data and market trends, predicting areas of vulnerability and suggesting preventive measures.
As per RBI regulations, NBFCs should maintain proper records of all cash transactions valued at more than INR 10 lakh, especially if they happen more than once a month. Herein, AI-enabled tools can streamline the process by real-time data analysis, helping maintain records. Also, it can streamline the process of reporting suspicious transactions to regulatory authorities. By automating the generation of accurate and timely reports, financial institutions can ensure that information reaches the appropriate agencies promptly, enabling more effective coordination and response to potential threats.
Way forward
As technology continues to advance, the role of AI in anti-money laundering efforts is poised to expand even further. Integrating ML models capable of continuous self-improvement will enable AI systems to adapt to evolving money laundering tactics. This adaptability ensures that AI remains effective in detecting new patterns of illegal activity.
Moreover, the collaboration between AI and human experts will be crucial. While this revolutionary technology can process data at an unprecedented scale, human expertise is necessary for contextual understanding and decision-making. AI systems can assist investigators by presenting relevant information, allowing them to focus on making informed judgments.
