Application of Artificial Intelligence and Machine Learning to Anti-Money Laundering
21 December 2020
Anti Money Laundering (AML) is a set of rules, regulations, and practices intended to prevent the offenders from unlawfully obtaining funds as legal income. Money laundering has turned out to be the principal source of compliance penalties for numerous Financial Institutions (FI’s). Since 2011, FI’s have immensely invested in Anti Money Laundering Regulation programs with the intention to Combat Financing of Terrorism, Assess the Money Laundering, and Monitor the Risk.
Yet the FI’s still face complications with the manual procedures and meeting the compliance regulations. AML Compliance Process in FI’s reduces the need to have an excessive amount of workforce, funds, and wealth to handle the operations and meet the regulations.
Challenges with Manual Procedure
Increased costs - As your business grows, the number of transactions and cases will increase, and you will have to increase your investment in maintaining the appropriate level of supervision
Manual compliance is prone to fatigue and cannot operate at peak efficacy for long hours
Manual procedure lacks adaptivity and continuity
For low capacity, FI’s Manual guidance can be productive for some point of time, but beyond that, transactions with huge volume will cause additional charges and a higher rate of error and fault.
To overcome the problems stated above, FI’s are looking for a way out. Solutions that are based on AI/ML can help the FIs in this regard. For example, AI/ML competence could be powered to relieve false positives, and enhance the feature, which benefits in saving substantial costs, increase productivity at any scale with better reliability and quicker process.
AI/ML can visualize risks and threats associated with any transactions or customers through behavioral trends that add context. Moreover, AI/ ML and advanced automation substitute the manually intensive process of the AML with insights that point towards money laundering.
Implementation of AI/ML will require:
Determining and learning the transaction movement of customers with similar behaviors as well as pinpointing the irregular and unusual ones.
Learning and understanding money laundering, scam, and terrorist financing categories and identify category-specific frauds
Learning connections between the alerts that generated confirmed-suspicious transactions with those that caused false positive alarms
Constantly evaluating false-positive alarms and learning common predictors
Many FI’s have begun to implement process automation in the form of RPA and see AI/ML as the subsequent move in the journey to better effectiveness. AI/ML technologies can efficiently enhance, automate, and boost AML efforts. Further, these technologies can scale to manage and handle the enormous volume, speed, and variety of data that is produced by today’s FI’s and combat money laundering. For banks and FI’s, it is now time to adopt AI/ML into their networks and systems. AI/ML technology proposes genuine solutions to decreasing threats associated with financial offenses, scams, compliance, regulations, and money laundering.
Authors: Santhosh Srinivasa, Tejas Shetty, Vaishnavi Bhat, Banking and Financial Services, EVRY India