Globally and in India, managing financial crime has been a major challenge. Regulators across the world have introduced several guidelines and taken measures to detect, prevent and tackle money laundering activities. The Indian banking industry has experienced considerable challenges in adhering to and complying with the financial crime guidelines on account of the new patterns the guidelines are evolving on a continuous basis for carrying out money laundering activities that remain undetected by the traditional rule-based system. Also, there have been instances of non-compliance, identified by the regulator (RBI); resulting in massive monetary fines and reputational damage. This paper covers current practices adopted by Indian banks to prevent financial crime and fraud risk management. It provides a holistic view of the prescribed measures (by RBI) to avert money laundering and financial fraud. The focus of this paper is to provide an overview of the risk-based AML principles and how artificial intelligence (AI) and machine learning (ML) could be leveraged for monitoring transactions and complying with the guidelines in an effective and efficient manner.
Know-Your-Customer (KYC) and anti-money laundering (AML) are in the limelight globally since some large banks were hit with hefty penalties in 2012. Regulators in the United States and Europe have imposed $342B in fines on banks since 2009 for misconduct, including violation of AML rules, and that is likely to top $450B by 2023. Despite several analytical platforms, tools and applications being available for AML transaction monitoring, there has been an increasing incident of penalties levied on the banks in India and abroad for non-compliance with the AML guidelines1.
In 2016, the Reserve Bank of India (RBI) had imposed INR 270M in fines on 13 Indian banks for violating KYC norms and 8 other banks were advised to put appropriate measures in place, and had reviewed from time to time to ensure strict compliance of KYC requirements2. As of August 2019, RBI has imposed INR 265M in fines on banks for non-compliance with its directions relating to opening/ operating of accounts and end-use monitoring of funds. This has triggered a flurry of initiatives across the banking sector to boost compliance both in India and abroad.
Most of the financial institutions (FIs) rely on a system of rules and procedures targeted for acquiring knowledge about their customer and their activities. However, money launderers have come out with alternatives to cover their activities, that a traditional rule-based system might not be capable enough to detect. It results in non-detection/ non-reporting of the suspicious transactions; leading to non-compliance with the AML/KYC regulations. This may result in both financial and reputation loss. Therefore, it is of utmost importance for banks to establish a reliable set of controls, allowing them to identify monetary activities and transactions even when the money launderers are using the best of their ability to circumvent the rules. One of the promising ways is to use an AI & ML driven AML transaction monitoring system.
2.0 Traditional process – Anti-money Laundering (AML) transaction monitoring
The traditional process performs routine scans for transactions based on pre-defined rules and flags that meet the criteria of those rules for the purpose of further investigation. These rules generally fall in the following categories of scenarios:
- Anomalies in behavior
- Transaction patterns
- Hidden relationships
- Credit, debit and bank cards
- High risk entities
Alerts generated out of these rules are then investigated by a bank’s operations team, which is set up for conducting a detailed review/ investigation. Based on the outcome of the investigation, they are either closed considering false positives or reported as suspicious transactions.
2.1 Drawbacks of the traditional AML transaction monitoring
- Increased operational cost: Per industry estimates, as much as 90 to 95% of the alerts generated by the traditional AML system are false positives3. This requires substantial workforce to analyze such alerts and take subsequent action. Each alert needs careful analysis, as any non-compliance in identification and reporting of the suspicious transaction can lead to huge financial and reputational loss to the bank. Thus, there is a need for adequate staffing depending upon the bank’s size and volume of alerts generated.
- Lack of AML system capability to detect new scenarios: Considering the fast evolution and new number of ways / techniques emerging for money laundering by fraudsters, there is an urgent need to constantly evaluate the transactions and external factors and identify new scenarios. Any delay in the detection mechanism would result in the system’s inability to detect the suspicious transaction; thus resulting in non-compliance of the regulatory prescribed guidelines.
- Identification of new scenarios: These are identified based on the human analysis of the past events. This requires strenuous manual work, yet there are chances that some scenarios are missed out or not completely captured due to lack of robust data analysis.
Despite the efforts taken by the banks, the level of undetected suspicious transaction remains high. Thus, to overcome various shortcomings, banks are evolving from the traditional approach towards advanced approaches involving AI & ML enabled technology for transaction monitoring and complying with the stipulated AML guidelines.
3.0 Contemporary fraud risk models: Leveraging AI & ML
AI & ML facilitate continuous advancement of computing through exposure to new scenarios, testing and adaption, while employing pattern and trend detection for improved decisions in subsequent (though not identical) situations. Also, ML includes techniques, approach and tools that produce actionable insights from the data that can be used by an investigator for further investigation, which can lead to reporting a suspicious transaction or identifying a new fraud pattern. This enables the capability to update the rules to identify and monitor suspicious transactions on a real time basis.