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Follow Anti Money Laundering (AML) guidelines, the ING Vysya way

Anti Money Laundering (AML) guidelines have become necessary for BFSI sector companies. Follow ING Vysya Bank’s methodology to implement them in your company.

Money laundering is big business and a bigger problem. Financial institutions know it, which is why they believe it necessary to implement the Anti-Money Laundering (AML) guidelines. In this direction, blazing the trail is ING Vyasa Bank, which recognizes the need for investment in information systems in order to nail the launderers. And the Finance Action Task Force (ATF) has readily endorsed this view by issuing Standard Operating Procedures (SOPs) to combat money laundering as well as the attendant terrorist financing.

Awareness is spreading across the globe. In India, The Reserve Bank of India has made it mandatory for banks to adhere to the Know Your Customer (KYC) norm. The AML guidelines as stipulated under the Prevention of Money Laundering Act, 2002 (PML Act, 2002) make the banks, financial institutions, and intermediaries to  tighten up their regulatory scrutiny. Here again, ING Vyasa Bank is a front runner, employing measures and systems that are exemplary. So then, what exactly does the bank do?

Ashok Rao knows. Rao is a Chartered Accountant of renown and is Chief of Staff at ING Vyasa Bank, handling legal, compliance, AML and fraud monitoring and investigation portfolios. With over three decades of experience in banking, finance and insurance, Rao is also a certified Information System Auditor and has his task well cut out. But the methodology involves encompassing the guidelines and contextualizing with common as well as particular needs.

Awareness programs

“That’s how the bank began,” recalls Rao. “We started an AML implementation exercise through awareness programs for staff members. These programs covered areas including customer acceptance, transaction monitoring, stages of money laundering, economic sanctions risks, exit procedures, risks of non compliance, and penalties under PML Act, 2002 which were conducted on an ongoing basis to equip the operational users with the knowledge to nip the problem in the bud.”

Looking for patterns

With this groundwork, the next question was how and what should be monitored. Says Rao: “The AML guidelines required that scans be made through the daily gamut of transactions to locate unusual activities like a sudden spike in the transaction amounts, and different modes of operating the account such as shifting from branch banking to internet banking or activating and using an account which was lying inoperative for a while.”

Accordingly, potential cases were identified by using rule-based or parameterized software, with built-in, predetermined thresholds. Events that met the rules/ set parameters reflected as alerts. These alerts were disseminated to the first line of business.

Neural technology

To follow the AML guidelines around fraud analysis, the bank deployed a neural network technology tool that could identify complex schemes/ transactions. The bank’s focus was also on increasing the operational efficiency of the staff. Rao recalls that an integrated, and customized neural network scoring engine was seen as a solution that could assure regulatory compliance, an increased level of protection, and help in managing high volumes of transactions. He is happy to note that  the bank has witnessed a growth in transaction volume due to the introduction of various alternate payment channels such as cross border remittances, mobile banking, internet banking, electronic fund transfer, automated teller machines. As to the neural network, Rao points out the key  features of the technology that ING Vysya looked for to help it adhere to AML guidelines:

 Customized scoring

The tool bought to meet the AML guidelines offered the advanced analytics of a neural network scoring engine with a customized scoring model. Says Rao: “This component uses advanced pattern recognition technology, to give users a highly effective way of detecting potential money laundering activity. For example, if a customer structures the transactions regularly below a threshold, say, Rs 50,000 to avoid quoting of PAN details, the engine can search the entire database to detect similar patterns and throws up alerts.”

The neural network compares the characteristics of a customer’s financial activity with the custom-model and records the patterns of behavior for each account holder. The neural network then assigns a score reflecting the degree of risk for each transaction. Investigators are also provided with rationale for the score to improve analysis.

Drill-down functions

The tool provides various drill-down functions to analyze data. It has a built-in ‘learning’ mechanism whereby the system learns or records the genuine behavior or pattern of transaction. This feature helps the investigator in reducing repetitive alerts or false positives.

The tool facilitates comprehensive monitoring and detection that helps identify unusual activity and offers a fast track to compliance. Automated monitoring of customer activity and a comprehensive investigation process is a natural first step to track the AML guidelines set by the organization, but today’s atmosphere also demands a 360º oversight of financial crime and compliance program activity to ensure business continuity and a high ROI.

Relational intelligence

The tool enables investigators to further analyze the transaction in relationship to the customer’s historical activity on all related accounts. AML guidelines around transaction monitoring involve matching an activity with a pre-recorded pattern or scenario. Accordingly, alerts are generated which are delivered to investigators via a user-friendly client interface.

Security precautions

The tool provides link analysis to investigate financial similarities between apparently unrelated accounts to detect money being moved across accounts. It helps in establishing connecting patterns in potentially fraudulent transactions by scanning a history of transactional data.

Rao’s observation is that the tool also provides secured access to information without compromising data access policies and AML guidelines. The ‘alerts distribution’ is automated and monitors the completion of work by investigators and invokes escalation procedures when necessary.

Supervisors are provided with management tools such as case management directory to manage the workflow among investigators for optimal performance. This includes escalation of the cases to the next higher level also. Further it should maintain a comprehensive audit of transactions and investigator actions to ensure compliance with the Bank’s anti-money laundering (AML) policies and procedures.

Challenges involved

Rao remembers the challenges that the bank had to deal with during the implementation of AML guidelines:  A lot of hand holding and awareness building activities were conducted to get the users scale up from the Excel way of reporting that they were accustomed to. Training was offered to end-users to use the features of the tool efficiently to see optimal results.  ING Vysya had to incorporate various reports and customize the tool to suit the requirements of the local governance. The bank conducted corporate awareness programs to get the employee buy-in to introduce internal transaction monitoring.

Benefits of guidelines

The implementation of AML guidelines has helped ING Vysya Bank in ways more than one. It helps the bank to meet a common standard for monitoring activity. Rao declares: “The AML guidelines have facilitated a risk-based approach and also provision for an investigative workflow and audit trail.”

 

 

This was last published in April 2011

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