Bank of the Future

Prepping for the Future: Transforming AML Compliance through Automation

 
April 5, 2019

In the last decade, a staggering $26 billion has been imposed in fines on global financial institutions for non-compliance with anti-money laundering (AML), know your customer (KYC), and sanctions regulations. A robust  process is critical to ensuring compliance with global AML regulatory norms that are in sync with bank’s AML policy and risk management process. This is easier said than done.

A typical AML process is complex and intensive. It involves the monitoring various factors relating to a customer’s aggregate transactions, and types and amount of transactions. It also includes checking historical alerts by accessing relevant data from multiple systems, and reviewing the underlying transaction details and the KYC details of the subjects and their transactional counterparties. In addition, it entails performing public domain searches for negative information related to customers and their counter-parties.

To make matters worse, most AML compliance programs are highly manual and time consuming. As banks prep for the future amid intensifying regulatory scrutiny, changing customer expectations, and emerging types of fraud, technology will be a critical enabler of new risk management and compliance techniques. Leveraging technology across training, quality, production, Request for Information (RFI) and calibration processes can boost AML delivery standards by improving speed and quality. Here’s how.

Create dynamic and targeted elearning programs: To be effective, AML regulatory training must not only be specific but also evolve with changing regulations. It must address areas such as  typologies, red-flags, crime and Suspicious Activity Report (SAR) trends, and  articulation in audit logs. Given the dynamic nature of the exercise, eLearning options are ideal for ensuring anytime-anywhere learning, real-time content updates and feedback, and  content stickiness. A generic eLearning platform will not suffice for this.  Such a platform must be customized to offer targeted and dynamic AML related content. Leading Financial Institutions such as Lloyds Bank, TSB, and The Co-operative Bank are great examples of organizations leveraging e-learning to quickly and cost-effectively impart training on compliance and risks.

Leverage AI to boost QA across compliance lifecycle: Thorough process documentation and effective feedback mechanisms are critical to identifying issues early on. Some of the additional ways of boosting quality include focusing on quality of output rather than quantity, validating larger number of sample checks, and deploying personalized training. To deliver on these mandates, financial institutions are shifting from independent testing towards more holistic assurance of business processes and outcomes. They are increasingly leveraging AI or cognitive abilities to automate and enable intelligent QA across the lifecycle by leveraging multiple data sources, and enabling defect prediction and sample optimization.  Global banks such as HSBC, for instance, are using AI to stay compliant with AML regulations, freeing up their employees from manually reviewing tons of data, and allowing them to focus on value creating activities.

Additionally, automating the process with simple accelerator tools can help reduce processing time and improve productivity. One such process step that lends itself to rules-based automation is the sorting of leading counter-parties and issuing of alerts based on the severity of risk. Similarly, banks can also automate transaction analysis review wherein a tool reviewing an alert collates data from multiple source systems, KYC systems, and public domain. This can help financial institutions quickly:

  • Review past alerts against a particular customer
  • Understand the extent of deviation in the alerts with respect to the customer’s KYC profile
  • Identify negative information available in the public domain, relationships, or transaction history with counter-parties in order to anticipate customer behavior.

Enable faster RFI through automation:  RFI is crucial for robust AML investigations in the case of missing information. It ensures that the front-office team asks the right questions to gain more clarity on the transactions. At a time when many banks are struggling to map the right relationship manager and expedite the RFI process, an automated RFI process can help the customer-facing teams quickly reach the correct recipient and track the response receipt.

Flexing the technology muscle for better compliance and risk outcomes

By 2025, banks’ compliance and risk management functions will be even more critical. Holistic transformation using technology will be key to delivering value through cost-effective and compliant operations that exceed customer expectations. As the AML industry matures, technology can help financial institutions maintain the efficiency of the AML program while utilizing fewer resources. However, the successful transformation of the AML framework requires designing an appropriate process and RPA integration road map along with a deep understanding of the kind of tools required. This calls for in-depth process assessment along with a detailed assessment of the current tools, applications, and systems landscape. FIs that drive technology-based AML transformation initiatives aligned with their business strategy and goals will drive better outcomes over both the short and the long term.

R. Srivatsan is part of the AML CoE of TCS’ Banking, Financial Services, and Insurance business unit. He has over 14 years of experience in banking operations and has worked with TCS’ leading clients in the BFSI space. Srivatsan holds a Master's degree in IT from Bharathidasan University, Trichy, India, and is a Certified CAMS (2009).