Using Robotic Process Automation to Change AML Compliance

What exactly is robotic process automation? RPA is a software technique that makes it simple to design, deploy, and manage software robots that mimic human movements while dealing with digital systems and software. Despite the use of digital technology for a variety of procedures and services, most financial institutions continue to rely on a massive back-office with manually completed transactions backed by basic and common rules-based processes. The usage of heterogeneous technological programs that are rigid and frequently lack integration capabilities makes operations more laborious, costly, and ineffective. AML compliance, which is mostly a rear-office function, falls into this section and is now predominantly manual, despite the implementation of AML programs.

According to Booz Allen Hamilton (April 2016 study), financial organizations have increased the headcount in their AML operations by 500% over the last three years. According to the research, AML experts often spent only 10% of their time on analysis. Most of their effort, almost 75%, goes towards data collecting, with the remaining 15% going into data arrangement and input.

Investigating Automation Opportunities in the AML Investigation Process

Anomalies in client activity can be tracked based on KYC profiles and transaction information, which are generated as alerts by the AML transaction monitoring platform. The monitoring criteria can take into account a variety of customer-related factors. As a result, the traditional procedure starts with evaluating previous alert information, downloading relevant data from numerous systems, and manually examining the underlying contract details, KYC profiles of the individuals, and the transactional counterparties. In addition, public domain searches are conducted to see whether there is any unfavorable or disparaging information about the customers or their counterparties in relation to the transactions under examination. Watch-list tools are used for negative screening. The majority of the activities above are required, and the results are put into an investigative summary report.

An alert is raised if the transaction in issue is judged suspicious, or it is closed if there is no unusual behavior.

This entire procedure is done by hand. The full investigation procedure can take between 30 and 45 minutes, depending on the intricacy of the alert, the bank's requirements, the availability of information in the core systems, and the analyst's degree of experience.

The scenario is more complicated because the industry average for false positives in AML signals is 90 - 95 percent, implying that financial institutions spend a significant amount of time and effort on non-critical warnings. The manual mistake might also result in regulatory fines.

As a result, financial institutions must consider automating the AML investigation process. This will be advantageous on 2 faces: (a) false positive reduction and (b) manual effort removal.

The industry has made some headway on the first point, with a variety of AML analytics systems being installed at various global financial services businesses, and the number of false positives has been significantly reduced. These cutting-edge solutions may be built on top of an organization's current AML infrastructure. The analytics models are continually learning and improving the transaction behavior profile of each client and customer category, eliminating false positives.

On the second front, financial institutions may use robotic process automation in AML programs to increase operational efficiency, flexibility, and overall performance of back-end procedures. In our partnerships with multiple major financial institutions, we have discovered that by automating repetitive operations, RPA may help an organization save up to 70% of AML investigation time. Furthermore, it can significantly increase the accuracy and efficiency of process execution. For example, intelligent workflow automation may integrate seamlessly with all internal and external data sources to review, evaluate, and create transaction analysis reports (using analytics methods), decreasing the need for manual involvement in the AML investigation process.

It is usually best to apply RPA in stages, which is why financial organizations should begin by automating only a few phases rather than the full process. This too can bring about visible results. For example, just automating data collection from different source systems and presenting it to the investigator through a single sign-on will result in a 10 to 15-minute reduction in investigation time - no small feat.

With 'responsible innovation' emerging as the guiding principle, financial services businesses are striving to harness FinTech to optimize procedures in order to fulfill clients' shifting expectations successfully. Given the possibilities, RPA provides, particularly in the regulatory compliance function, banks and financial services organizations throughout the world have begun large-scale implementation initiatives. This is exacerbated further by the fact that AML compliance costs are excessive, and robotic process automation solutions can provide much-needed relief on both fronts — timely compliance and cost efficiency.

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