Regulatory standards play an important role in safeguarding consumers, financial institutions and the global financial industry. As regulatory scrutiny in areas such as anti-money laundering (AML) and Counter-Terrorism Financing (CTF) continues to grow, so does the opportunity to streamline and enhance compliance efforts to tackle financial crime more efficiently and effectively.
In a notable example, financial institutions are turning to automated solutions, specifically robotic process automation (RPA), to perform routine compliance tasks quickly and accurately in a cost-effective manner. RPA can be configured to fully automate high-volume tasks like data entry and transaction processing, or even partially automate AML compliance processes such as suspicious activity investigation, customer onboarding, and client screening, to reduce workloads for employees. This capacity to optimise data generation and interpretation while streamlining compliance has made RPA a particularly desirable tool for financial institutions’ regulatory departments.
Opportunities for automation
There are several processes related to AML compliance that are particularly suited to automation. First among these is the identification of suspicious activity via transaction monitoring. Transaction monitoring is a perfect candidate for RPA because it involves standardised and repetitive tasks. When a transaction is identified as suspicious an alert is generated for analyst investigation. As RPA cannot completely replace analyst knowledge, its purpose is to optimize the workload of human analysts by lowering the number of false positives they must investigate, allowing them to more efficiently identify actual fraud.
During customer onboarding, automated solutions can be utilized as part of Know Your Customer (KYC) document gathering and validation processes. The automation of customer onboarding can take diﬀerent forms, from automated document capture to automated identity veriﬁcation. RPA can be used to collate data from disparate internal systems or from external sources such as regulatory agencies and open databases, including company registers used to identify beneﬁcial owners.
Finally, for client screening, ﬁrms are using RPA in Open Source Intelligence (OSINT) research. In this application, robots automatically navigate and collect live data from any website, in multiple languages, in the present moment as well as into the future as sources change. RPA can ﬁnd content hidden within the deep web and navigate complex menu structures, using diﬀerent crawl techniques to access the volume of sites needed during early investigative work and then to pinpoint the precise information required as this work proceeds.
RPA is worth the investment
Crime monitoring and screening processes traditionally require employees to compile and evaluate large and unstructured datasets. RPA is flexible, straightforward to implement, and can be designed by regulatory departments to follow specific logic parameters to analyse and interpret this data quickly. RPA also enables internal control of these processes, as bots’ actions are saved in log files that can be accessed and reviewed by the institution at any time.
The speed, accuracy, and efficiency with which RPA can be used to gather and aggregate data from different sources can increase the effectiveness of regulatory and risk reporting. A successful deployment of RPA can also free up employees to direct their expertise towards analysis-based tasks, such as judgement-based monitoring and higher value reporting. Furthermore, RPA technology is not limited to large institutions. Its scalable nature enables adaptation to business environments of different sizes and dynamics, helping institutions prepare for regulatory audits with less employee input required.
Automated solutions reduce the need for repetitive, deterministic, and manual tasks, presenting clear benefits. Observing this, many financial institutions are already seeking to enhance those benefits by combining RPA with more advanced machine learning (ML) technologies, such as intelligent automation (IA). This will result in further simplified interactions, an increase of speed, and the enablement of better enforcement of regulation.
IA bots offer an elevated level of process accuracy and speed by eliminating the need for error-prone manual data entry. They can centralise reporting data into an institution’s desired format, increasing auditability and significantly decreasing time and resources expended by compliance teams.
Combatting challenges ahead
Although the benefits of automated AML solutions are clear, widespread implementation will require financial institutions to address a few challenges.
Documentation of infrastructure and standards for existing manual compliance processes must be thorough in order to identify suitable opportunities for automation. Many financial institutions may lack this information and would therefore need to expend more human resources gathering requirements before the automation implementation process can begin.
Successful integration of RPA and IA involves a cultural shift in favour of automation. Financial institutions often overlook robotics as a long-term solution for compliance, instead viewing it as a tactical quick fix for specific issues. Further, a lack of understanding amongst senior executives on how these systems work and the benefits attached can make permanently adopting automation a complex, time-consuming, and difficult process.
RPA and IA integration could cause friction within a financial institution among employees, management, and human resources. Automation goes hand-in-hand with vigilance and scrutiny on the part of employees, and detecting and solving technical problems requires thorough training on human-robot collaboration. Without training and a corresponding willingness to adapt, small issues can materialise into delays and service interruptions which counter the original goals of automation.
It’s important to note that RPA is unlikely to fully replace underlying business systems or existing jobs. Instead, automation allows organisations to concentrate employees’ time and capabilities towards higher value-added and more investigative tasks.
The role of the regulators
Regulators are increasingly showing measured but encouraging approval for automated solutions, recognising the potential improvements that recent innovation can bring to compliance processes. Regulators themselves are implementing RPA to bolster compliance via enhanced monitoring and analysis of potentially suspicious transactions. Despite this affirmation, ambiguity and distrust over the utility of IA and RPA persist, potentially illuminating a need for industry-wide updates and clarification as these technologies continue to emerge.
The lasting importance of the role AML departments play within financial institutions to protect risk and reputational damage cannot be understated. Given Bank of England Governor Andrew Bailey’s 2020 endorsement of innovation in the financial sector, robotic technologies like RPA and intelligent automation (IA) may represent a powerful solution to modern AML legislation and the cost of AML compliance.
The UK’s Financial Conduct Authority (FCA) also noted the importance of automation in a 2019 speech by its Executive Director of Strategy and Competition, Christopher Woolard, entitled “The Future of Regulation”. Mr Woolard recognised that innovation had gathered pace in the regulatory sphere, underlined that automation had the capacity to bridge the gap between customers and providers, and said that it was, therefore, of utmost importance for regulators to adapt to the times, including by digitising many of their analogue processes.
Adopting automated technologies like RPA and IA to combat crime has the potential to generate exponential value for financial institutions. The benefits of reduced time spent on manual repetitive tasks, enhanced efficiency and accuracy of data processing, and automatic monitoring of suspicious activity, demonstrate how automated solutions can help financial institutions tackle financial crime more effectively in a properly regulated environment. With clear-cut, coherent regulatory guidelines, as well as appropriate internal culture and governance within financial institutions, RPA and IA can lead to a more efficient industry altogether.