Identifying, analyzing, and responding appropriately to risks is vital to any company, especially one that handles customer data, like the finance and banking industry. As government oversight and market competition are constantly and rapidly increasing, there has been a great percentage of risk and compliance technology adoption in the financial sector over the last few years. Given these challenges, how are organizations taking action to ensure they don’t risk falling foul of the regulators – and experiencing reputational and financial damage as a result? Middle-and back-office operations across many industries are quickly transformed by Robotic process automation (RPA).
Have you ever needed support regarding decision-making and new insights extracted from complex data? Intelligent- automation solutions can help your business to achieve that efficiency and effectiveness gains. Here are 4 ways to leverage RPA in compliance process activity that can lead to many benefits, including expanded resource capacity, improved outcomes through greater quality and consistency, reduced remediation efforts, and lowered costs.
But first, let’s take a closer look at the Risk and Compliance business model:
Compliance and Risk management teams are at the forefront of strategies undertaken by financial services institutions to transform and grow their businesses: Compliance with established rules and regulations helps protect organizations from various risks, while risk management helps protect organizations from risks that could lead to non-compliance—a risk itself.
While the volume and complexity of regulation across the globe continue to gather pace, banks and other financial institutions need to respond accordingly. Thus, there is an abundance of tasks across risk and compliance operations that can be automated using robotics.
Suspicious activity alert investigation
In day-to-day activity, the transaction monitoring systems generate many alerts that must be investigated to identify the suspicious activity and its cause. Financial institutions have to screen millions of customers and transactions continuously. Most of the screening platforms produce numerous alerts, of which a large percentage of the alerts are false in nature. A lot of time and effort is spent investigating and managing these alerts generated. All these aspects of the processes for researching and resolving anti-money laundering alerts are manual or semi-automated in nature and are, therefore, conducive to RPA.
Attended robots are dependable partners in alert investigations because they can scan through a customer’s history in seconds. Once an alert is detected, an automation program combining attended and unattended robots enable a quick review of all history. Then, the information can be reviewed by a human fraud analyst trained in nuanced instances of mistrust.
Refocus employee activities on higher-value activities
Traditionally, a portfolio of individual tests was executed by compliance teams to determine if their operations were compliant with specific rules, laws, and regulations and then adjust internal policies accordingly. Banking compliance was already a significant challenge for financial institutions before the pandemic began. Today, the one thing we are absolutely certain about is that the increasing economic uncertainty has sparked major industry changes around regulation and customer interaction. Furthermore, as more customers seek financial support and guidance, banks must respond promptly and gracefully to the demand.
In order to improve employee engagement, RPA can be applied to repetitive and manually intensive activities like monitoring and testing. Consequently, employee activities can be refocused on activities that require creativity and critical thinking, such as judgment-based monitoring and testing and quality assurance.
Internal and external reporting
In most financial companies, the process of data gathering and creating internal and external regulatory reports is very manual. Data gathering and consolidation is a lengthy and tedious process, which needs to leave adequate time for reviews, adversely impacting the accuracy and quality of these reports. With RPA taking over the data assimilation process, business teams will be better able to focus on analyzing and reviewing reports.
Scalability
The correct RPA automation tool would be agile and mitigate risks by enabling the systems to make room for any new change and deal with complexity. The digitized workforce is becoming dynamic that is ready for change and growth. With the help of RPA, a harmonious framework would be created that allows the established frameworks to allow for the employment of maintaining consistency through bots.
Finally, the first step in leveraging automation to meet the challenges of modern financial institutions is understanding that not all automation programs are created equally. In industries such as banking, decision-making on AI applications can support functions overseeing analytics risk assessment, such as model-risk management and the analysis they perform. Feel free to turn to a specialized RPA implementation company to help you take the right steps to automate your business processes.
