The financial services industry has always been an early adopter of technology to enhance the efficiency of operations and customer satisfaction. In the 1960s, banks introduced ATMs. In the 1970s, electronic card-based payments arrived. With the new millennium came 24/7 online banking and then mobile banking. Not long afterward, financial institutions began implementing rule-based Robotic Process Automation (RPA) on a limited scale to handle repetitive, structured processes.
Today, RPA software bots are employed to handle various processes, including customer service, account onboarding, know your customer (KYC) compliance, loan and credit card processing, collections, fraud detection, accounts payable, and much more. The technology streamlines and accelerates processes to increase efficiency, reduces manual errors, strengthens compliance, and improves customer satisfaction. It can also boost productivity and improve employee satisfaction by freeing up resources to focus on more value-added activities.
Even so, many financial services companies have not yet fully realized all the potential benefits that RPA can offer. This underutilization comes at a time when competition from fintech, born of the digital technology revolution, is putting pressure on the industry to be more cost-effective and more in tune with customers’ desire for an anytime, anywhere personalized experience.
Obstacles to scaling automation
RPA can help financial institutions compete with the speed and agility of fintech. So what’s holding them back from scaling their RPA programs across the enterprise? Some of the barriers to deploying at scale include:
- Lack of executive/organizational support
- Decentralized automation program governance structure and no centralized control over bots in production
- Rule-based bots that are not designed for expanded use to conduct process discovery or intelligent document processing
- An RPA platform based on a legacy on-premises architecture and requiring IT programmers to use, resulting in a high cost of ownership
3 tips for success
Tip #1: Start small and scale fast. Just because one group is employing bots within an organization doesn’t mean everyone in the organization is familiar and comfortable with the technology. Organizations should start by conducting a simple proof of concept to get staff across the enterprise familiar with RPA and achieve a quick win. Then, the focus should quickly turn to larger opportunities to achieve scale.
Tip #2: Organize for success. No matter the size of an organization, it’s important to create a center of excellence (COE)—a team of people with key skill sets such as RPA and program management—to provide centralized governance/quality control, share best practices, and ensure change management. An organization should designate federated RPA automation leads in each business unit. This approach will empower the units to identify, prioritize, and design automation opportunities to accelerate scaling.
Tip #3: Choose a platform designed for scaling. RPA has evolved from offering only rule-based solutions for repetitive back-office processes to intelligent platforms offering easy-to-use integrated capabilities to automate workflows across the entire enterprise.
Some of the critical capabilities to look for include:
- A cloud-native, web-based solution. An RPA platform architected for the cloud can free up IT staff and an organization from legacy infrastructure and offer anytime, anywhere access. There’s no need to add and maintain on-premises equipment and incur related costs. And cloud-native RPA solutions can still seamlessly work with legacy systems. They also offer the flexibility to handle any workload—planned or unplanned—by scaling the Digital Workforce as required.
Even if an organization is not ready to move to the cloud, it can still reap the benefits of a cloud-native, web-based platform. The platform can help an on-premises operation lower its total cost of ownership for RPA and enable easier upgrades and microservices to facilitate scaling. Its web-based design can also enable rapid deployment and provide a user-friendly experience. In addition, the platform can help prepare an organization for a smooth migration when the move to the cloud occurs.
- An easy-to-use platform. An RPA solution that requires a programmer to develop and use bots can make scaling difficult. Most organizations, including financial institutions, have a limited number of programmers on staff, with little time for initiatives outside their daily duties.
A no-code/low code platform designed for easy development can remove that obstacle, allowing users to create bots for their processes without requiring programmers to be involved, aside from quality assurance. The platform enables subject-matter experts and business users to directly access bots, resulting in more opportunities to scale automation across the organization.
- Integrated capabilities. A platform that offers end-to-end processing capability with a diverse set of tools gives users the power to do more. Those tools can include intelligent document processing, process discovery, and real-time analytics.
Platforms with AI-based document processing capabilities allow users to employ bots to handle critical data from structured and unstructured documents of varying quality—something not possible with rule-based bots. For example, intelligent bots can digitize high volumes of documents and extract, index, and upload the customer data into a KYC/AML compliance management system to quickly assess risk.
Platforms with process discovery capabilities allow users to document workflows and identify automation opportunities across the enterprise, which also helps with scaling and process owner buy-in to the technology. And platforms that offer real-time, actionable intelligence can help organizations monitor their automation program and Digital Workforce to check performance on-demand.
By employing best practices for successful scaling and a cloud-native RPA platform, financial services organizations are empowered to break down the scalability wall. That can lead to improved operational efficiency, higher productivity, and the level of service that today’s customers expect from their financial institution to stay competitive.