The financial industry has always been immune to innovations. However, the accelerated pace of digital transformation and the rise of tech-savviness among consumers require banking to evolve exponentially. As financial companies grow, their services become more complex. Here is where RPA (robotic processes automation) can become an effective problem solver with its software automation techniques that allow streamlining time-heavy operations, reducing organizational costs, reducing or even eliminating human error. RPA combines robotic automation and artificial intelligence to automate human activities in banking, which may include data entry and simple customer service communications.
RPA has already re-shaped the back-end of many banking processes, and financial companies continue exploring how they can deploy this technology at large. It has given back time to employees for more complex tasks, while AI handles all back-end operations. Consistency, speed, cost-effectiveness, and scalability are among the primary benefits of robotic process automation.
According to the Gartner analysis, the industry is expected to spend $2 billion on RPA in 2021, and the companies that have already embraced RPA are now seeing a higher business performance with most of their operations streamlined, processes automatically updated, and more effective communication within their digital infrastructure. As Accenture reported: “With robotics, you automate and build an automation platform for your front office, back office, and support functions.”
RPA Use Cases in Banking and Finance
Customer onboarding is a time-consuming process that often requires the manual verification of documents and setting up a customer profile across multiple internal systems and platforms. RPA can simplify this process by capturing the customer information from KYC documents, using optical character recognition (OCR). This data can be compared against the information provided by a customer in a form, and if there are no disparities detected, it can be automatically entered into a customer profile. RPA in customer onboarding allows banks to provide more accuracy and save a lot of time for employees.
A similar algorithm is applied in account opening, making this process much more straightforward and faster. Banks can use RPA to extract customer data from the input forms and use it in various host applications. The automation also eliminates data transcription errors by matching the information from the core banking system with new account opening requests. Over time, the integration of RPA into account opening processes can enhance the quality of data in banking systems.
RPA is particularly efficient for lending-based activities, as it provides maximum transparency and visibility of every task across the process. With RPA, an entire lending process is electronic, which means that financial companies can collect and manage data at every stage of the journey. It also allows finding the necessary information in their document management base and checking a process status within minutes. While loan processing has always been considered a very tedious process, now it can be delivered within approximately 10-15 minutes with the help of RPA.
RPA is predicted to have a large impact on the compliance operations of banks, including the know your customer (KYC), anti-money laundering (AML), and fraud detection. All of these processes are known as extremely data-intensive, which makes them particularly suitable for RPA integration. It allows automating significant chunks of these activities, reducing risks associated with a human factor, and increasing efficiency. With the fast-growing volumes of customer data, it becomes challenging for banks to detect and prevent frauds on time. With the ability to process information across various streams and sources simultaneously, RPA greatly improves the fraud protection mechanisms of financial systems.
Credit card processing
In traditional banking systems, processing a credit card application usually takes weeks, as it requires validating customer data, checking background, exploring the credit history, and verifying information from various sources. With the help of RPA, this becomes way easier since all the information is simultaneously extracted from multiple systems, matched, and verified within hours, and the decision on the credit card approval is taken on the basis of clearly defined rules. So the entire process of credit card approval runs faster. It benefits both sides of the table, contributing to higher customer satisfaction and bank performance.
In the United States, a process of mortgage loan approval usually goes through a range of operations like employment verification, credit checks, repayment history, and other inspections. A minor mistake may slow down the process. RPA allows financial companies to automate crucial processes associated with mortgage lending, such as loan initiation, document processing, quality control, and financial comparisons. A repetitive and time-consuming process of mortgage lending is now being transformed into a fully automated workflow, where the human intervention is minimized and the efficiency is maximized. It results in a quicker mortgage approval process and unloading employees from tons of work performed manually before, so as they can focus on more complicated and high-value tasks.
Like all public companies, banks have to prepare regular reports to keep all stakeholders in the know of the business performance. Considering the significance of a report, there is no chance for banks or financial institutions to make a minor mistake. RPA can eliminate the risk of human error by filling all the fields of the report automatically. It enables banks to speed up monthly reporting and deliver more accurate information to their stakeholders.
Examples of the RPA implementation in banking
Example #1. OCBC Bank
OCBC is a banking and financial services corporation headquartered in Singapore. The bank reported that they used RPA to automate the home loans repricing. The result was fascinating. OCBC reportedly reduced the time of this process from 45 minutes of human labor to only 1 minute with the help of RPA.
Example #2. DBS Bank
DBS is another multinational bank based in Singapore. The bank partnered with IBM to scale an enterprise-wide Centre of Excellence (COE) using RPA. Just 5 months after the COE was set up, DBS Bank experienced a tremendous rise in performance. As reported, they succeeded in automating and optimizing more than 50 complicated business processes.
Example #3. Sumitomo Mitsui
Sumitomo Mitsui is one of the largest multinational banks in Japan. This financial institution operates in retail, corporate, and investment banking segments worldwide. The integration of robotic process automation into banking services has allowed this Japanese bank to cut out 400,000 hours of manual labor for employees.
What’s Next for RPA in Retail Banking?
Robotic process automation is becoming a hot topic in finance. There are two innovative technologies that will play a crucial role in the further development of RPA: artificial intelligence (AI) and machine learning (ML). Although RPA has been mainly used for back-end applications in the banking industry before, there is something for customers too. The integration of RPA with AI and machine learning can enable banks to reimagine customer experience from a new perspective - a conversational one. Voice assistants are becoming more popular in financial services. They can provide basic guidance for customers and increase their overall satisfaction with financial products.
To increase the accessibility of financial services, we at Hennii created a personal financial assistant powered by conversational AI. We believe that these technological advancements can truly revolutionize finance and address modern customer needs. Join our waitlist to stay updated about when we launch.