What are the Benefits of Robotic Process Automation in Banking Sector
Today’s customers expect easy and fast service, great personalization, and value for money. Financial institutions must meet these requirements while keeping costs down, all of which can be achieved through robotic process automation. RPA involves bots; bots are deployed to mimic day-to-day routine tasks that are performed using some business rules and can be easily automated. A large number of financial institutions have chosen RPA to meet these needs.
Benefits of RPA in Banking and Finance
The fact that bots are highly scalable allows you to manage large volumes of business during peak business hours by adding more bots and responding to any situation in record time.
In addition, the implementation of RPA enables banks to grow their business by freeing their employees from routine tasks to focus more on innovative strategies.
2. Improve operational efficiency
Once set up correctly, banks and financial institutions can make their processes faster, more efficient, and more efficient.
Similar to any other industry, cost savings are critical in banking. Banks and financial institutions can consider saving around 25–50% in processing time and costs.
4. Risk and Compliance Reporting
RPA in banking helps to generate a complete audit trail for every process, reducing business risk and maintaining a high degree of process compliance.
Whether you want to reduce human error or achieve high precision at low cost, robots can work 24×7 to complete their assigned tasks. So, reaffirming the ever-present availability.
6. Zero infrastructure costs
One of the benefits of RPA in financial services is that it does not require any major changes to the infrastructure due to its UI automation capabilities. In the case of cloud-based RPA, hardware and maintenance costs are further reduced.
7. Faster implementation
Automate banking processes with the drag-and-drop technology provided by RPA tools, making it very easy to implement and maintain automated workflows without any (or minimal) coding requirements.
8. Business growth from legacy data
With the implementation of RPA, the banking and financial services industries are using old and new data to bridge the gaps that exist between processes. This way of launching and providing essential data in one system enables banks to create faster and better reports for business growth.
RPA use cases in banking
There are a plethora of use cases that RPA in banking sector and financial sector. Here are some potential use cases for RPA in banking:
Banks need to deal with a wide variety of customer issues, from account inquiries to fraudulent transactions or loan-related inquiries. Dealing with issues in the shortest possible time every day is no small matter for the customer service team. If RPA exists, banks can ensure that bots can handle simple questions, freeing customer service teams to focus on more important inquiries that require human action. In addition to this, RPA helps to authenticate customers faster, provide them with sufficient information from various sources, and submit them quickly. This reduced wait time and flexible arrangements allow financial institutions to strengthen their relationships with customers.
Complying with too many restrictions can be a daunting and time-consuming task for banks. Robotic process automation makes compliance simple for banks. According to the study, 73% of compliance officers assured that RPA compliance will have a significant impact over the next three years. In addition to increasing productivity by eliminating tedious tasks and engaging employees in tasks that require human intelligence, RPA helps improve compliance by reducing costly FTEs and improving compliance quality.
Credit card processing
Traditional credit card application processing can take weeks to verify customer information and approve credit cards. Long waits often lead to customer dissatisfaction and bank fees. With the introduction of RPA, banks were able to process the application process within hours. Additionally, RPA can interact with multiple systems simultaneously to verify information such as basic documents, credit checks, and background checks, and make decisions based on rules for approving or denying applications.
Fraudulent activity has become a major concern in the banking industry, especially with the advent of digital systems. Monitoring all transactions makes it really difficult for banks to detect possible fraudulent activity. At the same time, RPA can monitor transactions to identify and pinpoint possible fraudulent transaction patterns, reducing delays in real-time responses. In some cases, RPA is able to block accounts and suspend transactions to deter fraud.
KYC (Know Your Customer) is another important and effective use case of RPA in the banking and finance industry. According to a survey conducted by Thomson Reuters, $384 million was spent on KYC tracking in a year. Considering the huge costs associated with the KYC process, banks are increasingly relying on RPA bots to collect, test and verify customer data. Robotic process automation in banking helps banks to complete the process in the shortest time with fewer errors and personnel.
A time-consuming and labor-intensive task for customers and employees in the banking sector is mortgage loan processing. Most banks in the US take close to 50–60 days to create and complete a mortgage processing loan. Before approving a mortgage, banks have to go through a number of processes, including credit verification, customer employment status verification, and checks. Small mistakes on the part of the customer or the bank can slow down the whole process. However, with the help of banks deploying RPA, the process has been accelerated. It follows predetermined rules and removes potential roadblocks to speed up mortgage processing.
Banks deal with massive amounts of data every day, constantly collecting and updating information on income, liabilities, expenses, and more. Banks use this information to prepare annual financial statements. The resulting financial reports are evaluated by the public media and other stakeholders to analyze the operations of significant organizations to determine whether they are meeting expected performance. Handling such huge amounts of data and preparing financial statements without error is definitely a daunting task for banks. Banks can quickly collect, update, and validate large amounts of information from multiple systems through RPA deployments.
Also Read: Cost of developing a banking app
Every month, the bank receives many customer account closure requests. Likewise, banks sometimes have to close customers’ accounts if they cannot show proof of payment. Robotic process automation helps banks send automated reminders to customers even if they forget to provide necessary proofs. RPA can queue and process account closure requests according to specific rules.
Some of the key benefits of entrusting such tasks to robots include cost savings; time savings, where RPA frees up time for employees to handle more complex tasks; reduction or even elimination of human error; Execute tasks quickly. What’s more, scalability means that automated solutions will cope with higher volumes and tasks will be delivered in record time. One such example is the account opening process, which is often repetitive, tedious, and unnecessarily time-consuming for employees. But with automation, these tasks can be done faster and more accurately. In the long run, RPA can significantly improve the integrity and quality of account data within a financial institution’s systems.
Account opening is just one of the many areas in the banking industry that could see a major transformation through RPA. In fact, according to its 2018 research, McKinsey found that the technology on display can “completely automate” 42 percent of financial activity and “mostly automate” another 19 percent. With turnaround time becoming one of the most important metrics for measuring overall customer experience, banks can now use bots to handle a variety of tasks related to areas such as accounts, loans, and fraud inquiries. Since customer service teams are currently dealing with such issues, using RPA as an alternative will free up a lot of time for these teams to focus on more important queries that require more intelligence and nuance.
Another important banking sector where RPA is now causing a huge shift is mortgages and lending. Given the number of routine processes involved in buying a home — employment verification, credit checks, title orders, and inspection reports, to name a few — RPA has become a prime candidate to take over many of these tasks without human intervention, significantly increasing the efficiency, reducing loan processing time and greatly reducing overall turnaround time. Oversea-Chinese Banking Corporation, for example, uses RPA extensively in this area, which has allowed the Singaporean bank to reduce the time it takes to reprice home loans from 45 minutes to 1 minute. OCBC’s RPA bot checks whether customers are eligible for repricing, recommends appropriate repricing options, and even drafts recommendation emails to customers. All of this means it can handle heavier workloads than before, handling up to 100 repricing applications per day.
Perhaps most notably, for loan-based activities, RPA manages to increase visibility into each specific task that needs to be performed as part of the overall process. “With workflow automation, every step of the loan process is electronic, meaning you can collect data through every step. Finding loan applications to know their current status is now a thing of the past,” Focused on RPA Digital Products Engineering firm RapidValue said. “You can search for documents in the document management system and instantly know their processing status.” As a result, it can greatly improve the overall lending experience for customers.
RPA will also have a significant impact on banks’ compliance activities, which is especially beneficial given the rising costs banks have inflicted on complying with increasingly stringent regulatory requirements over the past decade or so. In particular, RPA can eliminate the need for manual processes related to Know Your Customer (KYC) and Anti-Money Laundering (AML). Automating most of these important requirements will help minimize human error, reduce costs and greatly improve the efficiency of the onboarding process for new clients. Likewise, fraud detection will benefit from automation, especially given the rapid increase in the number of cases banks have faced in recent years, making the management of compliance teams more challenging. But with RPA, bots can be programmed to identify patterns of fraud and immediately escalate these incidents to the appropriate department within the bank.