RPA in banking industry – benefits, use cases, and implementation

Published On October 25, 2020
In Banking Financial Services & Insurance, RPA, Blog Archives

Nowadays banks are increasingly turning towards new-age technologies to stay ahead in this highly competitive market. The transition from traditional banking to digital has taken a leap in the last few years and now, banks are concentrating their efforts to enhance the customer experience. RPA (robotic process automation) has been fundamental in increasing the efficiency and ROI without compromising the security. In this race towards digital transformation, RPA helps banks maintain their competitive advantage. 

What is RPA? 

Robotic process automation (or RPA) is a type of business process automation technology that is based on metaphorical software robots also known as “bots” or artificial intelligence (AI)/digital workers. This advanced technology today enables anyone to configure ‘bots’ by integrating human actions to execute business processes.  

Most of the people have had an experience with RPA – and they don’t even know that. For instance, the popping up of private messages while a user browses through a brand’s Facebook page. These messages are pre-programmed and are sent by a robot which is designed to answer queries/questions in Messenger – all without the intervention of any human employee. This allows the brand to answer the customer’s questions more promptly while ensuring that human employees focus on other important tasks. The repeated operational tasks are easily handled by bots, in turn helping the company serve their customers more efficiently.  

How can banks benefit from RPA 

RPA is helping companies in the BFSI industry engage with their customers in real time, enhancing efficiency and productivity. The pressing need for banks currently is to look for cost-effective and fast alternatives that can help them achieve short-term objectives while the big initiatives progress simultaneously. RPA has proved to be that alternative for both customer-facing as well as middle and back office functions. Although commoditization of banking services has prompted the banks to upgrade the front-end services and customer experience however, the backend operations have a huge potential to improve.  

Benefits of RPA in banking

The backend processes generally comprise of rule-driven, high volume repetitive tasks that are labor intensive hence, prone to errors. When these tasks are automated using RPA, they won’t require constant human intervention thereby helping human employees focus on higher value-added tasks. The best part about adopting RPA is that it requires minimal investment as it does not modify the underlying legacy IT infrastructure. Additionally, it helps to speed up the core processes, giving an instant boost to accuracy and productivity, helping lower costs and time-to-market for new services. 

There are several benefits of adopting RPA in banking sector. It is also important to highlight that the successful implementation of RPA requires extensive domain knowledge, technology experience and expertise and deep understanding of the tools with constant monitoring. It is always advisable for the banks to partner with the best RPA service providers to avoid the complexities related to RPA. By partnering with leading third-party RPA service providers, banks can ensure that the implementation of RPA is smooth and complication-free. Also, this helps to lower down the costs and effort. 

Some more benefits of using RPA in banking: 

  • Customer Service Level Agreements (SLAs) are improved 
  • Operational agility is raised 
  • Customer trust and loyalty is enhanced 
  • Revenue and cash flow are increased 
  • Processing time is reduced 
  • Customer churn is lowered 
  • Customer experience is enhanced 
  • Business response time is reduced 
  • Regulatory compliance is improved 
RPA in banking

Use cases of RPA in banking 

Several banks across the world are favouring the use of RPA in order to reduce human efforts and errors. The request processing TAT (Turnaround time) has reduced tremendously with the introduction of RPA, from days to minutes. Also, the cost of processing has also reduced by 30% (ComTec). Below are some use cases of RPA in banking: 

1. Customer experience and service 

Banks must deal with innumerable customer queries ranging from account enquiry to bank frauds or loan enquiry etc. daily, therefore it can get extremely cumbersome for the customer service team to address the queries within a short turnaround time. With RPA in place, banks can ensure that low priority queries are taken care of by the bots, helping the customer service team focus on high priority queries that require a human mind for analysis.  

Additionally, RPA helps to reduce the time taken for customer verification, mapping them with various details from different sources and onboarding them faster. This reduction in waiting time and easy redressal has helped banks improve their customer relations. 

2. Processing of credit cards 

Validation and approval of a credit card application took weeks a few years ago. Due to extended waiting periods there was a surge in customer dissatisfaction which also led to the cancellation of the request. However, with RPA’s help, banks now have the capability to speed up validation, approval and dispatching of the credit cards.  The RPA software takes a few hours to complete the entire process from gathering the documents from the customer, performing background and credit checks, and taking a decision based on set parameters to approve or disapprove the credit card request. RPA has streamlined the whole credit card processing wonderfully. 

3. Risk & compliance reporting 

With several compliance rules in action, complying with all the rules can become a hefty task for banks. Robotic process automation in banking has made it easier to comply with the rules. Also, banks must access several applications to churn out the required data for reporting. With the implementation of RPA in banking operations, the risk management capabilities are enhanced where going through multiple external websites, e-mail systems and broker statements can be automated to generate reports and pinpoint anomalies. By using RPA, banks can automate 90% of these tasks, in turn saving considerable amount of cost and time. 

RPA in banking - benefits and use cases

4. KYC (Know your customer) 

Being a data-intensive and rule-based process in every bank, KYC is one of the perfect use cases of RPA. According to Thomson Reuters, several banks spend approximately $384 million every year on KYC compliance. Due to such huge costs associated with the KYC process, banks are increasingly turning towards RPA to aggregate customer data, evaluate and validate it. This helps banks wrap up the process in shorter duration with less errors and employees. End-to-end digitizing of the entire KYC process is being done by many banks. 

5. Anti-money laundering (AML) and fraud detection 

Many banks across the world are automating manual process with RPA for inspecting questionable or suspicious transactions flagged by the AML systems. Fraudulent transactions have been one of the biggest concerns of banks and due to the proliferation of technology, such incidents have risen sharply. Hence, it is increasingly becoming difficult for the banks to monitor every transaction manually and identify fraud patterns. 

‘If-then’ method is used to determine potential frauds and raise a flag. For example: if multiple transactions are made in a short span of time, then RPA identifies it as a potential threat and highlights the case for further investigation. 

6. Mortgage processing 

A budding area for digital and automation disruption as well as transformation – mortgage processing is a time taking activity where the application must travel through various scrutiny checks before getting approved. In the United States, it takes around 50-53 days to process a mortgage loan (ComTec). A small error by the bank or customer can result in slowing of the process, also adding unnecessary complications. With RPA in place, banks can easily accelerate the process and clear any bottlenecks that interrupt the process. 

How to implement RPA in banks 

Factors that are crucial for successfully implementing RPA in banks: 

  1. Assessment: Detailed analysis should be conducted in order to list down various processes that are suitable for RPA. A list of operational issues being faced which can be solved through RPA should be made. It’s feasibility and impact must be analyzed. 
  1. Strong use case: Cost and efficiency gains delivered by RPA must be documented. This will act as a proof that will demonstrate the benefits that RPA is delivering in terms of cost, effort, time, efficiency and utilization of resources. 
  1. Execution strategy: Banks must formulate a well-defined execution strategy based on their goals. Accordingly, key stakeholders to manage the execution must be identified.  
  1. Choose the right service provider: It is very crucial to choose the right RPA service provider that can provide end-to-end implementation starting from ideation, solution, definition, planning, execution, and support. 

Conclusion 

The BFSI industry is being hit by a wave of innovation. A ‘virtual workforce’ of bots or robots can effectively transform the banking functions without modifying the underlying infrastructure. Banks are now capable of handling processes faster and in an efficient manner through RPA. With multiple benefits of RPA in banking and finance, enhancement customer experience and gaining competitive edge will remain as one of the top priorities for banks. The digitization of manual tasks in banks has helped players in the BFSI sector increase operational agility and meet ever-evolving customer demands. 

Check out how SG Analytics can help leading banks maintain their competitive edge with end-to-end RPA solutions.

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Nupur Verma
Nupur Verma
About the Author

Nupur is a Digital Marketing Professional with a demonstrated history of working in Research and IT industry. She is skilled in social media marketing, brand strategy, email marketing, inbound marketing, SEO, Google Analytics. She is also ardent about content writing and graphic designing.

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