The Banking and Financial industry is seen to be growing exponentially over the past few years with the implementation of technological advancements resulting in faster, more secure, and reliable services. To remain competitive in an increasingly saturated market – especially with the more widespread adoption of virtual banking – banking firms have had to find a way to deliver the best possible user experience to their customers. As per Gartner, the pandemic has catalyzed the business initiatives to adapt to the demands of employees and customers and make digital options the future of banking services.
Table of Contents
- What is RPA in Banking?
- Why RPA is Important in Banking?
- Top 13 RPA Use Cases & Examples in Banking in 2023
- How to Implement RPA in Banking?
- Implement RPA in Banking with AutomationEdge
What is RPA in Banking?
RPA in banking industry can be leveraged to automate multiple time-consuming, repetitive processes like account opening, KYC process, customer services, and many others. Using RPA in banking operations not only streamlines the process efficiency but also enables banking organizations to make sure that cost is reduced and the process is executed at an efficient time. According to reports, RPA in banking sector is expected to reach $1.12 billion by 2025. Also, by leveraging AI technology in conjunction with RPA, the banking industry can implement automation in the complex decision-making banking process like fraud detection, and anti-money laundering.
Why RPA is Important in Banking?
Bank employees deal with voluminous data from customers and manual processes are prone to errors. With huge data extraction and manual processing of banking operations lead to errors.
Moreover, a single error in the important banking process leads to the case of theft, fraud, and money laundering case. Instead of humans processing data manually, simple validation of customer information from 2 systems can take seconds instead of minutes with bots. Introducing bots for such manual processes can reduce processing costs by 30% to 70%. Several processes in the banks can be automated to free up the manpower to work on more critical tasks.
Top 13 RPA Use Cases in Banking
RPA has a plethora of different applications in the BFSI segment to free up the manpower to work on more critical tasks. Some of these processes include:
Banks deal with multiple queries every day ranging from account information to application status to balance information. It becomes difficult for banks to respond to queries with a low turnaround time.
Over 80% of customers who have used chatbots for product inquiries in the last 12 months wouldn’t want to use them again—and 46% said that they’d prefer to use branches (Deloitte).
RPA can automate such rule-based processes to respond to queries in real-time and reduce turnaround time to seconds, freeing up human resources for more critical tasks
With the help of artificial intelligence, RPA can also resolve queries that need decision-making. By using NLP, Chatbot Automation enables bots to understand the natural language of chatting with customers and respond like humans.
Banking being the center of the economy is closely governed and needs to adhere to many compliances. According to an Accenture survey in 2016, 73% of respondents believed that RPA can be a key enabler in compliance. RPA increases productivity with 24/7 availability and the highest accuracy improving the quality of the compliance process.
Accounts payable is a simple but monotonous process in the banking system. It requires extracting vendor information, validating it, and then processing the payment. This does not require any intelligence making it the perfect case for RPA.
Robotic Process Automation with the help of optical character recognition (OCR) solutions can solve this problem. OCR can read the vendor information from the digital copy physical form and provide information to the RPA system. RPA will validate the information with the information in the system and process the payment. If any error occurs, RPA can notify the executive for resolution.
Credit Card Processing
Traditional credit card application processing used to take weeks to validate the customer information and approve credit cards. The long waiting period was dissatisfaction to customers and cost to banks. However, with the help of RPA, banks now can process the application within hours. RPA can talk to multiple systems simultaneously to validate the information like required documents, background checks, credit checks and take the decision based on rules to approve or disapprove the application.
In the United States, it takes approx. 50 to 53 days to process a mortgage loan. Process of approving a mortgage loan goes through various checks like credit checks, repayment history, employment verification, and inspection. A minor error can slow down the process. As the process is based on a specific set of rules and checks, RPA can accelerate the process and clear the bottleneck to reduce the processing time to minutes from days.
With the introduction of digital systems, one of the major concerns of banks is fraud. It is really difficult for banks to track all the transactions to flag the possible fraud transaction. Whereas RPA can track the transactions and raise the flag for possible fraud transaction patterns in real-time reducing the delay in response. In certain cases, RPA can prevent fraud by blocking accounts and stopping transactions.
Know Your Customer (KYC) is a mandatory process for banks for every customer. This process includes 500 to 1000+ FTEs to perform necessary checks on the customers. According to Thomson Reuters, banks spend more than $384 million per year on KYC process compliance.
Considering the cost of the manual process, banks have started using RPA to validate customer data. With increased accuracy, banks no longer have to worry about the FTEs and the process can be completed with minimal errors and staff.
The banks must keep the general ledger updated with information like financial statements, revenue, assets, liabilities, expenses, and revenue which is used to prepare financial statements. Financial statements are the public documents that are then accessed by the public, stakeholders, and media. Considering the amount of detailed information in the statement, errors in the report can very badly affect the bank’s image.
To create the statement, the bank needs to update information from the multiple legacy systems as these systems cannot integrate, verify it and make sure that the general ledger is prepared with no errors. With this amount of data from multiple systems, it is bound to have errors. Here comes RPA to the rescue. RPA is independent of the technology and can integrate data from multiple legacy systems to present in the required format even if the data in the systems are not in the same format. This reduces the huge amount of data handling and time.
Like all other public companies, banks need to prepare reports and present them to their stakeholders to show their performance. Considering the importance of the report, there is no chance for the bank to make an error.
While RPA systems provide data in multiple formats, they can create reports by auto-filling the available report format to create reports without errors and minimum time
Account Closure Process
With such a huge number of customers, it is supposed to get some account closure requests monthly. There can be various reasons for the account closures and one of them is when a client has failed to provide the mandatory documents.
With Robotic Process Automation, it is easy to track such accounts, send automated notifications, and schedule calls for the required document submissions. RPA can also help banks to close accounts in exceptional scenarios like customers failing to provide KYC documents.
Underwriting is the process of assessing the risk of financial transactions such as bond issues, bank loans, and insurance policies. Collecting data from multiple systems and analyzing them before entering into the system requires huge manual effort and efficiency as well and processing them manually is a tiring and time-consuming process. Here RPA can play a better role in automating the underwriting process. Automated underwriting processes in banking enable taking loan-related decisions based on algorithms rather than relying on inhuman beings.RPA in underwriting removes the risk of manual error, and misinterpretation of loan risks, and takes care of biases while having decisions.
Cash Collection and Deposits
Cash collection and deposits is another challenge that banking and financial organization often struggle with. Collecting tasks from multiple points of sales and migrating them to different branches accurately is an erroneous process. Instead of giving humans the task of maintaining the data records, RPA in banking can take care of all the records coming from multiple sources, and integrate them into a centralized system for easy access and sharing. Also, the security of transactions is maintained and there are no risks of money theft and alert in case of any fraudulent activity.
Account Origination Process
Account origination is a time-consuming process ranging from application and underwriting to disbursal of funds. Banking service desk staff has to go through multiple steps of origination processes like pre-qualification documentation, the application process, the underwriting process, credit decision, quality check, and initiating loan funding.
Implementing RPA in this process removes the need for data collection and removes the errors all across the process and enables faster loan processing and meets the regulatory compliances and rules.
Banks can do more with less human resources and rip the financial benefits with RPA. A survey in the financial section by PricewaterhouseCoopers shows that 30% of the respondents were not only experimenting with RPA but were on the way to adopting it enterprise-wide.
[Also Read:What is Robotic Process Automation (RPA) in Healthcare? Use Cases, Benefits, and Challenges in 2023]
How to Implement RPA in Banking?
Once the Automation Roadmap is ready, financial institutes can go for ‘Proof of Concept’ in which the business benefits can be demonstrated and the automation approach can be refined.
For effective RPA implementation, Banking organizations can follow the below steps:
- Identify the finance areas for automation
- Develop a multifaceted automation roadmap for implementation
- Identify, evaluate, and partner with the right providers to support the design and implementation
- Build an enterprise-wide delivery model and governance strategy to help the global business
- Organize training sessions and design a change management strategy to drive effective RPA adoption
Implement RPA in Banking with AutomationEdge
Being an automation solution provider for multiple industries, AutomationEdge has scaled multiple banking and financial services providers in accelerating their business process efficiency and workplace experience. For example- one of our clients HDFC bank had been facing huge challenges in process inconsistency and a high rate of errors that were leading to lower revenue and higher operational costs. To process a single loan application through HDFC bank processing time was 40 minutes. But leveraging the AutomationEdge RPA solution made the process a lot simple and helped the banking staff t bring down the time spent on a loan application from 40 minutes to 20 minutes.