20 use cases of Robotic Process Automation (RPA) in banking [Updated]

Robotic Process Automation (RPA) refers to the use of software robots, programmed to complete repetitive and labor-intensive tasks. It makes it ideal for numerous applications in banking, one of the most data-intensive industries that operate in a highly regulated market. RPA automates manual-labor-intensive processes and drastically reduces the need for traditionally repetitive manual work, data reconciliation, and transcription—up to 70 percent, so that human employees can focus on more complex banking operations work, human interaction, and decision-making.

RPA is a lower-cost, higher-productivity model, and it is currently disrupting all business-process-outsourcing models in all financial institutions, which are battling fierce competition, workflow disconnects, and erroneous reporting. The key to RPA’s success in banking is its process standardization on a large scale.

RPA is the next phase of technology that will help address several problems in the banking and financial services sectors. It is evolving as a highly effective way to help financial institutions support their digital transformation efforts. Some of the examples of back-office processes at banks and financial services that can be automated through RPA are as follows:

  • Fraud detection by account activity tracking
  • New account entry across systems and moving data and doing multiple entries
  • Account reconciliation, i.e., duplicating and moving data
  • Report generation across systems
  • E-form extraction by taking data forms and making system entries
  • Accrual support by making and updating entries
  • Mortgage approval, i.e., making calculations and moving data from place to place
  • Processing of credit card applications from web forms
  • Ten use cases of Robotic Process Automation (RPA) in banking:

1. Customer Service

As banks handle multiple queries ranging from bank fraud to account inquiry, loan inquiry, and so on, it is extremely difficult for the customer service team to address them in less time. RPA helps the low-priority queries to be resolved, freeing the customer service team to focus on high-priority questions. RPA also helps to reduce the amount of time it takes to check customer details from disparate onboard systems. The reduced waiting time and easy redress help banks improve their customer relationships.

2. Compliance

With so many rules, compliance is a challenging task for banks. RPA facilitates the adherence of banks to the standards. Seventy-three percent of the surveyed compliance officers believed that RPA could be a key enabler in compliance over the next three years, according to an Accenture 2016 survey. RPA helps to increase productivity by working 24/7 with fewer FTEs, improving compliance process quality, and increasing employee satisfaction by eliminating monotonous tasks and involving employees in jobs that require human intelligence.

3. Accounts Payable

Accounts Payable (AP) is a tedious process requiring the vendor to digitize invoices using Optical Character Recognition (OCR), extract, validate, and process information from all fields in the invoice. RPA helps to automate this process and credits the payment to the vendor’s account automatically after error and validation reconciliation.

4. Credit Card Processing

It took weeks earlier for a bank to validate and approve a customer’s credit card application. The long waiting period led to customer dissatisfaction, sometimes leading to the cancellation of the request by a customer. However, banks can now speed up the process of dispatching credit cards with the help of RPA. It only takes a few hours for RPA software to collect customer documents, conduct credit checks and background checks, and decide on whether or not the customer is eligible for a credit card based on set parameters. By using RPA, the whole process has been perfectly streamlined.

5. Mortgage Processing

Ideally, it takes between 50 and 53 days to close a mortgage loan in the United States. The process took time because different scrutiny checks, such as credit checks, job verification, and inspection, had to be carried out before approval. A minor error on the side of the customer or bank may slow down the process and result in unnecessary complications and delays. Based on set rules and algorithms, banks can now accelerate the process with RPA and clear the bottlenecks that delay the process.

6. Fraud Detection

One of the bank’s major concerns was the growing number of cases of fraud. The incidents of fraud have only multiplied with the advent of technology. Thus, checking every transaction and manually identifying fraud patterns becomes difficult for banks. RPA uses an ‘‘if-then’’ method for identifying potential fraud and flagging it to the department concerned. For instance, if multiple transactions are made in a short time, the RPA will identify the account and flag it for a potential threat. It helps the bank’s account scrutiny and fraud investigation.

7. KYC Process

Know Your Customer (KYC) in each bank is a critical process of compliance. The process is so critical that it involves at least 150 to 1,000+ FTEs to carry out customer checks, and according to Thomson Reuters, some banks are spending at least US$ 384 million per year on compliance with KYC. Considering the costs and resources involved in the process, banks are now beginning to use RPA to collect, screen, and validate customer data. It helps the banks complete the process with minimal errors and staff in a shorter duration.

8. General Ledger

Banks must ensure that all critical information, such as financial statements, assets, liabilities, revenue, and expenses, update their general ledger. This information is used to prepare the banks’ financial reports, which are then accessed by the public, the media, and other interested parties. It is vital to ensure that the general ledger is prepared without any error, considering the enormous amount of details required by disparate systems to create a financial statement. RPA comes to rescue here. It helps to collect, validate, and update information from different systems without any errors.

9. Report Automation

Banks must prepare a report on their different processes as part of compliance and present it to the board and other stakeholders to show the bank’s performance. Given how vital the reports are to the bank’s reputation, ensuring that there are no errors is essential. While there are systems for providing data and templates for presenting them in a digestible format, accurate data without error was what the banks required. RPA assists banks with actual data in preparing reports. It collects information from various sources, validates it, arranges it in an understandable format, and schedules it to be sent to multiple sources.

10. Account Closure Process

Banks receive several requests for monthly closure of accounts. Occasionally, the accounts can also be closed if the customer does not provide the proofs needed to run the account. The scope for human error also increases, considering the high volume of data handled by the bank each month and the checklist they need to adhere to. With RPA, banks can send customers automated reminders asking them to provide the required evidence. In the queue, it can also process the account closure requests in a short time with 100 percent accuracy based on set rules. RPA is programmed to cover exceptional scenarios, such as the closure of an account due to non-compliance with KYC. It makes it easier for the bank to focus on other, less monotonous functions that require more human intelligence.

New and Emerging Use Cases in 2024

11. Loan Processing and Underwriting

Loan processing involves extensive document verification, credit scoring, and underwriting, which can be time-consuming and error-prone when done manually. RPA can streamline this process by automatically gathering required documents, performing credit checks, and assessing risk based on predefined criteria. This not only speeds up loan approvals but also ensures higher accuracy and consistency in decision-making.

12. Customer Onboarding

Customer onboarding is a critical process that involves verifying customer identity, conducting background checks, and setting up accounts. RPA can automate the entire onboarding process by extracting and validating customer information from submitted documents, performing necessary checks, and updating relevant systems. This leads to faster onboarding times, reduced manual errors, and improved customer experience.

13. Trade Finance Operations

Trade finance involves complex procedures like document verification, compliance checks, and transaction processing. RPA can handle these tasks efficiently by automating the extraction and verification of trade documents, conducting compliance checks, and processing transactions. This reduces the turnaround time and operational risk, enhancing the overall efficiency of trade finance operations.

14. Anti-Money Laundering (AML)

AML compliance is crucial for banks to prevent financial crimes. RPA can assist in monitoring transactions for suspicious activities by applying predefined rules and algorithms to identify potential money laundering activities. It can flag suspicious transactions for further investigation, helping banks to comply with regulatory requirements and reduce the risk of financial crimes.

15. Treasury Operations

Treasury operations involve managing a bank’s liquidity, investments, and risk. RPA can automate repetitive tasks such as cash position monitoring, reconciliation of treasury transactions, and reporting. By providing real-time data and analytics, RPA enables treasury teams to make informed decisions and manage financial risks more effectively.

16. Data Analytics and Insights

With the increasing importance of data-driven decision-making, banks need to analyze vast amounts of data from various sources. RPA can automate data collection, aggregation, and preliminary analysis, providing valuable insights for strategic planning and operational improvements. This allows banks to leverage their data more effectively for better decision-making and competitive advantage.

17. Customer Feedback and Sentiment Analysis

Understanding customer sentiment and feedback is essential for improving banking services. RPA can automate the collection and analysis of customer feedback from various channels, such as surveys, social media, and emails. By processing this data and identifying key trends and sentiments, banks can make data-driven decisions to enhance customer satisfaction and loyalty.

18. Foreign Exchange (Forex) Transactions

Forex transactions require real-time processing and accuracy. RPA can automate the monitoring of forex rates, execution of trades, and settlement of transactions. This ensures that forex operations are carried out swiftly and accurately, reducing the risk of manual errors and improving operational efficiency.

19. ATM Management

Managing a network of ATMs involves tasks like cash replenishment, maintenance, and monitoring for issues. RPA can automate the scheduling of cash replenishments, monitor ATM performance, and alert relevant teams for maintenance. This ensures that ATMs are always operational and well-maintained, enhancing customer convenience and satisfaction.

20. Enhanced Personalization in Banking Services

Personalization is becoming increasingly important in banking. RPA can help banks analyze customer data and preferences to offer tailored banking products and services. By automating the analysis of customer behavior and transaction history, banks can provide personalized recommendations, targeted marketing, and customized financial solutions, improving customer engagement and loyalty.

By leveraging RPA across these diverse use cases, banks can significantly enhance their operational efficiency, reduce costs, improve compliance, and deliver a superior customer experience. As RPA technology continues to evolve, its potential applications in the banking sector will only expand, driving further innovation and transformation in the industry.