RPA in Banking: Definition, Benefits & How to Implement


In today’s highly competitive banking and financial sector, particularly with the growing presence of FinTech and virtual banking solutions, constant adaptation, competitiveness, and the delivery of exceptional customer experiences are required by banks and financial institutions. These institutions are subjected to intense pressure to have costs reduced, productivity increased, and issues like talent shortages, streamlined processes, and rising personnel costs addressed. Consequently, Robotic Process Automation (RPA) is being utilized by many banks and financial institutions to address these issues. In this blog, various aspects of RPA in Banking and its benefits will be examined.

What is RPA in Banking?

In the banking industry, a wide range of time-consuming and repetitive tasks, such as account opening, KYC, customer service, and many others, can be automated using RPA. The utilization of RPA in banking operations not only results in improved process efficiency but also enables banking organizations to reduce costs while achieving more efficient process completion. It is anticipated that RPA in the banking sector will reach $1.12 billion by 2025 according to reports. Furthermore, complex decision-making processes like fraud detection and anti-money laundering can be automated

Why RPA is Important in Banking?

Employees at banks handle a lot of consumer data, and manual procedures are prone to mistakes. When significant volumes of data are extracted and manually processed during banking operations, errors happen. Furthermore, a single error in an important banking process can result in a theft, fraud, or money laundering case. Bots can validate customer information from two systems in seconds rather than minutes, replacing the need for humans to process data manually. Using bots for such manual processes can cut processing costs by 30% to 70%. Several banking processes can be automated to free up manpower for more critical tasks.

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RPA Use-Cases In Banking

  • Loan processing: Loan processing has long been regarded as a time-consuming and inefficient process. Although the bank has partially automated the process, RPA speeds it up even more, reducing processing time to a record 10-15 minutes.
  • Know your customer (KYC) and Anti-Money Laundering (AML): The fact that KYC and AML are both extremely data-intensive processes makes them ideal for RPA. Whether automating manual processes or detecting suspicious banking transactions, RPA implementation proved beneficial in terms of time and cost savings when compared to traditional banking solutions.
  • Fraud Detection: With the implementation of digital systems, one of the primary concerns for banks is fraud. It is extremely difficult for banks to track all transactions and identify potential fraudulent transactions. Whereas RPA can track transactions and flag potential fraud patterns in real-time, reducing response time. In some cases, RPA can prevent fraud by blocking accounts and halting transactions.
  • Automatic Report Generation: Compliance reports for fraudulent transactions, known as suspicious activity reports or SARs, are required by banks and financial institutions regularly. Traditionally, compliance officers are expected to manually read all reports and fill out the necessary details on the SAR form. This results in a highly repetitive task that requires a significant amount of time and effort. RPA technology, which includes natural language generation capabilities, can read through these lengthy compliance documents before extracting the necessary information and filing the SAR. For best results, the RPA software can be trained using compliance officers’ inputs on which parts of each document best fit each section of the report.
  • Customer onboarding: The process of onboarding new customers in banks can take a long time because so many documents need to be manually verified. By utilizing optical character recognition (OCR) to extract data from KYC documents, the process can be greatly simplified by RPA. Subsequently, the information provided by the customer on the form can be contrasted with this data. If no discrepancies are found during the automated matching process, the data is automatically entered into the customer management portal. RPA automation in customer onboarding not only helps to avoid manual errors but also saves time and effort for employees.
  • Cash Collection and Deposits: Cash collection and deposits are another issue that banks and financial institutions frequently face. Collecting tasks from multiple points of sale and accurately migrating them to different branches is an incorrect procedure. RPA in banking manages all data records from multiple sources, consolidates them into a centralized system for easy access and sharing, and maintains transaction security to prevent money theft and send alerts in case of fraudulent activity.
  • The account closure procedure: Due to the volume of clients, account closure requests are anticipated each month. Closure of accounts can be due to several reasons, including the failure of clients to submit the necessary paperwork. Tracking of these accounts, scheduling of calls for document submissions, and sending of automated notifications are easily handled with robotic process automation (RPA). In rare situations, such as when clients neglect to submit KYC paperwork, account closures can also be assisted by RPA.
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Benefits of RPA In Banking & Finance

  • Scalability: Robots’ high scalability enables you to manage high volumes during peak business hours by adding more robots and responding to any situation in record time. Additionally, by relieving staff members of repetitive duties, RPA implementation allows banks to concentrate more on creative business growth strategies.
  • Increased operational efficiency: Once properly configured, banks and financial institutions can make their processes much faster, more productive, and more efficient.
  • Cost-effectiveness: The banking industry, like any other, relies heavily on cost-cutting measures. Banks and financial institutions can expect to save 25-50% on processing time and cost.
  • Risk and compliance reporting: RPA in banking aids in the generation of complete audit trails for every process, thereby reducing business risk and maintaining high process compliance.
  • Faster implementation: With Robotic Process Automation (RPA) tools that use drag-and-drop technology to automate banking processes, it is simple to implement and maintain automation workflows with no (or minimal) coding required.
  • Business growth with legacy data: With RPA implementation, banks and the financial services industry use both legacy and new data to bridge the gap between processes. This type of initiation and availability of critical data in a single system enables banks to generate faster and better reports for business growth.

How to Implement RPA in Banking?

  • Identify financial areas for automation.
  • Create a multifaceted automation roadmap for implementation.
  • Identify, evaluate, and collaborate with the appropriate providers to support design and implementation.
  • Create an enterprise-wide delivery model and governance strategy to support the global business.
  • Organize training sessions and develop a change management strategy to promote effective RPA adoption.
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Q1. What is the complete form for an RPA developer?

Ans. Often referred to as software robotics, robotic process automation (RPA) uses intelligent automation technologies to carry out data extraction, form filling, file moving, and other repetitive office tasks that are performed by human workers.

Q2. What is the payment procedure for RPAs?

Ans. At this stage of the accounts payable process, the use of RPA allows for better management of invoice approval and PO matching. When used in conjunction with intelligent document processing solutions, data can be extracted from any type of document by RPA and routed to the appropriate person.

Q3. How do banks use RPA?

Ans. Nevertheless, banks can now process the application in a matter of hours thanks to RPA. RPA can communicate with several systems at once to verify data, including background checks, credit checks, and necessary documentation. It can then decide whether to approve or deny an application based on predetermined criteria.

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