RPA bots can use User Interfaces (UI) like humans and rely on rules-based programming for simple tasks and machine learning for more complex tasks like understanding documents. According to SSON Analytics, banking, financial services, and insurance are the top industries where RPA solutions are implemented. This article will mainly focus on the banking industry.

Data Source: SSON Analytics

RPA allows banks to run repetitive processes like data entry and customer service more accurately and effectively without overhauling existing systems. This will enable them to reduce costs, turnaround times, and manual mistakes while helping employees focus on high value-added activities. Top use cases include:

Financial products

Loan Processing and Validation

Loan processing includes

  • extracting relevant information from the documents submitted by the customer
  • combining this information with internal and external to prepare the necessary material for a loan decision
  • using machine learning or simpler statistical approaches to make a decision based on the available data

RPA bots can simplify data transfer between systems as loan processing includes input from multiple systems. For example, in a banking case study Blue Prism claims that its solution was used to built intermediary bots between the online system and the mainframe that used business logic to ask the user to fix incorrect entries, make the loan decisions, and generate confirmation letters.

In another example, Piraeus Bank leveraged process mining to improve its loan application processes and identify process steps to automate. QPR, a process mining vendor, claims that Piraeus Bank cut its lead-time by 85%. The company also reduced its loan processing from 35 minutes to 5 minutes by detecting and leveraging RPA opportunities.

Document automation systems can enable companies to auto extract data from customer’s documents.

Credit scoring models powered by machine learning can help financial institutions improve the accuracy of their loan decisions.

Trade Finance

Trade finance involves multiple parties coordinating and ensuring the delivery of goods and payments. Banks and companies communicate through letters of credit and other documents that need to be processed. WorkFusion provides an example where they mostly automated a trade finance application without writing an extensive rule set, relying on workers of the process to train the cognitive automation tool.

Account Closure

For the co-Operative Bank, account closure was lengthy and time-consuming. It required the manual cancellation of direct debits and standing orders, transfer of interest charges, and the transfer of funds from one account to another, etc. The bank automated the system with an RPA vendor so customer service agents can complete an electronic form over the phone, which is sent to a central mailbox where the RPA system processes it with no manual intervention.

Card Management

RPA can support processes like lost/stolen card replacement, charge reversals, card blocking decisions based on customer requests. As these processes are often repetitive, automation will reduce demands on employees, improve cycle times, and customer experience.

Same-Day Funds Transfers

The co-Operative Bank needed to complete payments using The Clearing House Automated Payment System (CHAPS), which offers same-day funds transfers. The manual process, which took 10 minutes per request, was automated and reduced to a few seconds of turn around time per request thanks to RPA bots. Process steps include checking for fund availability, performing the transfer to the point where manual authorization is required without error, charging the customer, and notifying the account.

Customer Service

Know Your Customer (KYC)

While dedicated KYC solutions are emerging, it is also possible to use RPA bots to automate portions of the KYC process. For edge cases that require human intervention, the case can be forwarded to an employee. For regular cases, RPA bots can speed up processing times, improve security and compliance, and reduce error rates for this customer-facing processes.

Customer ID Verification

Some companies have used RPA in their call centers to facilitate ID testing through a range of legacy core systems. RPA can bring all relevant customer service documents or account information to a single screen to allow client verification. This helps to improve the customer experience and the efficiency of call center operations.

Responding to Customer Requests

Conversational AI can be embedded into RPA bots. Conversational AI systems, also called chatbots, can help customers complete their requests automatically. If customer request is not clear, it is forwarded to a human for resolution.

To read more about chatbots, feel free to read our detailed guide.

Data Processing & Verification

Processes Involving Data and Verification Checks

RPA can help with verification tasks like searching for external databases to check information, including licenses and registrations. Businesses can speed up administration processes and improve SLAs this way.

Financial Reconciliation

RPA can compare data from multiple systems to ensure accuracy and identify any discrepancies. Businesses have more accurate data and reduce the extra tasks caused by unidentified errors in this way. Mindfield claims that they have reduced reconciliation processing time by 70% and saved around $100,000 annually with one of their partners.

Digitization of Structured Paper Forms

Businesses can benefit from document capture technologies integrated with RPA to automate processing of paper-based forms. You can read more about document capture from our in-depth guide.

Audit & Compliance


Banks need to reply to requests by the auditors for company audit reports. Bots have been used to find all the customer’s accounts year-end balances and return the audit to the audit clerk in the form of a Word document. This can reduce an average audit, which can take several hours to complete and an extensive audit that can take several days into an operation that can be completed in minutes.

Quality Assurance (QA) Processing

QA controls and audits have traditionally been manual and only looked at the portion of the portfolio. RPA can conduct QA tests on 100% of data that is prone to error or includes monetary payment, to detect anomalies. Thus, businesses can significantly reduce errors in important payment processes and improve customer satisfaction.

Regulatory Monitoring

RPA can be used to scan regulatory announcements for future changes, to catch changes early, or to access the latest updates as new information is released. As regulation is established, changes may not always be apparent. RPA can be used to cross-compare notifications to show what has improved. This significantly reduces the time spent on identifying regulations and decreases the possibility of noncompliance fines due to errors.


Trade Execution

In cases where legacy systems are not capable of storing complex limit orders, RPA bots could help. However, this is more of a band-aid case as in the long run, moving to a sophisticated and capable trading system would probably be a good investment given how it could improve trading and reduce the load of traders.

For more on RPA

If you want to read about RPA use cases under different industries, feel free to visit our detailed RPA use cases article with 61 RPA use cases/applications/examples.

To read specific case studies about these RPA application areas, visit our growing list of RPA case studies.

As the number of RPA use cases may demonstrate, RPA is used across a wide variety of areas. Our articles on RPA marketplaces and reusable RPA plugins/bots explain how reusable code reduces development time in RPA implementation.

To learn more about RPA, you can read:

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