RPA 101 – Applications of RPA in Banking Industry

Robotic Process Automation (RPA in banking) is a rule-based software solution that automates repetitive tasks without any self-learning capabilities. It is not inherently artificial intelligence. RPA vendors now offer AI tools as add-ons to their automation platforms, including RPA applications in banking where some form of AI, such as computer vision or natural language processing, is a part of the automation workflow.

In the banking and financial industries, which involve large-scale manual workforces, RPA in banking has been used with the aim of saving costs, time, and human effort. For instance, banks have used document processing automation software to automatically retrieve information from external auditors or correct formatting and data mistakes in incoming funds transfer requests.

According to a report by McKinsey & Company, banks could automate up to 60% of their processes via RPA, significantly enhancing efficiency and productivity (source).

1. Benefits of RPA in Banking

Banking and financial institutions have been known for their long-lived traditional processes, which affected overall productivity and customer satisfaction levels. Although these businesses have already been trying to upgrade the process, applying RPA in banking creates further opportunities to automate and shorten complicated processes, as well as to assist many other developments in this angle:

1.1. RPA fuels digital transformation

Digital transformation could be achieved via different approaches. One of them is RPA in banking, which helps to automate both the bank’s front and back-office business processes, achieving greater operating efficiencies. Specifically, RPA in banking will affect digital transformation in banking (or vice versa) by four major dimensions: People, Processes, Platforms, and Products.

1.1.1. People

One of the critical factors of any transformation is people. New processes could be significantly affected by the number of FTE headcounts (full-time equivalent) needed to perform them and could change the requirements for staff competencies. In addition, changes in the business processes could be designed and developed only with the active encouragement of senior managers, experts, and developers.

Using RPA in banking to automate processes allows banks to:

• Reduce and/or optimize the FTE number.
• Improve employee culture. The processes most suitable for RPA in banking are typically the most exhausting and least enjoyed. When employees are freed from them, they can refocus on more rewarding and higher-value activities.
• Decrease dependency on the human factor. Robots are programmed to follow rules, and they do not make typos, mistakes, or forget to do tasks.

1.1.2. Processes

The goal of any business transformation—digital or otherwise—is always to deliver a better customer experience. Reviewing, simplifying, and optimizing business processes is the main step of any transformation.

RPA in banking can help banks to:

• Optimize processes and reduce hidden wastage. According to a study from Deloitte, banks that adopt RPA can see up to a 30% reduction in operational costs (source).
• Decrease costs and increase profitability.
• Reduce cycle times and improve throughput. Software robots are designed to perform tasks faster than a person can.
• Increase the quality of process monitoring. The tasks performed by a software robot can be monitored and recorded at every step, producing valuable data and an audit trail that can support further process improvement.
• Help collect data and follow regulations connected with compliance.

1.1.3. Platform

Successful business transformation that delivers a better customer experience can only occur with the support of modern platform tools. It is a comprehensive task to integrate new technologies with the many legacy systems that are still parts of the bank’s IT infrastructure.

RPA in banking has several advantages compared to other approaches for the transformation of business processes (for example, Business Processes Modeling (BPM) or IT transformation):

• RPA could be integrated faster and easier with the existing software infrastructure.
• This approach is very flexible and can be altered or updated quickly to adapt to the changing world. Robots can be rapidly reassigned when other, more important processes arise, as each robot can typically perform many types of processes.
• It has high scalability. Once a process has been defined as a series of instructions that a software robot can execute, it can be scheduled for a particular time, and as many robots as required can be quickly deployed to perform it.

1.1.4. Products

Digital transformation could support new products, software development service delivery, or modification of core products. Banks’ digital products must be designed within the context of the other digital properties that their customers engage with daily.

Using RPA in banking allows banks to:

• Increase the quality of services. According to a survey by the International Data Corporation (IDC), 75% of organizations reported faster service delivery after implementing RPA (source).
• Implement new services or refine existing ones. Better data collection and optimization of processes can form the base of significant changes in core business processes.

1.2. RPA helps smooth credit card businesses

Credit cards constitute a major component of the banking business. With several countries pushing for a cashless economy, the share of the payment card business is expected to grow significantly. Fierce competition and a thin spread mean that success depends on the ability to handle volumes efficiently. Therefore, process automation—or RPA in banking specifically—will play a crucial role in enhancing both efficiency and profitability in the credit card domain.
There are lots of opportunities for RPA to benefit in this matter:

If you want to know more about RPA in other industries, check out our latest research about RPA in Retail, rpa in healthcare, rpa in manufacturing, rpa in telecom and how does robotic process automation work.

2. Use cases of RPA in Banking

Since RPA in banking can be applied to various business process automation projects, there are numerous well-defined use cases in this space. Here are some of them:

2.1. ATM testing

A global bank deployed an ATM testing robot to automate the test cases that were previously conducted manually. The robot came with five components:

• Vision system for screen reading and identifying keyboard numbers, card slots, cash, and receipt identification.
• High dexterity robotic arm to reach all areas of ATM operations.
• End effector to handle multiple cards as well as perform keyboard, cash, and receipt operations.
• Processing unit.
• Defect logging engine that interfaces with the defect management system.

The robot tested various aspects, including the screen, keypad, card dispensing mechanism, and other functionalities, to deliver up to 80% cost and time savings.

2.2.  Transaction processing and sweep operations

For a bank, robots could be deployed in transaction processing and sweep operations. This eliminated manual efforts, reducing turnaround time (TAT) by 30 to 35% and significantly improving accuracy, which increased productivity by 20% while also reducing FTE resources.

2.3. Automatic report generation

Generating compliance reports for fraudulent transactions in the form of Suspicious Activity Reports (SARs) is a regular requirement at banks and financial institutions. Conventionally, compliance officers are required to read reports manually and fill in necessary details. RPA technology, combined with natural language generation capabilities, can read lengthy compliance documents, extract required information, and file the SARs, saving time and reducing operational costs effectively.

2.4. Know your customer (KYC) and Anti-Money laundering (AML)

Both KYC and AML are extremely data-intensive processes, making them most suitable for RPA in banking. According to a report by Accenture, implementing RPA in KYC processes can lead to a 50% reduction in time spent on compliance activities (source). Whether automating manual processes or catching suspicious transactions, RPA implementation proves instrumental in saving time and costs compared to traditional banking solutions.

2.5. Customer onboarding

Customer onboarding in banks is typically a lengthy process, primarily due to several documents requiring manual verification. RPA can simplify this by capturing data from KYC documents using Optical Character Recognition (OCR). This data can then be matched against the information provided by the customer. If no discrepancies exist, the data is automatically entered into the customer management portal, helping avoid manual errors and save time.

2.6. Account opening

With RPA in banking, the cumbersome account opening process becomes more straightforward, quicker, and accurate. Automation eliminates data transcription errors between the core banking system and new account requests, thus enhancing overall data quality.

2.7. Mortgage lending

Lending is a critical service area for financial institutions. Given that mortgage lending is process-driven and time-consuming, it is highly suitable for RPA automation. RPA technology can be utilized for handling critical tasks like loan initiation and document processing efficiently.

2.8. Loan processing

Loan processing can be slow, but with RPA, it can be accelerated, bringing processing times down to a record 10–15 minutes.

2.9. Optical character recognition (OCR)

RPA platforms can integrate intelligent OCR solutions that assist banks in converting handwritten forms into corresponding electronic formats. For example, a customer submitted a handwritten KYC form, which would ordinarily require manual transcription. With RPA, the form can be scanned, and AI-enhanced software automatically reads through it, replicating it in digital forms, thus saving time and costs for banks.

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