Robotic process automation in banking:
use cases and adoption tips

Robotic process automation in banking: use cases and adoption tips

October 16, 2023

Kate Aleksandrovich

by Kate Aleksandrovich,

Head of RPA Center of Excellence

RPA in banking means using advanced business process automation tools to automate many mundane and repetitive tasks, allowing employees to focus on more value-adding and customer-centric activities. In a nutshell, RPA emulates human actions interacting with the software while exponentially increasing efficiency.

Banks now actively turn to robotic process automation experts to streamline operations, stay afloat, and outpace rivals. We help to figure out the most potent use cases for robotic process automation in banking, outline real-life RPA application examples, define the implementation mindset, and provide tips on adopting the technology in your business.

additional AI value for the global banking industry annually


the estimated global RPA and Hyperautomation market size in 2027

Research and Markets

the estimated RPA services and software market size in 2025


Top 10 RPA use cases in banking

Now let’s figure out some of the most potent RPA in banking use cases:

Customer onboarding

Banks deal with an avalanche of regulatory requirements when onboarding new clients. On top of gathering personal and financial data, bank employees verify that data through approved governmental organizations, set up an account, and establish data archiving and monitoring processes. An RPA system can automate most of these processes, significantly decreasing operational costs, risks, and the time it takes to onboard a new client.

Onboarding request

3–6 weeks


Document gathering

1–4 weeks


Background verification

2–4 weeks


Credit terms setup

1–3 weeks


Agreement management

1–3 weeks


Account setup

1–2 weeks


Tracking % data archiving


+ recurring costs

Analytics & cross-selling


+ recurring costs

Scheme title: Challenges in customer onboarding lifecycle
Data source: — Automation in onboarding and ongoing servicing of commercial banking clients

Regulatory compliance

The financial industry remains one of the most heavily regulated ones in the world. In addition to a wide array of reports, banks must also perform post-trade compliance checks and compute expected credit loss (ECL) frequently. On top of that, compliance officers spend nearly 15% of their time tracking changes in regulatory requirements. RPA bots can automatically gather data from disparate sources, including federal bodies, government websites, and news outlets, and input this information into a bank’s internal system following data structuring guidelines. It can dramatically speed up the process and decrease its costs.

Loan processing

Essentially, the loan processing volume is capped by the number of employees dedicated to the task. Besides customer service automation, RPA technology in banking can bring real value by automating many loan administration processes, including underwriting and validation. RPA solutions allow for the autonomous consolidation of relevant information from paper-based documents, third-party systems, and service providers. On top of that, RPA tools can also enter this data into the appropriate systems for underwriters' further analysis.

Customer service

Customer satisfaction is one of the most significant benchmarks of any business with banks being no exception. Given that the majority of customer requests require some sort of simple data retrieval, RPA in the banking sector can considerably decrease the time it takes to process low-priority customer queries and almost eliminate the need for human intervention in many cases. For example, checking account balances, initiating urgent account blockage, checking mortgage loan application status, or simple loan inquiry processes can be completed via RPA-powered chatbots.

Accounts payable

Many invoices still arrive as paper documents, and there is little to no document standardization. Therefore, accounts payable remains a notoriously monotonous process that requires a lot of mindless copy-pasting. Retrieving vendor data, checking for mistakes, and initiating the payment – are all rule-based processes that organizations can do without human involvement. RPA software augmented with optical character recognition (OCR), can automatically capture and re-enter data while simultaneously providing an audit trail. It also significantly simplifies compliance reporting.

Credit card processing

While the general digitization of banking services has accelerated the issuance of credit cards, the process still requires human support. In most cases, an RPA bot can approve credit card applications by itself, substantially quickening the process and increasing customer satisfaction. An RPA bot can access various systems to verify applicants’ identity, perform background checks, and approve, disapprove, or, in rare cases, direct customers to a human employee.

Fraud detection

Banks have vast amounts of customer data that are highly sensitive and vulnerable to cyberattacks. There are many machine learning-based anomaly detection systems, and RPA-enabled fraud detection systems have proven to be effective.

Instead of relying on human judgment and largely manual data manipulation, banks can apply RPA tools to continuously monitor customer transactions, detect anomalies based on an elaborate rule-based system, flag them as potentially fraudulent, and send alerts to human employees for further review. Rather than spending valuable time gathering data, employees can apply their cognitive abilities where they are truly needed.

Know your customer

The ever-strengthening regulatory scrutiny around know-your-customer (KYC) and anti-money laundering (AML) standards and rising compliance costs, encourages banks to turn to automation. In many cases, banks are reluctant toward KYC process automation, because the cost of revamping a well-established web of many connected, yet disparate systems is often unjustifiable. The appeal of RPA systems is that they can be seamlessly integrated into existing systems and cause minimal disruption to the ongoing workflows. RPA automates rule-based processes such as setting up, validating, gathering, and compiling customer data.

Account closure

Account closure also entails a large amount of sequential and often predictable activities like sending emails regarding customer documents, validating a bank’s records (for example, verifying that the relevant check cashing agreement is in place), and updating data in the internal system. All these rule-based processes can be automated by RPA, allowing employees to focus on more valuable and cognitively-demanding tasks.

General ledger

Tedious and repetitive account reconciliation is a perfect candidate for RPA-enabled transformation. Especially for mid-sized and large banks, overseeing and updating financial statements, assets, liabilities, and expenses in disparate legacy systems is time-consuming and error-prone. Banks can shift most of these responsibilities to the RPA and let bots automatically gather data from multiple systems, validate payments, verify loans, and reconcile general ledger accounts.

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How to implement RPA for your bank

There are several important steps to consider before starting RPA implementation in your organization.

RPA implementation tips

Conduct a detailed assessment, choosing the right use cases

Vendor selection

Evaluate RPA vendor’s ability to meet all your requirements


Build a comprehensive execution framework

Choose right use cases

Selecting the right processes for RPA is one of the major prerequisites for success. Banks have thousands of repetitive processes for potential RPA automation, and relying on intuition rather than objective analysis to select use cases can be detrimental. Selecting use cases comes down to a company-wide assessment of all the banking processes based on a clearly defined set of criteria.

Below we provide an exemplary framework for assessing processes for automation feasibility. The processes above a cutoff point can be selected for automation.

Process assessment framework

Candidate processesVolume of transactionsWhat is the volume of transactions that require manual intervention?RepetitivenessWhat is the level of repetitiveness of tasks within the process?Automation abilityWhat is the level of digitization of tasks within the process?Importance of human involvementHow important is human involvement for delivering customer experience?Probability of system upgrade in the near future?Will the underlying system need upgrading soon? How likely is it?Good RPA candidate

Select a reliable vendor

Selecting a trustworthy RPA implementation partner is not easy and requires careful consideration. Check out the following factors:

Intelligent automation

Vendor choice should first of all stem from vendor experience in the banking sector. Consider the vendor's ability to expand beyond rule-based automation and introduce intelligent automation that usually involves AI and data science.

Deployment flexibility

While on-premise solutions still exist, it is more than likely that you will need to migrate to the cloud in the future. Today, all the major RPA platforms offer cloud solutions, and many customers have their own clouds.

Vendor vs partner

Make sure you distinguish vendors and partners. For example, UiPath, the leading RPA platform on the market today, provides the means of implementing this technology. Meanwhile, Itransition is a UiPath silver partner that helps organizations to adopt it.

Build a comprehensive execution roadmap


Requirements gathering

The RPA implementation starts with designing a detailed framework for adopting use cases, which involves establishing both process and technology requirements and defining success metrics.


Backup plan

Every bank's infrastructure and underlying software architecture are unique, meaning that seemingly minor issues can transform into significant bottlenecks down the path. However, considering all possible issues that can arise during implementation is difficult. Banks should come up with various backup plans if things go south.


Running a pilot

Once the framework is ready, it is time to run pilot projects for the selected use cases. While most RPA bots rely on rule-based decision-making, it does not mean that they can’t adjust to reasonable process variability. That is why it is imperative for teams to iterate bots based on their performance in different scenarios.


Performance assessment

Lastly, successful RPA implementation is not a one-time endeavor. Having determined key performance indicators and success metrics, banks should continuously measure how exactly the RPA deployment affects processes.

Real-life banking RPA case studies

While retail and investment banks serve different customers, they face similar challenges. Regardless of the niche, automating low-value-adding tasks is one of the most effective ways to realize employees’ full potential, achieve superior operational efficiency, and significantly increase customer satisfaction.

Postbank, one of the leading banks in Bulgaria, has adopted RPA to streamline 20 loan administration processes. One seemingly simple task involved human employees distributing received payments for credit card debts to correct customers. Even such a simple task required a number of different checks in multiple systems. Before RPA implementation, seven employees had to spend four hours a day completing this task. The custom RPA tool based on the UiPath platform did the same 2.5 times faster without errors while handing only 5% of cases to human employees. Postbank automated other loan administration tasks, including customer data collection, report creation, fee payment processing, and gathering information from government services.

CGD is the oldest and the largest financial institution in Portugal with an international presence in 17 countries. Like many other old multinational financial institutions, CGD realized that it needed to catch up with the digital transformation, but struggled to do so due to the inflexibility of its legacy systems. When it comes to RPA implementation in such a big organization with many departments, establishing an RPA center of excellence (CoE) is the right choice. To prove RPA feasibility, after creating the CoE, CGD started with the automation of simple back-office tasks. Then, as employees deepened their understanding of the technology and more stakeholders bought in, the bank gradually expanded the number of use cases. As a result, in two years, RPA helped CGD to streamline over 110 processes and save around 370,000 employee hours.

KAS Bank, an independent Dutch bank founded over two centuries ago, is a leading European provider of custodian services to institutional investors and financial institutions. KAS Bank wanted to solve one of the most recurring problems a modern bank faces: high operational costs. After subpar results of conventional cost reduction strategies, KAS Bank opted for RPA. Like CGD, KAS Bank carefully explored RPA use cases, conducted multiple proofs of concepts, and only then engaged in the enterprise-wide implementation. This calculated approach helped the bank to reveal various IT bottlenecks and discover the most value-adding RPA use cases. With five RPA bots, the bank automated 20 financial business processes, including treasure operations, obligation payments, internal invoicing, and calculating and booking. Importantly, while the focus of this RPA strategy was to reduce costs, automation significantly improved the quality of KAS Bank’s business processes.

UBS is a multinational investment bank present in more than 50 countries. After the Swiss Federal Council allowed commercial companies to apply for loans with zero interest rates because of the pandemic, UBS, like many other investment banks, had to deal with an unprecedented spike in the number of loan requests. When they could not process the amount of loans using conventional methods of loan request processing, UBS turned to RPA. In collaboration with Automation Anywhere, the bank implemented RPA just in 6 days, resulting in a reduction of request processing time from 30-40 minutes to 5-6 minutes.

RPA has been really helpful to actually show the people on the ground that we can break barriers pretty quickly, which probably previously using other tools and traditional methods of development wouldn’t be as agile and fast.

Karolina Mikolajow

Karolina Mikolajow

Executive Director, UBS Investment Bank

Benefits of RPA in banking

According to Gartner, 80% of leaders in the financial sector are already using some form of RPA for various purposes. Here are some of the most prominent benefits of financial process automation:

Scales operations

Saves time

Minimizes IT department interference

Cuts down expenses

Facilitates compliance reporting

Increases employee efficiency

Reduces human errors

Implements seamlessly


Regardless of the number of requests to process and tasks to complete, RPA bots' efficiency and accuracy stay the same, allowing banks to scale operations on demand.
RPA bots complete tasks much faster than humans, allowing banks to complete day-to-day tasks in shorter time frames.
After completing comprehensive training programs, employees can configure RPA bots themselves. This dramatically reduces the IT department's workload.
By minimizing human involvement in many processes, RPA implementation allows banks to cut operational costs by 30% on average.
Essentially, recorded RPA bots' actions are an audit trail, which significantly simplifies compliance reporting.
Given that RPA bots alleviate the burden of repetitive and mundane tasks from humans, employees can focus on more value-adding activities.
Unlike humans, RPA bots never get tired and perform tasks with the same accuracy regardless of their complexity, which reduces errors.
Compared to the other automation strategies, RPA causes minimal disruption to the established infrastructure and takes less time to implement.
Scales operations
Regardless of the number of requests to process and tasks to complete, RPA bots' efficiency and accuracy stay the same, allowing banks to scale operations on demand.

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Challenges of robotic process automation in banking

Despite numerous benefits RPA can bring and its comparatively undisruptive implementation, adopting this technology is not easy. Here are the three most recurring challenges that financial institutions face when trying to integrate RPA into their banking operations:



Resistance to change

Regardless of the promised benefits and advantages new technology can bring to the table, resistance to change remains one of the most common hurdles that companies face. Employees get accustomed to their way of doing daily tasks and often have a hard time recognizing that a new approach is more effective.



That is why change management is pivotal to successful RPA adoption. As soon as it becomes clear that RPA implementation is the right thing to do from a business standpoint, banks need to create comprehensive change management programs to help employees shift their mindsets and make the transition as smooth as possible.

Process standardization

In a nutshell, the more complicated the process is, the harder it becomes to adopt RPA. In the RPA implementation context, the process complexity correlates with standardization rather than the number of branches on a decision tree. When it comes to global companies with numerous complex processes, standardizing becomes difficult and resource-intensive.



RPA adoption often calls for enterprise-wide standardization efforts across targeted processes. A positive side benefit of RPA implementation is that processes will be documented. Bots perform tasks as a string of particular steps, leaving an audit trail, which can be used to granularly analyze what the process is about. This RPA-induced documentation and data collection leads to standardization, which is the fundamental prerequisite for going fully digital.

IT support

While employees are reluctant to change because of the mindset, IT departments often have too much weight on their shoulders to support RPA implementation. In this current age of digital transformation, bank IT departments are already spending considerable resources to overcome challenges associated with cloud migration, legacy system maintenance, and implementing new ERPs.



While RPA is much less resource-demanding than the majority of other automation solutions, the IT department's buy-in remains crucial. That is why banks need C-executives to get support from IT personnel as early as possible. In many cases, assembling a team of existing IT employees that will be dedicated solely to the RPA implementation is crucial.

RPA as the first step towards digital transformation

RPA as the first step towards digital transformation

RPA can help organizations make a step closer toward digital transformation in banking. On the one hand, RPA is a mere workaround plastered on outdated legacy systems. On the other hand, RPA is a bridge to digital transformation. Still, instead of abandoning legacy systems, you can close the gap with RPA deployment. 

With the right use case chosen and a well-thought-out configuration, RPA in the banking industry can significantly quicken core processes, lower operational costs, and enhance productivity, driving more high-value work. Reach out to Itransition’s RPA experts to implement robotic process automation in your bank.

RPA as the first step towards digital transformation


Why is RPA important for the banking industry?

An average bank employee performs multiple repetitive and tedious back-office tasks that require maximum concentration with no room for mistakes. RPA is poised to take the robot out of the human, freeing the latter to perform more creative tasks that require emotional intelligence and cognitive input. According to Gartner, process improvement and automation play a key role in changing the business model in the banking and financial services industry.

What is the next step in RPA development?

Augmenting RPA with artificial intelligence and other innovative technologies is a definitive next step toward digital transformation. According to McKinsey, the “AI-first” institution will yield greater operational efficiency via the extreme automation of manual processes (a “zero-ops” mindset), and the replacement or augmentation of human decisions by advanced diagnostics.

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Banking software development

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