Insights from The Lab

A step by step guide to implementing RPA in the banking industry (with use case examples)

Article by : Chris Wilds

What is robotic process automation in banking?

Robotic process automation (RPA) is about to change the way banks conduct business faster than any other new piece of technology available. Think of it as the consumerization of banking automation – meaning that front-line employees will eventually be able to automate their own work if trained properly. For the banking industry, RPA is a new, and completely underutilized way to increase productivity and keep repetitive, manual-labor-intensive processes at a minimum.

RPA in banking is defined as the use of robotic process automation software like UiPath, or Blue Prism, to install software robots, or an artificial intelligence workforce, or assistants, to help process banking work that is repetitive in nature. Once set up and implemented, banking robots take control of mouse and keyboard actions such as opening applications, clicking, copying and pasting information from one banking system to another, sending emails and other labor-intensive “low-value add” tasks.

Established banking institutions have historically relied on legacy systems in which operational benefits had been oversold on the front-end during the sales process, and more often than not the implementation underdelivered on the back and fell short of automation promises. The result is an industry in which large amounts of manual work has to be done outside of core systems in order to reconcile and transcribe data to process transactions. In an industry prone to large-scale white-collar work processing, this snowballs as banks and their IT departments struggle to merge different legacy systems into a coherent workflow.

RPA enables the banking industry to integrate “the last mile” across business units like it has never been able to do before. RPA use cases can be applied to a wide range of processes, including; retail branches processes, commercial lending, consumer lending, loan processing, underwriting, and anti-money laundering, just to name a few.

Robotic process automation in banking is minimizing the need for extra repetitive manual work tasks, data reconciliation, and transcription—sometimes by up to 70%. While the idea of implementing RPA into the banking industry may seem daunting, it’s simpler than most companies selling it want you to think. Below, we’ll discuss some RPA banking use cases that show how surprisingly easy it is to implement, provided you do the required front-end analysis and standardized processes first.

Benefits of using robotics in the banking industry

Banks are facing increased pressure from management, shareholders, and external competition (like fin-tech companies) to reduce costs and speed up processing of back-office work. Benefits of RPA in banking can be financial, and operational while improving customer experiences.

Examples of RPA benefits and use cases in banking:

1) Reduction of consumer loan processing time from 30 minutes to 10 minutes by eliminating copying and pasting of customer information from one banking system to the next

2) Increased accuracy of new bank account opening requests – reduced down-stream errors, and improved system data quality by eliminating data transcription errors from inbound new bank account opening request emails into the core banking system

3) Increased auto loan processing speed of customer verification by automatically validating customer data on government websites, such as the DMV, tax payment, or property appraisal sites

4) Banking RPA does not require new core IT infrastructure—it’s a low-cost layer that sits on top and across all currently installed banking applications

5) RPA for the banking sector is nimble and can be updated on the fly. Robots can be installed or updated in less than a week when banking processes change.

6) Banking robots can work 24/7—365 days per year. Banks don’t have to pay a robot overtime, don’t have to pay the health insurance, or worry about them quitting.

RPA is most beneficial when led to the deep front-line process analysis and a desk-level work standardization plan across the organization. The only way to realize the maximum benefits of RPA is to standardize all manual work, then implement afterward.

A detailed RPA case study in the banking sector—robotic loan processing

In this banking RPA use case example, we’ll follow Cathy, a consumer loan processor who’s getting ready to process a loan of a prospective borrower. Normally, Cathy would need to process each loan request manually at the bank, which would take her at least 20 minutes per customer. She had to cut and paste information between email, multiple loan processing systems, credit bureau, and several government websites.

When a customer requests a new consumer loan or line of credit, a call center representative, branch employee, or website will input the data into loan processing system #1. This captures the customer request for a new line of credit. Once this is done, a credit check must be run manually run by the processor by transcribing data from loan processing system into external websites. Once the credit check is complete, data is gathered from 3 different systems, then placed into the customer master system for creating a new account. Once this step is finished, the information must be copied and pasted again and put into the “line of credit” system. The final step is for Cathy to log into a government website only to copy all of the information again and paste it into the website to validate the address of the customer requesting the credit. This was all done by hand, 20 times per day. 80% of Cindy’s day is spent copying and pasting.

Now, with the help of UiPath, Cathy’s loan processing job is going to be very different. The RPA bot will now process the work as follows:

  • UiPath logs into the bank’s loan processing system and captures the new loan request.
  • UiPath switches back and forth between the loan processing system and Cathy’s other 2 banking systems to cut and paste all the information needed from one system to another.
  • RPA completes a credit check on an external website after transferring the information from the initial loan request automatically and then copies and pastes the credit check results for Cathy and places it into the three different LPSs.
  • Banking RPA software then logs into the government website, enters the necessary data again, and validates the property appraisal and customer address automatically.

What used to take Cathy 20 minutes to do now takes her 5 minutes, and she is able to focus more on delivering an exceptional customer experience instead of just moving data around. This sounds like something that you might want to try out, right? Let us show you how below or even do it for you.

Steps to implementing RPA use cases in the banking industry – work standardization must come before robot installation

The above example is just one of the many ways banking RPA is making the lending process faster and more cost-effective; saving both the banks and consumers time and money. So how does one go about implementing RPA into a bank? We’ve highlighted the key points that must be completed to undertake when you try to do this on your own, or with the help of a professional.

How to scope the banking RPA project

The first step to an effective RPA in banking industry project is determining a manageable scope. Think of a small test drive, then broaden your scope to include more once you have learned your first RPA lessons. It might seem tempting to come out swinging with an enterprise-encompassing scope, but don’t fall into the trap. Despite what some might tell you, starting robotic process automation banking project on a large scale is a great way to fail fast.

Financial institutions have multiple divisions in the back office that process and transcribe data all day long, including; credit operations, loan servicing, fraud prevention, collections, and loan operations. Of the multiple functions that banking RPA helps with, one we’d recommend starting with is loan operations.

Loan operations employees deal with large amounts of data spread out over different systems, fields, and forms. Loan officers, processors, and credit analysts are loaded with manual work, customer information, and have to deal with redundant data processing from multiple core systems.

Start your mapping exercise with a group of 20–500 employees who you know are open to change.

We’ve learned through experience that it’s best, to begin with, a pilot scope to reduce project failure risk and reduce project learning curve costs. This means that identifying use cases for RPA in the banking sector for just a few tasks to start, rather than for each and every process that a bank executes all at once. Org change is less painful if dished out in small doses.

Determine baseline operational cost to calculate the total benefits realized from implementing RPA in your bank

To realize the full scope of financial benefits that could be realized from banking RPA implementation, it’s essential to have a baseline for operational costs before attempting to add RPA. Make sure to budget for this task to usually take a week or two to do. You will have to coordinate with the HR department at your bank to get the cost for each employee in scope.

It’s also a good idea to measure the costs after the first initial attempt to implement RPA, as well as subsequent attempts over time. This will allow you to show the costs and savings achieved over time with the use of RPA. The best way to make a case for robotic process automation in banking is showing a lot of green to people who are on the hook for benefits, i.e., senior management. So, make sure you have the proof before you run around RPAing  everything!

Analyze current state banking processes to document RPA use case opportunities

Roll up your sleeves, because it’s time to get dirty. Now’s the time to do process mapping. So many people always want to skip this step – and it’s the most important one. We’re not just talking a little here and there, either. You have to map all of the systems and people you’re considering adding banking RPA to. A thorough process analysis of all front and back office tasks needed to carry out daily operations are essential for determining how RPA can help your bank. You need to get down to the “grain of sand at the beach” level of detail.

This could be done conducting what we do here at The Lab called “day in the life of” observations via screen sharing software, or by doing face to face observations in person. We literally watch workers perform tasks for a whole week, all day long, to document where RPA can be used. We like to use GoToMeeting or Webex for the off-site observations. This route reduces the costs of having an on-site consultant to analyze the current daily processes needed to conduct banking business.

Once you’ve completed your shadowing process mapping software program, such as Microsoft Visio, can help you to visually represent your steps to build your business case. Every detail that goes into the workflow (down to mouse clicks and keystrokes) must be represented. A good way to document this for the IT department or RPA vendor hand off is to use BPMN 2.0 stencil, as this is the “coding level standard” way to represent process flows. Then, you get to repeat it all over again with the next employee and so on and so on, until you have a sample size large enough that it validates your findings. It might not seem like a breeze, but remember, no pain no gain.

Now on to the really difficult part – you must standardize banking processes before robotic process automation implementation — but not after. Here’s the deal: you shouldn’t be thinking of pressing that banking sector RPA button if you’re not up to analyzing and standardizing your current manual processes first.

Banking processes might be different across financial institutions, but they also vary across employees internally. Successfully rolling RPA out to scale means everything must be standardized and planned out before implementation so that it is executed properly and efficiently.

Find project managers with lean experience focused on transactional and white-collar process work. Their previous work standardizing processes at scale will serve your RPA banking use cases well.

Hire a financial services focused RPA vendor to analyze processes and implement for you

Once you have your workflows standardized and mapped out, it’s time to implement your new banking RPA processes. We recommend using UiPath. There’s a free Community Version that we think every business should try. You may also find it beneficial to research use cases to reduce any time lost on the learning curve that comes with using a new technology. Another option is to become RPA certified so you can do it yourself; saving money and adding a skill to your CV.

Of course, the easiest option is to hire a banking RPA consultant to both consult and handle the mapping and process standardization. They can then hand off the installation of the actual RPA technology once everything is sent to the implementation team. Prices vary among consulting firms, but we’re a firm believer in less cost and risk. Contact us, and you’ll get a solid, easy-to-follow blueprint before we shoot for the stars.

Benefits of choosing The Lab for analyzing, developing, and supporting the implementation of banking RPA

One great option for implementing RPA into your financial services operations is to use the “a la carte” RPA mapping offered by our team at The Lab. Our model is designed around starting small, earning our client’s trust, proving our competence, then increasing efforts over time. We offer a variety of template IP options to speed up RPA analysis and implementation. We provide RPA solutions for financial institutions who want to step up their game, or simply keep up with the rapidly changing industry. We would love to chat with you further about our RPA solutions, methodology, and mapping if you’d like to learn more.