April 30, 2021
Table of contents
RPA Business Analyst
Regardless of the industry, today’s consumers expect things faster than ever before. With the exponential rate of technological advancements propelling the speed of service, this trend won’t seem to subside.
In the banking industry, customers expect their mortgage loan to be approved the next day and questions answered instantly. Newer banks are built to address these needs from inception. These new industry players with digital at their core now become serious competitors to their older rivals—big banks with decades-old legacy systems. These banks now actively turn to RPA consulting to stay afloat.
In this article, we figure out the most potent use cases for RPA in finance and define the implementation mindset.
An average bank employee is plagued with performing a myriad of repetitive and tedious back-office tasks that require maximum concentration with no room for mistakes. While it may sound harsh, these people often act like robots. RPA is poised to take the robot out of the human, freeing the latter to perform more customer-centric tasks that require emotional intelligence and cognitive input.
UiPath, a recognized market-leading RPA vendor, identifies a range of use cases for software bots in the banking sector:
With the right use case chosen and well-thought-out configuration, RPA solutions can significantly quicken core processes, lower operational costs, and enhance productivity, driving more high-value work.
This is a chance not to be missed by financial institutions. The BFSI sector showed up as the leading adopter of RPA applications, accounting for a 29% share of the RPA market revenues according to Grand View Research’s 2021 report.
Let’s see now how exactly banks and financial services companies can benefit from the synergy of human and digital workforce.
Banks have to deal with an avalanche of regulatory requirements when onboarding new clients. On top of gathering personal and financial data, bank employees need to verify that data through approved governmental organizations. After setting up an account, banks also have to conduct data archiving and monitoring practices. When it comes to commercial clients, the regulatory burden becomes even heavier as business licenses, credit histories, partnership contracts, trust-formation agreements, and other documents need to be processed.
Although banks have clearly defined guidelines for every step of customer onboarding, the process remains cumbersome and resource-intensive. Not only do performing the related tasks require intensive training, but there is also a chance of detrimental error occurring every step of the way. With an RPA system in place, the majority of these processes can be automated, significantly decreasing operational costs, risks, and time it takes to onboard a new client.
Instead of manually collecting customers’ documents, customers can upload their documents to a bank portal themselves. Then, RPA tools can analyze these documents and cross-reference them with respective databases. Moreover, an RPA system can be configured to continuously monitor transactions and maintain an audit trail.
In a nutshell, the majority of tasks associated with the customer onboarding lifecycle come down to copying, pasting, extracting, and reformatting data, making calculations, and moving files and folders. These tasks require no cognitive input and can be automated using rule-based RPA tools.
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.
Again, these processes require human employees to spend a significant amount of time manually gathering data from disparate systems. For example, as we’ve mentioned earlier, after customer onboarding, banks are required to continuously collect and update customer data. Conventionally, this is done by asking customers to complete surveys with all the relevant questions. Afterward, human employees manually input this data into the system, which is then analyzed by compliance agents. OCR-powered RPA tools can automatically gather, input, and process both structured and unstructured data, enabling significant cost-efficiency and process speed-up.
As with robotic process automation in insurance, RPA tools can also be used to perform mandatory risk assessments. This is usually done by analysts, who need to access a wide array of external sources, including federal bodies, government websites, news outlets, and internal databases to collect all the relevant information about a particular person or an organization. A conventional bot can automatically access all these sources and input this information into the bank’s internal system. Most importantly, this ensures that an audit trail is maintained, which is a critical requirement for KYC due diligence.
For an average customer, accessing loan services through their preferred channel as well as the loan processing speed are the most important factors in selecting the provider.
Not only loan processing speed is a critical competitive differentiator, but it can also have a sufficient impact on the global economy. After the pandemic has turned the world upside down, banks around the world tried to soften the economic impact and keep businesses afloat by issuing an extraordinary amount of loans. Unsurprisingly, this caused banks to deal with an avalanche of applications, resulting in delays and interrupting the credit flow in the real economy.
Essentially, the loan processing volume is capped by the number of employees dedicated to the task. Apart from customer service automation, RPA can bring real value by automating many loan administration processes, including underwriting and validation.
Underwriting is the most essential step in lending. Bank employees need to extract copious amounts of data from physical documents, emails, web portals, etc. Then the collected data are screened for completeness and background review is initiated. For example, when it comes to federal loan processing, there may be more than 1,700 data points to be verified. Unsurprisingly, bank employees can make mistakes, which leads to decisions made based on inaccurate information.
RPA software allows for 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.
For example, Automation Anywhere has designed the Paycheck Protection Program (PPP) Loan Submission in Small Business Administration (SBA) E-Tran System bot, which can extract data from PDF applications and update the lender’s database without human intervention. Given that SBA lenders commonly deal with an avalanche of applications for PPP loans, this prebuilt solution has proven to be an extremely effective tool. Apart from helping banks to eliminate the possibility of human error, such tools can drive the economy further by speeding up the distribution of funds among small businesses.
Postbank, one of the leading banks in Bulgaria, has adopted RPA to streamline 20 loan administration processes. Among these processes, there was one seemingly simple task that 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 to complete this task. The custom RPA tool based on the UiPath platform managed to perform the same task 2.5 times faster without errors while handing only 5% of cases to human employees. Postbank went on to automate other loan administration tasks, including customer data collection, report creation, fee payments processing, and gathering information from government services.
As we’ve discussed in our previous article about RPA in the automotive industry, it’s critical to identify the best opportunities for bot-driven automation. In this sense, the banking industry is not much different. Look for repetitive, stable and rule-based tasks, which can bring the highest ROI in the shortest time.
Other than that, to squeeze the maximum value out of RPA implementation, financial institutions need to start looking beyond unattended automation. Naturally, when identifying the use case, banks are trying to figure out the processes which can be done without human intervention at all. While this really defines automation, much of the technology’s value is found beyond these simple scenarios.
Practically, many of banks’ operations have at least one step requiring rule-based decision-making (RPA’s natural playground), followed by the part where humans’ cognitive input is essential.
Think about fraud detection and suspicious activity investigation. Bots can identify deviations from normal customer activity, then compile this data in one report for an analyst to make a decision. After that, an analyst can order a bot to notify the customer about a possible account breach via a report and initiate password change or any other action needed in that particular situation. This human-in-the-loop approach is paramount for scaling up RPA use cases into more complex ones that require different types of cognitive automation.
Financial institutions realize the importance of digital transformation and personalized banking but fear the enormousness of this process, barely visible long-term benefits, and associated costs. In the banking context, however, the real roadblock is often the need for legacy system overhaul. Bank executives don’t know where to start, how to make the transformation as painless as possible, and how to identify the best opportunities.
RPA can help organizations to move a step closer towards digital transformation in banking. Digitization usually starts with standardization. When it comes to global companies with a myriad of complex processes, standardizing becomes a difficult and very resource-intensive initiative. In many cases, leaders struggle to achieve consensus on how to standardize in the best way possible, but mostly everyone would agree on automating a process, even if they disagree on how to run that process.
The greatest side benefit of RPA implementation is that processes will inevitably 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 really about. This RPA-induced documentation and data collection leads to standardization, which is the fundamental prerequisite for going fully digital. As we’ve discussed in our previous article on IPA vs RPA, augmenting RPA with AI and other innovative technologies is a definitive next step on the path towards digital transformation.
From one point of view, RPA is a mere workaround plastered on outdated legacy systems. From another point of view, RPA is a bridge to digital transformation. In a nutshell, rather than abandoning these legacy systems, close this gap with RPA deployment.
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