Warming Up to RPA

Technological innovations tend to gradually unfold within the finance and banking industries, and professionals in the field can’t help but notice the benefits and the challenges of a digital workforce and operational strategy. It’s clear digital technologies have cemented their place in boardroom discussions…but it’s also evident that finance companies will need some time to warm up to the idea of implementing automation.

An estimated 60% of respondents in an EY survey “believe looming advances in technology will require their companies to retrain and redeploy existing employees,” whereas 53% “believe it will create opportunities and necessitate hiring new talent.” However, similarly forward-thinking ideas about digital transformation aren’t embraced by financial institutions universally.

In fact, some have seen and continue to see technological development, including RPA, as a burden. Many see digitization as a threat to their business models, and thus, are more hesitant to adopt emerging technologies. In order to shed light on automation in finance, we need to consider the ideological skepticism surrounding RPA, assess some potential applications of the software for finance companies, as well as the value proposition RPA brings.

RPA’s Value Proposition

To gain a clearer understanding of the value of RPA, let’s first consider the technology’s emerging role in the financial industry through a few example applications and use cases.

Take the perspective of a publicly traded oil and gas company based at the Gulf Coast of the United States with 5,000 employees and $10 billion in yearly revenue – faced with slow operational processes, inefficient and timely relationship management with customers and partners, and middling digital optimization in the back office, the oil and gas company used the automation capabilities of RPA to:

Analyze data patterns 

Because they constantly record and monitor their actions, RPA software robots collect vast quantities of data—everything from a customer’s purchasing preferences to insights on the efficiency of internal business operations. Still not impressed? RPA robots can easily reveal the amount of time it takes to process an order, quantity of outstanding transactions, as well as the processes that generated exceptions and required further human intervention – without breaking a sweat.

While large amounts of data are produced by RPA, they can also be analyzed. The company in question, for example, used RPA to help identify patterns among sensor information and drilling data contained within daily drilling reports. Through RPA’s analytical capabilities, the oil company was able to make better informed decisions for drilling wells and reduce process bottlenecks to increase operational efficiency, improve employee utilization, and reach higher levels of back-office productivity.

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Improve Accounting Processes

Accounting processes generally take up a lot of employee time and stay extremely repetitive, with employees often working over weekends trying to close the books at the end of each month. A company can use RPA to automate several different financial processes that use a lot of time an energy, such as:

• Recording journal entries
• Monitoring revenue and expense accounts
• Preparing financial statements
• Reconciling balance sheet accounts
• Accounts Payable
• Accounts Receivable

Automation can help ease the process, only requiring finance analyst intervention when exceptions are generated and the employees then alerted, freeing their time for more constructive tasks requiring human ingenuity. Not only can automation help streamline a generally manual process, it enables a company to get better visualization of the process and their finances by providing analytic insight with recorded data.

Streamline Joint Ventures

Joint ventures, while necessary to help meet regulatory requirements, mitigate risk, and share resources – including access to technology, often involve the integration of various systems and platforms between companies. This can lead to serious IT chaos and cluster headaches. On the other hand, trying to maintain separate and incongruent systems can cost a business a lot of time and money.

RPA can meet in the middle and bridge the systems gap by interacting with different applications through the presentation layer, streamlining interactions between various customer databases, inventory systems, etc. This solution requires no system re-haul and can keep audit logs of various interactions.

RPA in the Present Moment

The Financial Services and Banking industry has always self-identified as early adopters of RPA in their search for robust digital transformation to continuously get ahead of their competitors and improve the customer experience with customer-facing applications. As RPA continues to gain ground across many different verticals, a 400% growth in RPA adoption in banking by 2023 is predicted, with most of it moving to backend processes to increase regulatory compliance. Different processes can include:

• Credit Underwriting
• Retail Credit Assessment
• Retail Fraud Detection
• Trade Settlement
• Mortgage Processing
• Credit Card Processing
• General Ledger
• Surrender
• Account Closure

RPA Fraud Detection Use Case

In 2018, UiPath discussed the success story of one of their clients in the Banking industry – a typically hour-long process done manually was 95% automated to complete in 5 minutes.

In the verification process tied to Fraud Prevention, the bank managed to reduce the time spent on requests from 45 minutes to 20 minutes, eliminating human involvement. The robots allowed SLAs to be brought back to acceptable levels without the bank having to multiply the resources allocated.

This saved the bank countless hours spent on a usually laborious process.

RPA in Finance and Banking

As we’ve seen with the automation capabilities provided by RPA, high volumes of continuously streaming data can provide an opportunity to optimize operational outputs in the back office through data analysis and improving accounting processes. RPA helps financial service firms manage risk and improve compliance. Financial analysts and stakeholders alike appreciate the accurate, replicable, and prescriptive nature of RPA – along with audit trails that track every action the bot performs. RPA’s efficiencies and cost savings over outsourcing may also enable companies to keep data in-house and under their direct control.

While machine learning and advanced cognitive capabilities are largely still a part of the future, the benefits of RPA for finance and accounting firms. Rather than being timid about implementation, finance organizations must adopt more ambitious goals in the digitization of their operations. RPA should be embraced with open arms to ensure that the benefits of digital transformation are being leveraged to their fullest extent.