What are the advantages of Microsoft Azure
Discover the advantages of Microsoft Azure: Scalability, security, cost-efficiency, and innovation. Learn how Azure enhances operations and drives digital transformation in Australia.
The landscape for financial institutions continues to become more complex. Fintech startups and now-established digital-first finance-adjacent businesses are putting pressure on traditional financial institutions.
For many legacy banks, the choice increasingly looks like “modernise or die.”Robotic Process Automation is a powerful yet easy-to-implement technology that helps to even the playing field. The technology isn’t particularly new; you may already be relying on it within certain software. But many banks could leverage RPA further to increase their capabilities and service offerings.
Insurance companies are already leveraging RPA in powerful ways, and many of these methods transfer well to banking and finance contexts. Of course, there are also numerous RPA applications unique to finance.
Below is our guide for operations managers in banking and finance looking to leverage RPA to reduce manual processes, improve speeds and customer service, and scale capacity.
“RPA stands for robotic process automation, a software-based automation technology that uses software scripts (called robots or bots) to interact with and manipulate computer files and data in ways similar to how humans do — but without direct human interaction. Businesses can create or implement a bot that can perform low-complexity, often repetitive tasks typically done by humans.
Within the context of finance and banking, robotic process automation can automate numerous tasks across a range of functions. RPA bots can vastly outperform humans in terms of speed and accuracy on high-volume and relatively simple tasks, which improves an organisation’s scalability on those tasks. Also, by transferring these tasks from human employees to RPA bots, organisations free up human resources so they can focus on the kinds of high-value work that only humans can do well.
In general, RPA in banking increases capacity, efficiency, and accuracy, all while reducing costs and empowering human staff to focus on high-value work.
To understand how robotic process automation in the banking industry is transforming the world of finance, we’ll examine benefits and real-world use cases.
Throughout the financial service industry and especially In banking and finance contexts, a well-designed RPA implementation leads to numerous benefits and unlocks both capability and capacity far beyond what was previously possible.
When properly configured and applied to the appropriate tasks, RPA can complete those tasks exponentially faster than human employees. Relatively simple tasks like reading data and copying that data from one field to another are easy for robot scripts to complete, while your human employees make occasional mistakes, get tired, and work comparatively slowly.
No matter the size of your organisation, there’s a functional limit on how many people you can hire for a particular task or function. Unless your business grows in perfect proportion, you’ll eventually encounter a bottleneck: somewhere you’ll need to accomplish more than your staff can, but you won’t be permitted to hire further. (Or, in the current business climate, you may simply be unable to find staff to hire.)
In other words, your human staff is constantly running into scalability limits. RPA doesn’t encounter these limits in the same ways. So by moving applicable tasks from human workers to RPA scripts, an organisation can greatly increase the scalability of those tasks and associated processes.
The banking industry already possesses massive troves of data. The problem financial institutions face is making sense of that data. Keeping up with ever-expanding new data sets as they come in is another challenge for banks.
Intelligent automation through RPA (especially when combined with machine learning and artificial intelligence tools more commonly used in business process automation, or BPA) is one way to process this data and draw insights from it. And all of this happens far faster than manual data analysis efforts, enhancing the capabilities of analysts and data scientists.
Using technology to accomplish more work without increasing headcount ultimately lowers the cost of producing a set amount of work. While there can be increased technology costs, these are usually quite low for two reasons: first, RPA can run atop an organisation’s existing tech stack without the need for significant increases in compute power. Second, setting up the scripts themselves is relatively simple. Many businesses rely primarily on off-the-shelf or plug-and-play scripts with little need for custom coding.
When your employees no longer spend significant amounts of time on repetitive, uninteresting, low-value work, they become empowered to do better and more creative work. By removing work that robots do well and humans do poorly (or slowly), you’ll give your team members more time and capacity to solve human problems and meet customer needs.
Below are seven of the real-world use cases and applications where RPA solutions can eliminate repetitive tasks, improve customer experiences, and improve manual business processes.
Bringing a new customer on board within the banking industry is a process with multiple steps and several manual verifications. The process can feel quite drawn out, lessening the customer experience and taking more employee time.
RPA can automate aspects of this process by comparing data sets and automatically verifying documents in most instances. An RPA system can in many cases take a customer’s initial forms, verify them, and create a customer entry using that data in a bank’s customer management system (CMS).
RPA solutions can flag discrepancies or missing data, prompting either the customer to supply the data or the bank officer to investigate the discrepancy. Best of all, RPA solutions don’t make manual entry mistakes, increasing overall accuracy.
The process of collecting applicant information, calculating risk, and ultimately rendering a verdict on whether an applicant will be approved for a loan is data-heavy and contains many steps. But these steps are quite consistent and predictable, making the process an ideal candidate for automation. Robust RPA applications can even process exceptions by following the rules set up in the script.
RPA greatly reduces the time required for the mortgage approval process, which improves a lender’s customer service reputation. This technology also has the potential to remove both bias (including unconscious bias) and guesswork from the process thanks to its massively increased data-processing capabilities.
Similar to the mortgage approval process, loan processing involves numerous steps, checks, and forms. By eliminating manual steps in the loan processing workflow, RPA can greatly reduce the time and resources needed to process a loan.
Throughout the financial system there are myriad reports that must be generated, checked, approved, sent, and so on. And banks often face compliance and regulatory concerns if these reports don’t arrive or arrive with errors.
But many of these reports involve little more than pulling the right data from the right locations and placing it into a template. This is exactly the sort of work that RPA excels at doing — near instantly, near error-free, and with no fatigue or coffee breaks.
The sheer scale of financial transactions makes detecting fraud in real time an impossibility when using manual processes. Banks are already using intelligent automation to screen transactions for signs of fraud, but flagged transactions route to analysts who must process them and often input their data into another system or field.
RPA can automate other layers of the fraud detection process, starting with that data collection and logging. When analysts no longer spend their time moving data from place to place, they can use that time instead to actually analyse that data.
A function in nearly every business, accounts payable is another discipline ripe for automation. Automated Accounts Payable relieves many of the same pressures already discussed: it eliminates manual processing, scales effortlessly to handle large transaction volumes, consistently performs according to compliance expectations, and so on.
As the institution on the receiving and processing end of accounts payable transactions from numerous clients, commercial banks can leverage RPA to automate the back end of these transactions as well.
RPA technology is easy to implement using prebuilt RPA solutions, many of which require little to no coding to implement. The market has responded as a result, and today there are numerous RPA solutions available to operations managers in banking and finance. These vary from industry-specific to general market, and of course they vary in quality and cost as well.
Building custom solutions is an option as well: in some cases, this route is the best method to accomplish an organisation’s goals. In others, fully custom RPA is not cost effective.
One of the key considerations is if adopting a RPA-as-a-Service model is best fit for your organisation or building a RPA centre of excellence is preferred - explore some of the benefits here.
Choosing among the available RPA solutions is a challenge for every operations manager. Look for the RPA solutions partner that has the right mix of capabilities and cost, and that has the scale to support your organisation through this phase of your digital transformation.
Canon Business Services ANZ is an ideal partner for many thanks to our combination of technical capabilities and regional scale.
Read the case study on how RPA transforms Canon’s banking operations.
