According to CompuTools, the challenge to maximize bank efficiency is one of the top four that banks face, trying to remain competitive in an increasingly saturated market. In order to properly address this challenge, banks must find ways to improve customer experience, by, e.g., maintaining costs as low as possible as a way to deliver customer-friendly services, while not saving any effort in adjusting to high security levels.
With its potential to increase operational efficiency and productivity, to provide cost-effective solutions, and to engage customers in real time, RPA presents itself as the right solution. After a quick implementation requiring a minimal amount of coding, deployment of robotic process automation in banking may be precisely what is needed to address the need for efficiency upgrades.
Consider the fact that the implementation of RPA, besides being rather easy, can bring as much as 50% savings in overall costs.
Real-world use cases of robotic process automation in banking
The effects of the digital revolution are already noticeable across the banking industry. In order to assist you in grasping its manifestation in the real world, we will list 8 common application areas in the businesses carried on by banks.
1. Verification of customer information
Auto loan approvals and processing require thorough verification of the data that customers provide. With the help of software robots, the time-consuming process of verifying and validating customer data on government sites such as tax payments, DMV, or property-appraisal sites, can be performed much quicker than manually (i.e., in a matter of seconds). It also minimises the risk of error and reduces the processing cost by 30 to 70 percent.
2. Account opening
Error-proof bots facilitate disposal of data transcription errors between customers’ requests for account opening and the core banking system. Because of this, not only are downstream errors practically eliminated, but the quality of system data is subsequently improved. The end result? The process is streamlined, more accurate, and completed much faster than by manual performance.
3. Loan processing
Typically, loan processing hinders banks’ efficiency because it takes a whole lot of time. By ‘typically’ we don’t mean only ‘when manually performed’, but also when other automation solutions are used. What RPA does, it eliminates altogether the need to copy and paste customer data between different banking systems.
As a consequence, the processing time is reduced from 30–40 minutes to 10–15 minutes. Software robots thus generate more accurate, quasi error free outcomes in a third of the time. This sounds like a practical definition of operational efficiency, doesn’t it?
4. Fraud detection
Cybersecurity is another primary concern for banks nowadays when the increase in frauds should be acknowledged as a side effect of the introduction of digitization. In order to counter it, banks would have to track all the ever-growing number of transactions and indicate those with a certain probability to be fraudulent. But this doesn’t sound like a reasonable thing to ask for.
What can RPA do about it? In the first place, the capacity for uninterrupted work makes bots able to actually track all transactions and to flag potential frauds in real-time. This leads to a significant reduction in the delay in response, and a consequential minimization of the effects of frauds for the banks. In specific cases, bots can even play a role in fraud prevention, by early detection followed by account blocking and transaction interruption.