In the Banking, Financial Services, and Insurance sector, RPA can be applied to multiple processes to reduce human resources and mistakes. For example, chatbots can help customer support executives to resolve customer queries, complete KYC, credit card or account closure processes, fraud detection, mortgage processing, collection, and much more.
One can easily imagine how much time and resources can be saved through Robotic Process Automation in banking.
For a better understanding, let’s see which are the most common use cases in the BFSI sector:
Better Customer Experience and Service
Banks receive numerous queries every day. Some are regarding accounts or loans; others include bank fraud, debit cards, or transaction-related questions. A customer support team may find it very tricky to tackle these queries in a quick turnaround time.
However, by incorporating RPA, the bots can tackle generalized queries, and the customer support team can manage complicated inquiries.
Faster Processing of Credit Cards
Until a couple of years ago, the processing of credit cards was a lengthy process that took weeks just to get validated and approved. As a result of using Robotic Process Automation in finance, this process takes only a couple of hours to collect the necessary documents, running background and credit checks, and making the decision to approve or disapprove the request.
There are an array of compliance rules for banks, and banks need to collect a whole lot of information for reporting. By implementing RPA in finance and banking, complying with all the regulations becomes simpler. Many complex processes involved in the selection of information can be automated to boost banking operations and risk management capabilities.
Quicker KYC (Know Your Customer) Processes
In accordance with Thomson Reuters, many banks invest around $384 million annually on KYC compliance. Because the KYC procedures cost so much, this becomes an ideal use case of RPA. Through automation in banking and financial services, banks may aggregate client information, evaluate, and validate it quicker with fewer mistakes and manpower.
With a rise in technology, the numbers of fraudulent transactions are also increasing, and tracking all the trades manually to detect fraud patterns is almost impossible for the banks. To reduce such events, RPA function in banking and finance is a must since it automatically inspects suspicious transactions flagged from the AML (Anti-Money Laundering) systems.
The manual process of producing reports is time-consuming, dull, and prone to mistakes. However, RPA methods hold all of the information and can quickly fill up the required fields in the report with no mistakes. This will aid the decision-makers to produce strategies faster to get a competitive edge.
RPA is the modern solution for smart banking operations and provides a competitive advantage to banks and a terrific customer experience.
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