10 Critical Banking Processes to Automate With RPA in 2022

10 critical banking

According to Deloitte, Robotic Process Automation (RPA) will reach almost universal adoption sometime in 2023. RPA is a robotic software that several companies use for repetitive and time-consuming tasks. RPA in the banking and financial industry can make their processes more efficient and simpler.

Here are ten important banking processes that can be automated with RPA:

Loan processing:

Month-long processes for loan processing can be reduced using RPA to a record time of 10–15 minutes. Automation enables important data extracted from customer-submitted documents to validate all details. Systems employ Machine Learning to provide more conclusive choices based on data analytics, supported by simpler statistical methods. Intermediary bots generate automated confirmation letters and extract business logic, urging users to correct any inaccurate entries to ensure safer loan choices.

Customer onboarding:

Integrating new clients into your bank is time-consuming and requires a lot of paperwork. Customer onboarding can be carried out digitally with the use of Robotic Process Automation and the verification of KYC documents. For this purpose, consumers can complete the forms from the comfort of their homes. RPA enables banks to address this issue by automatically tracking and sending all accounts.

Account closure process:

The volume of requests for account closure that banks must handle each month is tremendous. One factor is the clients’ failure to comply with the requirements for submitting the necessary documentation.

By effortlessly tracking all of these accounts and providing them an automatic message and extra reminders for the submission of the necessary paperwork, Robotic Process Automation enables the banks to address this issue.

Know Your Customer (KYC):

KYC is not just the most challenging compliance procedure for every bank but also crucial. Banks spend a humongous amount annually on their KYC compliance. Banks are now using RPA to gather consumer data, screen it, and thoroughly evaluate it to save money and resources. This enables banks to finish the KYC procedure with fewer resources and mistakes in a significantly shorter time.

Credit card application processing:

Previously, credit card applications required a waiting period of several weeks, which occasionally led them to cancel their requests. However, banks can expedite the distribution of credit cards with the use of RPA.

The RPA program can gather all customer paperwork, perform credit checks with thorough background investigations, and make informed decisions based on pre-established criteria in just a few hours. The entire credit card processing procedure has been flawlessly optimized by RPA, making banking easier for customers and the banking personnel.

Anti-money laundering:

One of the most data-intensive processes, AML, can be made simpler with RPA. Implementing RPA has shown to be more efficient than labor-intensive traditional banking solutions in identifying suspicious banking transactions or automating laborious operations.

Fraud detection:

Banks are concerned about strengthening their fraud detection system as the banking fraud landscape changes. The emergence of modern technologies has only increased the number of banking frauds. Therefore, banks can’t review every transaction to spot fraud trends in real-time manually.

RPA uses an intelligent “if-then” strategy to spot suspected fraud and flag it for speedy resolution by the department in question.

General ledger:

To properly prepare financial statements, banks are required to keep their general ledgers current with essential data like revenue, assets, liabilities, expenses, and revenue. The manual management procedure is very error-prone given the enormous data from many platforms.

In this situation, RPA comes to the rescue by combining data from many systems. As a result, less time and effort is spent handling data.

Mortgage processing:

It requires a lot of labor and is time-consuming for both banks and their clients. Before processing each loan request, banks take almost a month to process mortgages, going through several stages such as job verification, credit checks, and inspection. The mortgage loan processing could be significantly delayed even by the slightest mistake made by the consumer or the bank.

RPA helps banks to speed up this procedure. Robotics follows a predetermined set of rules to eliminate any potential bottlenecks and expedite the processing of mortgages.

Bank reconciliation:

Each year, businesses expend a lot of resources manually authenticating and analyzing inline transactions. Although introducing various technologies and dispersed solutions has lessened the laborious process of monitoring journal entries, banks are still swimming upstream towards many obstacles like convoluted processes, transaction volumes, and endless data feed sources.

RPA enables businesses to quickly cut costs while adjusting the workload of their back-office workers so that they may work on more rewarding projects. Banks might create reconciliation systems with automatic journal entries, complex data comparison, and long-term preservation by utilizing RPA solutions.

Conclusion

It has become critical for banks and other financial institutions to continuously evolve, remain competitive, and deliver exceptional customer experience to users in an increasingly saturated banking and financial sector (especially with the massive counter competition from FinTech and other virtual banking solutions). RPA will enable banks to overcome this pressure and optimize costs and productivity.

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