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Top 10 Use Cases of RPA in Banking & Finance Industry
It is no secret that the banking industry has battled to evolve with the times and stay up with technological advances. Adopting technologies has helped banks provide the best customer experience while remaining competitive in the saturated banking market. In addition, the pandemic has accelerated company measures to react to employee and customer demands, making digital solutions the future of financial services.
The challenge of maximizing efficiency and keeping expenses as low as feasible while ensuring maximum security standards has also drastically increased. Robotic Process Automation (RPA) has evolved into a powerful and effective technology to meet these expectations. Around 80% of finance leaders have implemented or are planning to implement RPA (Gartner).
Why choose RPA?RPA has been widely used in banking to organize and automate time-consuming operations. RPA has also significantly reduced many back-office tasks that formerly slowed employees’ efficiency. As a result, banks have minimized the need for human resources by shifting most of these repetitive, manual tasks from humans to machines. This has directly influenced everything from performance and efficiency levels to staffing concerns and expenses. As per Gartner’s analysis, if completely implemented, RPA can save up to 25,000 hours per year and $878,000.
Furthermore, because of its low-code approach, RPA best suits banks and financial institutions.
In the long run, RPA in banking will have a greater impact. They could include:
- Banks may not be able to attain zero back-office costs overnight but implementing RPA will gradually help them make progress.
- Identifying exceptional cases, validations, and personalized solutions for each customer will not be exhausting anymore.
- RPA tools are accurate and have a nearly 0% error rate. Owing to this, banks experience fewer customer complaints and call-backs while delivering a seamless customer experience.
- RPA may replace human labor, but digital resources with cross-domain competence will have a bright future.
RPA use cases in Finance Industry
Now that we’ve outlined some compelling reasons why financial services organizations require RPA technologies, let’s look at how it works in practice.
1. Customer Support
Improving client experience is a key to organizational success. RPA bots significantly relieve the banking industry of inbound queries and strain. It can aid in managing a large amount of daily traffic and improve customer support.
2. Onboarding Customers
Customer onboarding is one of the most challenging operations in the banking sector. Manually verifying each customer’s identity documents consumes too much time and effort. Furthermore, the Know Your Customer (KYC) process makes this process even more tiring. If this is the case, RPA is your answer.
RPA bots can automate the customer onboarding process saving time and increasing work efficiency.
3. Trade Finance Operations
Banks can use RPA technologies to expand their trade finance operations and strengthen their position in the financial supply chain. For example, RPA can automate activities related to issuing, managing, and closing letters of credit- the most often used trade financing instrument.
4. Loan Applications Processing
The loan application procedure is a fantastic option for RPA to show its potential. Few primary manual activities include data extraction from applications, verification against different identity documents, and creditworthiness evaluation. We helped a client process their loan activities within a TAT of just 10 mins, whose turnover time used to be 30-45 mins.
Read the entire case study here.
5. Automated Report Generation
Automating the report-generating process entails a variety of operations such as optimizing data extraction from both internal and external systems, developing reporting templates, reviewing, and reconciling reports. Many banks and financial service providers have adopted RPA to automate these report-generating operations.
6. Anti-Money Laundering (AML) Prevention
Automating the entire AML investigation process is one of the best examples of RPA in banking. The investigation of a single case takes anywhere from 30 to 40 minutes. RPA can easily automate these repetitive and rule-based operations, resulting in a maximum reduction in process TAT.
7. Bank Guarantees Closures
For many institutions, this is a highly relevant RPA use case. A staff team manually transcribes data and identifies bank guarantees due for closure/termination/discharge. The creation/distribution of notification letters, and the execution of reversals/closures, are all done by hand, which reduces overall productivity. RPA has the potential to automate the entire process successfully.
8. Processing Account Closure
End-to-end account closure entails various manual duties such as validating the bank’s records, sending emails to clients and branch managers, and changing data in the system. RPA Bots can automate all these procedures, allowing employees to concentrate on more complex operations.
9. Process of Bank Reconciliation
Bank reconciliation is a time-consuming process that requires a manual search for a large piece of transactional data involving many banks and the balance of the final figures. RPA Bots can be developed to automate numerous manual tasks, such as validating each payment entry against bank data and other records. The records are reconciled if the entries match.
10. Processing Credit Card Applications
Another use case where banks have found fantastic benefits is RPA-enabled credit card application processing. RPA Bots can easily traverse numerous systems, validate data, do several rules-based background checks, and decide whether to approve or reject an application. Customers might receive a credit card within hours, thanks to RPA.
With so many benefits, banks should explore implementing RPA in all of their operational areas to improve customer experience and gain a competitive advantage.