Robotic Process Automation (RPA) – Opportunities and Challenges in BFSI

RPA technology has a plethora of applications in the banking and financial sector. By automating manual or repetitive tasks, RPA has significantly accelerated time-consuming processes, reduced errors, and streamlined back-office operations.

Here is a look at the 5 most compelling use cases of banking RPA solutions:

With more individuals opting for banking services, banks need help to ensure customers’ smooth and seamless onboarding in their banking system. To prevent misuse and safeguard their interests, banks need to manually verify their incoming customers through an efficient identification process.

On its part, RPA solutions can facilitate the identification process by providing a seamless workflow addressing all customer service and identity documents on the same interface. For example, a UK-based private bank deployed RPA technology to upgrade its customer identity verification process. This involved a chatbot with complete access to the customer data.

Typically, U.S-based banks take between 50-53 days to process a mortgage loan. This is due to various checks, such as employment verification, credit score checking, and approval. Even a minor error in the loan application process can further delay the approval process.

Any loan processing process includes multiple steps, including deriving relevant information from the borrower’s submitted documents and using this information to arrive at the loan disbursement decision. RPA can effectively enable a document automation system to allow loan companies to extract useful data from customer documents as the process flows. Further, AI-enabled credit scoring data models can be linked to this solution to determine the customer’s creditworthiness. Thus, RPA solutions, working in tandem with AI, can accelerate the loan approval process and reduce the processing time to a few minutes.

According to this Booz Allen Hamilton report, AML data analytics spend close to 75% of their time in data collection and another 15% on manual data entry. Whether domestic or international, AML teams in banks spend much time screening each transaction. Manual investigations can typically take 30-40 minutes for a single case.

By implementing RPA, banks can typically reduce the turnaround time by 60%. Automated processes running for 24 hours can retrieve images of transaction checks, which can immediately be used by the AML investigation team. One successful case study is that of an American bank that leveraged RPA for its AML processes and reduced its due diligence costs by 75%.

Like customer onboarding, account closures can be challenging for banks due to the large volume of banking customers. Be it for any reason, the manual mode of account closure involves a series of tasks, including cancellation of any standing orders, interest payments, and fund transfers.

Account executives must also send the necessary emails for customer approvals and manually check submitted documents. RPA-enabled solutions can automate the account closure process so that banking executives can focus on other tasks. With an RPA system, customer service agents can fill and send electronic forms through email, which can later be processed without human intervention.

On average, Know-Your-Customer or KYC compliance costs the BFSI sector around $60 million yearly, while nearly 90% of corporate treasurers have had a poor KYC experience with their banks. As a critical compliance requirement, banks need to involve 150 to 1000 FTEs to check the customer’s background.

Using RPA tools for collecting and screening customer data reduces the costs and resources required for following KYC compliance. RPA-enabled bots can improve processing time and reduce error rates during KYC processes. One case study is that of an Indian bank that leveraged RPA technology to automate its KYC processes. The outcome was a 50% decrease in manual work hours and a 60% improvement in productivity.

What are the common challenges to successfully implementing RPA technology in the banking sector? A recent research study reveals that resistance to change is the leading reason in 45% of financial companies. Additional challenges include standardization of banking processes and organizational misalignment in the banking sector.

The continued use of legacy systems and processes is another obstacle to RPA adoption in the BFSI sector. Around 43% of American banks continue building applications on the COBOL programming language.

Among the most efficient and user-friendly RPA tools to emerge recently, UIPath can now implement RPA solutions in the banking sector. To overcome implementation challenges, the UiPath tool enables easier deployment of robots that emulate human actions.

UIPath enables capabilities such as:

• Automate manual or repetitive tasks.
• Allow knowledge workers in the banking domain to focus on revenue generation.
• Perform automated credit assessments on loan applicants.
• Detect and prevent any financial fraud.
• Automate front-end tasks to improve CX.

Additionally, UIPath can automate financial transactions and aggregate financial data with other applications. Besides, it can improve compliance by providing audit trails for each customer transaction.