Did you know that human mistake in the banking industry results in over $878,000 in wasted time and labor each year? It is evident that the desire for change on the part of banking and financial services is not surprising.
Implementing robotic process automation makes sense since the human error can have a high financial cost in the BFSI industry. A Mckinsey report claims that RPA can automate more than 30% of tasks in about 60% of occupations.
The banking sector is anticipated to grow as business procedures across organizations change. The urge to automate superfluous procedures and provide end users control is expected to drive the sector over the next few years.
Automation adopters will concentrate on four areas: Quick Automation, Auto Decision, Data Security, and Instant Scalability.
BFSI is a tremendously competitive industry; banks and other financial institutions must continuously innovate, stay competitive, and deliver excellent user experiences. This is especially true given the significant counter-competition from FinTech and other virtual banking alternatives.
Banks and other financial institutions are under a lot of pressure to reduce costs and boost output. The lack of qualified workers, the need to increase process effectiveness, and the sharp rise in labor costs all contribute to the adoption of RPA in banking business.
You must consider which tasks in your banking and financial organization to automate.
Enhanced Productivity and Efficiency
Another advantage of RPA systems is how easily and quickly they complete tasks since they listen to and carry out instructions without any space for ambiguity. Robotic accounting procedures don’t have any drawbacks, in contrast to manual ones. Gartner estimates that banking automation can prevent up to 25,000 hours of unnecessary work caused by human mistakes.
By addressing the need for bots to respond to events at record speed, robots’ high scalability enables you to manage big volumes during peak business hours. In addition, by relieving bank personnel of tedious activities, RPA deployment enables the bank to concentrate more on creative business growth ideas.
Accurate Information Extraction
Because there is a vast amount of consumer data saved in systems, RPA, a combination of AI and machine learning, can readily store the necessary data for any desired query. Additionally, automated processing leverages third-party databases to gather information when there are errors or blank fields on bills in order to streamline Accounts Receivable and Accounts Payable procedures. However, similar operations can be completed quickly with RPA and particularly Intelligent Automation.
Cut Down Expenses
The requirement for manual intervention will diminish as a result of the elimination of redundancy, which will allow banking and financial companies to drastically cut their additional expenses for resources, systems, and staff.
Repeated tasks like manually processing data and adding fresh data could be avoided by employees. The financial sector can employ this technology to boost efficiency, consume less energy, and cut back on time, which can lower expenses by around 25–50%.
Increased Accuracy and Dependability
It is natural for people to make mistakes. However, occasionally even the smallest errors could result in serious mistakes that would cost the company a significant amount of money. Unfortunate circumstances may even result in the customers’ excellent reputation being lost.
But the introduction of RPA systems can quickly allay these concerns. The systems will conduct the procedure precisely and effectively with RPA. With the most recent technical marvels like AI and ML, massive amounts of data and processes may be managed effectively. Additionally, RPA solutions are accessible 24/7 and are not hindered by data failures. The data is effectively, automatically, and frequently backed up. Therefore, even if an unexpected event or downtime occurs, it will only stay a short while, and the process will resume as usual very quickly.
According to Grand View Research, the banking and financial services sector, which accounted for over 29% of global revenue, was at the forefront of RPA adoption in 2019.
Most Power-Packed RPA Use Cases in Banking Industry
Fraud detection and AML (anti-money laundering)
Fraud and other erroneous behaviors that aim to harm banks’ reputations are no exception. With the use of the RPA system, screening procedures are carried out more effectively. These systems carry out a number of operations to assess the reliability of any applications or transitions and quickly find any breaches.
Additionally, banks and other financial institutions can compare common fraud behaviors and stop them after using the RPA system, preserving the company’s reputation for a longer period of time.
The number of requests for account closure that banks must manage each month is astonishing. Client noncompliance with the provision of necessary papers is one factor.
Banks can solve this issue by tracking all such accounts and sending out automatic notifications and reminders for the submission of necessary documents thanks to robotic process automation.
The most difficult compliance process for major banks is commonly referred to as KYC (Know Your Customer). There are at least 150 and maybe thousands of FTEs involved in this process who verify the customer’s background.
Additionally, Thomson Reuters reported that certain banks spend at least $500 million yearly on KYC compliance. Banks are now using RPA technology to gather, analyze, and flawlessly validate client information in order to save money and resources.
This enables banking and financial organizations to complete the KYC procedure with fewer resources and fewer mistakes in a significantly shorter amount of time.
Processing of Mortgage Loans
Banks require reliable details regarding a customer’s prior financial situation, including loans, properties, and other credentials. Additionally, because of the RPA system’s efficiency and dependability, banks can process and approve the necessary information more quickly.
Additionally, banks can easily and rapidly create financial statements and check the acceptance of loans, moving towards financial process automation without the least potential for error and anomalies.
Banks are required to create a report regarding their various procedures as part of compliance and deliver it to the board and other stakeholders to demonstrate the performance of the bank. It is crucial to make sure there are no inaccuracies because of how crucial the reports are to the bank’s reputation.
The banks demanded reliable data without any errors, even though there exist methods to provide data and templates to present them in an understandable fashion. RPA aids banks in producing reliable data-filled reports. It compiles information from several sources, verifies it, organizes it in an understandable format, and plans the distribution of it to various sources.
Bank workers struggle to respond swiftly due to the high volume of daily customer requests (varying from balance inquiries to basic account information). RPA implementation tools can assist banks in automating regular, rule-based procedures so they can more quickly and effectively respond to customer inquiries.
Credit Card Processing
The process of issuing credit cards is greatly simplified with RPA. For instance, it would be impossible to skim through a pile of documentation, make multiple queries to learn about and authenticate a customer’s background, and thoroughly analyze the information contained in their financial statements.
Therefore, using RPA in banking and finance, the full data validation process is done more quickly and precisely, and a final decision regarding the approval or refusal of a credit card is made in accordance with established criteria.
As opportunistic, system-based solutions that are quicker and simpler to adopt than extensive transformations, several banks, and financial institutions have started the journey of implementing RPA in their operations.
Robotic process automation, or RPA, services, which automate manual, repetitive, and time-consuming operations, can aid in the banking industry’s digital transformation if properly applied. Increased output, a considerable drop in error rates, and rapid turnaround times would result from automating such repetitive activities.
Throughout the RPA deployment process, having a partner with a track record of proficiency in RPA tools, technologies, and staffing is essential.
This will benefit banks and other financial organizations, but it will also show them when and how to transition from RPA to AI and other advanced technologies. And, who better than the RPA innovators – AutomationEdge!
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