Robotic Process Automation (Rpa) — A Game-Changer for Banking Sector
Over years, banks have been in search of ways to curb excess costs. Robotic Process Automation (RPA) has emerged as a solution of late which helped them to reduce costs and enabled the transition from services-via-workforce to services-via-software. Long since RPA has made its entry into the financial industry, this robotic workforce has supported banks to minimize (or completely eliminate) human involvement in the decision-making and implementation of tasks and radically increased operational efficiency.
“In a few years, machines will perform up to 10-25% of tasks related to bank operations, enhancing capacity and freeing staff to focus on value-driven tasks”— McKinsey.
WHERE TO IMPLEMENT RPA IN THE BANKING?
In the banking sector, there seem to be many areas where RPA can bring-in promises of growth, for example, accounting & finance, regulatory & compliance, sourcing & procurement, cyber risk management, financial risk management and so on.
On the Finance and accounting front, for example, RPA tools can be used to estimate asset depreciation, in order to authorize sales orders or master data management for accounting function. RPA also has the ability to scan system logs in order to locate suspicious or unauthorized activities, thus supports cyber risk management. The implementation of relevance, precision and comprehensiveness tests, or the performance of quick remedy activities whenever needed, are the use cases of RPA when it comes to managing financial risks.
Global Revenue of Banking RPA services will reach around $900 million by 2022—Juniper Research
HOW RPA BENEFITS BANKS?
For easy understanding, the benefits of Robotic Process Automation (RPA) are summarized under four key categories. Of course, each category has its own set of other promising significances, which further validate the widespread adaption of automation in the banking industry.
enhances the quality of work by eliminating the scope for human error completely. This is not an easy task, because
“for every 100 tasks, human interactions can lead to four errors on average” [Source: Yakidoo].
We cannot simply deny errors, since it is directly linked to customer satisfaction and ultimately the reputation of the bank. It is quite natural that customers expect error-free and efficient processes when it comes to their money transactions.
The quality of error-proof systems is high if RPA solutions are used. Also, it allows bank staff to refocus on customer service which highly improves the customer care. RPA raises the bar for compliance due to its ability to eliminate errors.
2. Robots are fast, efficient and never get bored and tired, unlike humans. This is a brownie point, as it leads to higher productivity. All the processes and tasks are taken care round-the-clock, resulting in 100% accuracy when compared to existing processes.
3. RPA implementation is not a time-consuming task, thus allows bank’ systems to execute new processes or incorporate updates.
4. The cost-saving potential of RPA is a well-known fact. Especially, for the banking industry whose prime focus is financialdevelopment, the cost-benefits provided by RPA help banks in attaining their financial goals.
We have begun from stating that the prime purpose of any technology update is to achieve sustainable g
rowth. By the end, Robotic Process Automation leads to extensive benefits such as reduced costs, better service, improved compliance, scalable operations and exposure to innovation.
“RPA helps banks minimize human involvement in the implementation of tasks and decision making, increasing operational efficiency around 70%” [Source: Medium]
USE CASES OF RPA IN BANKING
RPA implementation in banking brings more benefits when steered with in-depth process analysis and execution of standardized work plans at desk-level across the organization. Below are some high-level examples of areas in banking where RPA fits most.
Credit Card Process: For banks, handling credit card requests and providing approvals is time-taking. Implementation of RPA, cut-downs the time required to approve a credit card request from weeks to a few hours. The reason is that the automated assistant helps in the collection of documents, performing background verification and credit checks which are the main reasons for the delay.
KYC Process: The relationship of a customer with a bank starts with KYC which is highly crucial for banks. The costs associated with people and compliance issues of KYC are gigantically high and for large banks, it can cost millions. Using RPA for KYC speeds-up the process while noticeably lowering costs.
Fraud Detection: Banks today are flooded with data. This is a great opportunity to get customer insights, but simultaneously prone to frauds. With the changing tax codes, there is a high chance to exploit the loopholes of the system. However, RPA can identify patterns that are not noticeable to humans, catch unusual transactions and flag them for assessment. Thus, banks can limits frauds effectively.
Customer Service: Banking is one of the customer-service oriented industries. With increased expectations of customers on service from banks, RPA helps banks decrease the resolution times and better service by automating the processes like customer-details verification process.
RPA’s application is not just limited to these areas; there are many other areas such as compliance, accounts payable, transaction processing, Anti-money laundering (AML), front-office and so on, where RPA is proven to be beneficial for banks.
“RPA in banking reduces processing costs by 30-70% and turnaround time from days to hours to minutes”[Source: Medium]
HOW TO GET BEST OUT OF RPA-THE CASE FOR BANKING
Finding the right RPA platform is the first thing to do—wherein ‘right’ implies ‘providing the best method to obtain the above-mentioned advantages’. At EverMethod, for instance, we use advanced RPA technologies that suit your requirements to automate the banking operations such as mortgage, audits, regulatory reporting etc.
Undeniably, learning from examples is the best thing we can do. One useful lesson can be taken from RPA implementation by Deutsche Bank of Germany. Apart from the clear evidence of aforementioned advantages (1) and (4) in the previous section of this article, something worth learning from the RPA experience of Deutsche Bank is that ‘RPA should never be considered as a quick fix’ [CIO Review].
Hence, the expectations of business owners’ have to be customized accordingly. The basic reason why RPA is not a quick fix is—it demands a unified approach to different parameters, for example, system upgrades, primary architectures or developing business processes. In order to fulfill the desired goals, automation should not ignore the above and try to manage them in conjunction.
To summarize, RPA in the banking industry is more about the implementation of intelligent software to perform routine, mundane and redundant tasks involved in banking operations: gather and enter the data in different websites, portals, in-house applications, and other bank systems. The human workforce is liberated to take care of rewarding work, hence enhancing the satisfaction among bank staff. Taking everything into account, RPA is the growth-enabler for banks due to its ability to eliminate the costs resulted due to human error, focus on cost-savings and by its ability to upturn overall productivity. The RPA implementation is fast and convenient.
Provided all the benefits listed above and their anticipated consequences, considering the framework of credibly customized prospects; RPA is found out to be compelling for the banking sector. By suitably leveraging RPA to its maximum potential, the banking sector may enable an incredible rise in its capability and agility.
RPA has already been widely implemented by many banks and other industries. However, if you believe standalone RPA is certainly the trendiest technology, you may fall behind the race.
Artificial Intelligence (AI) has started suffusing many intelligent organizations and progressing towards the basic toolset for day-to-day engagement with employees and customers. At EverMethod, our definition of Artificial Intelligence spans across technologies such as natural language processing (NLP), speech recognition, machine learning (ML), deep learning (DL), semantic technology and many more current-age technologies.
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