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Did you know human errors in the finance sector cause companies 25,000 hours of redoing work, costing enterprises $878,000 annually? No surprise, that financial institutions are looking for a turnaround. One promising avenue is leveraging RPA with a fintech app development company.
Robotic Process Automation makes this “do more with less” scenario a reality. To your surprise, the technology has evolved from doing basic tasks of automation to processing full-fledged data reports, data analysis, etc. Furthermore, Grand View Research predicts the global RPA market is expected to grow at a CAGR of 39.9% from 2023 to 2030.
The above data highlights the effectiveness of RPA in the finance sector, as automated processes outperform manual ones. So, which task would your financial organization automate? And, once you’ve made up your mind, how do you start integrating RPA into finance?
Let’s see some remarkable RPA use cases in finance that are worth looking at.
Robotic Process Automation in finance helps achieve the most expensive commodity of all – “Time”.
There’s no doubt, that finance departments are stretched thin for resources and time, especially from the C-suite to sales, everyone needs astute analysis and structured financial data that helps in making timely business decisions.
In essence, leveraging advanced RPA to automate manual tasks and eliminate errors enables businesses to maximize operational efficiency, minimize costs, improve accuracy, and also adhere to compliance mandates.
Did You know?
In the US and Canada, Anti-money laundering compliance costs are estimated at $61 billion for financial institutions.
This alarming number portrays that highly skilled analysts responsible for uncovering such crimes are wasting around 75% of their time gathering data and another 15% entering the system. Fortunately, RPA helps in anti-money laundering investigations by establishing an “if-then” approach to identify potential red flags.
Accounts payable is daunting and error-prone as there is a need to digitize invoices, validate fields, and process the payment. However, Robotic Process Automation integrated with Optical Character Recognition improves the accounting manifold, as OCR extracts invoice information and shares it ahead for authentication and payment processing.
Here’s an example from our portfolio underlining the benefits of RPA integration in accounts payable. A kids apparel company looking to automate its account payable validation process. The company has outlets at numerous locations, and each shares the financial documents in its own format, making it difficult to validate the information at once.
Integrating RPA in the verification process improved the bottom line by $400,000.
The Mortgage Reports suggest that banks take up to 60 days to close a mortgage loan. This is among the prominent use cases of RPA in finance as loan officers follow a time-consuming process, consisting of employment verification, CIBIL check, and many other types of validations. Besides, a minor manual error can significantly slow down the process.
Thankfully, RPA integration in financial processes saves 80% on the processing time which is a huge relief for customers as well as banks.
A renowned name “Radius Financial Group” established RPA with the help of a trusted fintech app development company to accelerate mortgage processing. Earlier, the loan manager would feel overwhelmed managing 30 loans in the pipeline, but with RPA managing 50 loan cases is a seamless process. This validation facilitated Radius to save 70% on loan processing costs.
Surprisingly, during the COVID era Radius marked 30% more loan generation revenue compared to the rest of the industry. Also, 50% more net income is registered compared to the average in the banking industry.
There is no doubt, that RPA in finance can automate a myriad of reporting tasks including reconciliations, monthly audits and management reports.
Financial institutions and banks, adhering to compliance, must prepare reports that include their performance and challenges. These documents consist of behemoth data, making it an onerous and error-prone process. Fortunately, RPA in finance and banking can seamlessly extract data from different sources, convert it into an easy-to-understand format and create error-free reports.
To your surprise, Societe General Bank Brazil integrated RPA for report generation into their processes, automating a workflow that earlier required six man-hours daily.
Furthermore, RPA in finance can also assist compliance officers in identifying Suspicious Activity Reports (SAR). Thus, rather than reading the long documents manually, officers depend on software embedded with NLP capabilities to extract the required information and fill it into the SAR form.
KYC (Know Your Customer) is a time-consuming procedure mandatory for banks to perform for every customer. Usually, it takes up to 1000 FTE (Full-Time Equivalent) hours and $ 384 million/year to carry out these tasks with compliance.
In addition, the alert investigation is also curated as a time-consuming task, in which 85% of daily notifications are false positives, and nearly 25% require level-two senior analysts. With all saving avenues, banks are still losing €50 million/annually on KYC compliance.
Therefore, incorporating RPA in KYC will minimize errors and time duration while upholding customer satisfaction and the onboarding process.
To address time-dependent workflow challenges, demurs of monotonous tasks, and human-errors in the BFSI market, businesses must get started with fintech or eWallet app development company with expertise in RPA. It’ll also assist with operational inefficiencies that lead to unproductive scenarios and poor customer service.
Notable changes can be observed in the following areas:
RPA offers seamless integration potential with existing legacy systems, enabling banks and financial institutions to reap the benefits of automation skipping on the exclusive system overhauls.
Establishing RPA solutions in the banking and finance industry saves around 25-50% on the complete costs.
Robotics Process Automation in banking and finance minimizes monetary fraud and proactively flags instances of fraud or breaches.
Reports and data gathered by RPA bots help businesses and organizations analyze customer behaviour regularly, helping drive sustainable growth.
With automated repetitive and monotonous tasks, RPA helps get real time data analysis and enhanced team’s operational efficiency.
Leveraging Artificial Intelligence and Machine Learning, automation tools can interact with an untold number of internal applications like CRM and ERP. This amalgamation assists in reducing the processing time with precise data analysis, initiating automated customer responses, and connecting with other internal systems. Besides, a streamlined and cost-efficient AI development company helps in delivering better customer service and improving the entire customer experience.
Using AI and ML in RPA helps Fintech organizations improve their capabilities and ensure seamless operations. Artificial intelligence’s potential to extract detailed reports, identify patterns, analyze data and provide valuable information helps in making informed decisions accurately.
In totality, the incorporation of AI and ML with RPA improves the potential of RPA in financial services, leading to enhanced efficiency, minimized errors, elevated customer experience and data-driven decision-making.
Also Read – AI Analytics Benefits, Use-Cases and Real-Life Examples for BFSI
Robotics Process Automation in banking is gaining momentum at an unprecedented pace. In accordance with Deloitte’s Global RPA survey, 78% of respondents leveraging RPA are planning to grow their investment in the next three years. Ernst & Young also suggests that RPA has the capability to help financial institutions save 20% to 60% on FTE costing.
In essence, RPA is gradually transforming the banking and finance industry. Stay ahead of the curve. Seek the proper guidance from Apptunix experts today and dominate the lion’s share in the landscape.
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