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The financial industry is immensely diversified, ranging from banks and credit unions to wealth management and insurance firms. Despite their diversity, these financial organisations are all facing the same rising issues, which include:
Regulatory compliance in the face of increased scrutiny
IT infrastructure in the back office is outdated.
Data mistake rates are high.
Fintech start-ups are becoming increasingly competitive.
To address these issues, software robotics—process automation utilising digital systems that do jobs traditionally performed by human employees—is stepping in. In fact, the banking, financial services, and insurance (BFSI) business spent more on Robotic Process Automation (RPA) in 2020 than any other area, accounting for 29% of the entire RPA market share.
But how does all of that investment play out in the real world? Here's a quick primer on robotics.
Explaining Financial Robotic Process Automation
Robotic process automation (RPA) is already well-established in the financial sector, and for good reason: a study conducted by Leslie Willcocks of the London School of Economics in 2017 indicated that RPA can provide returns of up to 200 percent in the first year.
However, automation technology is rapidly evolving, and terminology might be confusing. When we talk about RPA, we're talking about software that automates the completion of rules-based tasks via an application's user interface. This method enables businesses to quickly automate back-office procedures, lowering expenses and cutting mistake rates at the same time. Among the possibilities for automation are:
Customer onboarding in accordance with Know Your Customer (KYC) and Anti-Money Laundering (AML) legislation
Origination of mortgages and loans
Account reviews and reporting... Customer service ticket handling and a lot more
Listed below are a few examples.
3 Finance Use Cases in the Real World Automation of Robotic Processes
1. Automating Loan Pre-Qualification at a Mortgage Company
RPA was employed by a rising mortgage service provider to automate pre-qualification on new loan applications.
Prior to RPA, the pre-qualification procedure necessitated the manual validation of loan application materials by a team of people. Because they had to manually enter application data into the loan origination system, staff worked after hours to satisfy client deadlines.
After RPA, ferocious RPA bots began to monitor employee email accounts 24 hours a day, seven days a week. The bots copied loan data into a desktop pre-qualification application when they found it. Bots promptly notified end-customers and other stakeholders when they received permission or rejection.
2. Automating Back-Office Processes in a Wealth Management Firm
RPA was utilised to automate 18 essential reporting and post-trade processing procedures at one of India's major wealth advisory firms.
Before RPA, the company's back-office workers spent hundreds of hours each week manually validating and transferring data between old and cloud-based systems. To meet reporting deadlines, the team downloaded data from many systems, which resulted in inaccuracies and delays. Risk assessment procedures, as well as daily post-trade reporting, experienced similar difficulties.
After RPA, trained RPA bots from automated 18 of these reporting, risk-assessment, and post-trade activities from start to finish—and the RPA bots were deployed in less than eight weeks.
3. Automating Franchise Onboarding in Stock Brokerage
RPA was used by a large provider of financial products and services to automate its franchise onboarding operations.
Prior to RPA, onboarding a new franchise was a lengthy process. Multiple stock market portals provided registrations, which necessitated the manual entry of data into a Customer Relationship Management (CRM) system by a staff of four full-time personnel. This manual task resulted in a high incidence of mistake and prevented more valued workers from being utilised.
After RPA: RPA bots automated all data transcriptions between the CRM and the exchange gateways in just two weeks.
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