Using RPA to Automate End-to-End Federal Loan Processing – GCN



Using RPA to Automate End-to-End Federal Loan Processing

In the days following the pandemic, central banks around the world, especially the Federal Reserve and the European Central Bank, acted with extraordinary speed to support financial markets and calm volatility. The banks’ willingness to support the financial sector softened the economic impact of the crisis, with the S&P 500 Index posting its best quarter in more than two decades. By maintaining credit to the real economy, the speed and disbursement of loans has had a tremendous impact on the health of the US and global economies.

In recent years, however, the wait time for borrowers to get loans processed in national emergencies has increased, given the greater volume of applications, existing systems and limited manual processing capacities. The main reasons for the delay are usually application errors, time-consuming manual processes, and the involvement of various stakeholders in the approval process.

Given the impact of these loans on global economic recovery efforts, should manual processes that take weeks or months for loan disbursement continue? Replacing manual processing of federal loans with robotic processes will ensure dramatically improved speeds. We’ve seen robotic process automation (RPA) improvements provide a 10 and sometimes even 100-fold improvement in processing time when implemented in other areas of the financial services industry.

While the federal government offers loan programs for farmers, businesses, homeowners, students, and veterans, each loan has its own pre-defined requirements, data points, and approval processes. However, the majority of loan workflows have a number of identical processes for underwriting, loan origination, selection, validation, etc. This facilitates the development of accelerators for automation with a strategy of unique creation and frequent use.

RPA can help agencies manage the end-to-end processing of these loans and make underwriting decisions every step of the way, from origination, selection, validation to loan management, through them. partially or fully automating.

Underwriting is the most crucial stage of credit. Unfortunately, sometimes approvers get it wrong because they end up trusting inaccurate information. The manual process of collecting information is tedious, complicated, and error-prone.

Federal loan processing begins with extracting entries from paper documents, emails, faxes, or other online portals, followed by a 360-degree review for completeness, review of applicant’s background and credit check. There are over 1,700 data points per loan to extract and compare.

RPA-based software allows a candidate’s record to be compiled from multiple systems, channels, and service providers to be collected and entered into government systems for underwriters to analyze.

In addition to simply taking over the underwriting process, RPA can also help federally regulated financial institutions automate the processes of granting loans, managing loans, reviewing risk and fraud, routing documents, task assignment, email notifications, and warranty management and imaging.

Additionally, with RPA, the government can provide instant responses to inquiries through online portals and chatbots. With all the processes automated, the government can reap the real benefits of RPA – lower operational expenses, accelerated operational efficiency, and the ability to leverage existing data and infrastructure.

Yet some agencies may rely on mistaken impressions for their reluctance to implement RPA.

Costs: While the cost of deploying RPA is one of the main reasons companies are reluctant to adopt it, a McKinsey study suggested that “RPA is a promising new development in business automation that offers a potential return on investment of 30-200% – the first year. “

Lack of skills and technical capacities: While existing IT teams can hold back, thinking about RPA requires significant technical skills and knowledge, with the right tools and the right training, the learning curve is negligible and less disruptive than you might think.

Legacy infrastructure: Although RPA aims to leverage existing resources, implementation may require minimal infrastructure refresh.

Resistance to change: Some organizations believe that robots can replace the majority of their human workers, but in fact, RPA takes over the work of the employees.

With its ability to reduce loan processing time by up to 80%, RPA is poised to take the financial services world by storm, as evidenced by its rapid growth. It minimizes and reduces human errors, automates mundane tasks, helps with regulatory compliance, delivers significant cost savings, provides 24/7 support, and reduces the risk of cyber fraud.

Implementing RPA within federal agency loan departments is an ideal way to lower costs, reduce errors, and speed up processing and disbursements – better serving federal clients and the workforce, too.

About the Author

Gautam Ijoor is CEO of Alpha Omega Integration, LLC.

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