How to Use RPA for Lean and Efficient Mortgage Processes: A Practical Guide


Robotic process automation (RPA) is a powerful – but frequently untapped – enabler for the mortgage industry. With stiff competition from digital natives and fluctuating profit margins amid the pandemic, lenders need to strive for efficiency and they must get there fast. RPA can reduce effort and error across the end-to-end mortgage processing lifecycle. At the loan origination stage, for instance, it can shrink processing times by 80% so that you can increase capacity and deliver a better customer experience to borrowers. At the post-closure quality control stage, it could expedite processes by 20%.

Particularly now, as we begin a new year and a new phase in the ongoing industry transformation that started with the pandemic, lenders need to carefully assess the potential and impact of RPA.

Understanding RPA and its Evolution

Robotic process automation or RPA is a set of technologies that intends to replace human effort either partially or fully through a software robot (or bot) when completing a task. It is typically employed in low-variance, low-complexity tasks – e.g., checking a loan application for data entry errors.

Now, this traditional definition of RPA has evolved significantly. Cognitive technologies like artificial intelligence and object recognition allow bots to perform tasks that might be slightly more complex or variable in nature. For instance, an advanced RPA bot can listen to, understand, and respond to borrower queries and reduce workloads for service teams. This next evolution of RPA, called intelligent automation, is one of the key fields of research and development at Nexval.

As RPA evolves, its scope to positively influence mortgage processes grows in tandem. In fact, correct RPA implementation could be a primary differentiating factor between mortgage leaders and laggards as the industry moves forward.

Target the Low Hanging Fruits in Mortgage Process Automation

There are several mortgage processes that are prime candidates for RPA and should be targeted as your “low hanging fruits” to accrue ROI.

  • Automated fraud alerts – Loan origination systems generate a high volume of alerts, and a team of human analysts must look into each alert individually to ratify/de-prioritize them. Instead, a software bot configured with appropriate business rules can automatically analyze alerts, detect false positives, and provide you with actionable reports.
  • Automated upselling and cross-selling – Banks and financial service providers that offer holistic lending services, including mortgage, can leverage customer information to drive upselling and cross-selling. For instance, in a loan application, the customer mentions that they have a child just graduating high school, AI algorithms can help to scan the lender’s entire product portfolio and recommend related services – such as a college financing program.
  • Automated customer service – RPA can help reduce the number of borrower queries your team has to answer. The bot is configured with customers’ most frequently asked questions, and natural language processing (NLP) ensures that it understands the query no matter how it is phrased. The lion’s share of borrower queries on issues like timelines, forbearance, verification, etc., are answered by the bot so that your team is free to act on the most challenging and value-generating interactions.
  • Automated document and report generation – Lenders receive information, data, and documents in a variety of formats, which need to be painstakingly standardized. RPA allows you to automatically extract information from unstructured sources, index images, and archive in a centralized repository so you can generate consolidated reports.

These four cases are an excellent starting point for any company exploring mortgage automation in 2022, with minimal risk and a greater possibility of rapid returns. But to get this right, there are a few considerations to remember.

How to Steer Clear of Buyer’s Remorse: 3 Best Practices for Decision Makers

Mortgage providers new to RPA could face buyer’s remorse for a number of reasons. They might have purchased an in-house solution, but did not have the requisite technical resources to operate it. The IT infrastructure might have been unprepared, causing RPA to suffer from lack of maintenance. And, if the wrong process isn’t automated first, it could take years to start generating value, which holds back the entire automation initiative. Buyer’s remorse must not dissuade decision-makers from staying confident about RPA’s potential. Here are three best practices to avoid this:

  1. Understand the specific type of automation you need. Depending on whether the mortgage task is high/low on variability and high/low in complexity, the automation script has to be modified. You may need AI for tasks that are more complex – e.g., where unstructured data is involved.
  2. Know the fallouts of DIY RPA. Lenders (particularly large banks) often choose to build RPA in-house in a bid to save capex and retain control. In reality, in-house RPA initiatives tend to lose steam in the mid to long-term as resources change, new initiatives come up, and funds are reallocated. Instead, consider partnering with an RPA vendor who has specific expertise in the mortgage industry.
  3. It is a good idea to start small and invest in governance. As mentioned, there are several low-hanging fruits in mortgage automation that can give you returns without disrupting your entire process landscape or infrastructure setup. But make sure that implementations, no matter how small, are governed by a clear owner and preferably a center of excellence.

Why RPA is Instrumental for Lender Success in 2022

RPA was always poised as a sort of “magic bullet” for the mortgage sector due to the sheer degree of efficiency it unlocks. In 2022, lenders can no longer put off implementation. The US market is now extremely diverse, with non-depository originators (50%), digital and direct-to-customer players (over 25%), and traditional banks competing for customer attention. Profit margins are precarious, with 31% of US lenders expecting margins to stay the same and 65% expecting a slight decrease in the next three months. Meanwhile, laws like the CARES Act on forbearance are compelling mortgage providers to renegotiate customer relationships and navigate new processes.

RPA makes your job easier by looking after the repetitive, high-volume, often mundane, but always essential tasks involved in a mortgage processing lifecycle. At Nexval, we have over a decade’s experience in mortgage automation, with 40+ tools and 90+ successful implementations.

Speak with our Tech Gurus to know how our expertise could fit into your automation journey.

Subhankar Saha

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