Anyone who has ever purchased a home can testify to the amount of paperwork involved – credit checks, employment verifications, insurance statements, inspection reports and more. Even the slightest error such as a missing date on a form can slow down the process and complicate matters, creating stress for both buyer and seller.
Robotic Process Automation (RPA) applications that execute routine, rules-based tasks without human intervention can significantly improve the mortgage loan experience. While banks have made substantial progress in automating basic business processes, work flow and functions related to mortgages, RPA can enhance efficiency by supplementing existing workflows.
Moreover, a key opportunity for RPA lies in identifying and resolving errors and exceptions that today require a high level of human intervention. For applications such as healthcare claims, process exceptions typically raise complex questions about policies and coverage guidelines that require human reasoning, judgment and expertise to resolve. However, a high percentage of errors and process exceptions in mortgage lending are routine and can be resolved by applying clearly defined, rules-based algorithms. As such, RPA is ideally suited to automating the resolution of these errors and exceptions and clearing the bottlenecks that frequently hinder mortgage origination.
Beyond improving the operational efficiency of loan processes, banks and other financial service providers have a unique opportunity to leverage smart tools to add business value. Consider the mortgage customer: he or she is a prime candidate for a wide variety of additional services the bank may offer – insurance, savings programs, retirement funds and the like. For a bank, the challenge lies in connecting the dots between a given customer – and what’s known about that customer – and the bank’s other offerings that may be of interest.
At present, that process is people-intensive and slow in many environments. The opportunity lies in combining cognitive applications that use pattern recognition and logic to analyze data and draw conclusions, together with RPA as a processing engine. So, for example, a cognitive tool could discern from John and Mary’s mortgage paperwork that they have two school-aged children and tag them as candidates for the bank’s college savings program. RPA tools could facilitate the process of getting John and Mary into the marketing funnel of the appropriate product lines.
In this context, the smart tools would complement a broader customer-focused strategy driven by data analytics, social media and other technologies, leveraging foundational capabilities provided by RPA. Moreover, the tools can significantly enhance the data collection and analytics that enable that critical customer insight. And robots can easily extend to other bank offerings such as customer self-service, by, say, following up ATM interactions with recommendations for improved safety and security of a customer’s savings or other assets.
Customer retention is a critical imperative for banks today. Unlike preceding generations, millennials will switch banks at the drop of a hat, so any opportunity to deliver a positive customer experience must be seized. Smart tools that enhance operational efficiency and facilitate insight into customer preferences can be a key ingredient to a winning competitive formula to drive customer loyalty and enhance results.