Machine learning helps mortgage provider with its digital transformation
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A company can claim it's undergoing a digital transformation and then make some minor tweaks in the data center and call it a day. Carrington Mortgage Holdings, a holding company whose primary businesses provide a range of real estate services, isn't just talking the talk when it comes to transformation.
The business, which offers mortgage and other lending services in almost every area of the real estate industry, recently launched initiatives in areas such as robotic process automation (RPA), digital document automation, and advanced data modeling and predictive analytics into mainstream production.
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In one of the most significant moves, Carrington is deploying machine learning (ML) for advances in process efficiency, enhancing the current data modeling and predictive analytics efforts, and also for use cases in the area of quality improvement and compliance.
A current effort is underway to adapt ML into the emerging paperless mortgage environment," said Brent Rasmussen, CIO at the company. "Given the vast selection of home loan providers available, machine learning has the potential to help buyers quickly find the home and financing options that are right for them, vastly simplifying the customer journey," he said.
Digital document capture, recognition, and data extraction rely on strong ML to "learn" the vast number of document types, formats, and content used in the mortgage lifecycle, Rasmussen said. "However, the ML journey does not stop with document intake and workflow," he said. Integrating ML with predictive modeling helps to continuously enhance Carrington's ability to offer customers the right products and services at the right time.
"The value of machine learning is rooted in its ability to create continuous improvement in efficiency and the accuracy of processes and data models, at a scale that was not achievable before," Rasmussen said. ML methods "are particularly applicable when it comes to powering new insights within the home loan industry, because the market conditions and consequently the data sets change quickly."
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One example is that ML has the power to help the company proactively reach out to its customers with personalized assistance before they encounter serious issues with their mortgage. Combining a satisfying home buying experience with a transparent, customer-friendly financing process will be vital components of future home lending, Rasmussen said.
One of the more intriguing applications of ML comes when it is integrated with RPA, Rasmussen said. "RPA is able to leverage the abilities of ML by consuming model and parallel processing outputs in near real time," he said.
In addition, with RPA, the continuous improvements aspect of ML are adopted into the product processing, immediately delivering improvements for customers. Carrington is currently pursuing the integration of ML and RPA in its title, loan servicing, and lending divisions.
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