3 Ways AI and machine learning are disrupting mortgage lending

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Ellie Mae recently surveyed 3,000 millennials, Gen Xers, and Baby Boomers with surprising results. Borrowers across all three groups valued the same things in their mortgage experience – speed, convenience, security, and personal interaction. The key takeaway: customers, regardless of their age or gender, are looking for an efficient and frictionless mortgage process – a far cry from the time consuming and complex traditional process.

The good news is next-gen technologies are creating new avenues for lenders to move beyond routine automation and leverage analytics-driven insights to improve customer experience. Unsurprisingly, forward-thinking lenders are turning to AI and machine learning to create competitive differentiation in three major areas – cost, compliance, and customer service. Here’s why.

  1. Optimize efficiency and costs. AI-based self-learning algorithms extract data from images, pinpoint data anomalies, develop validation rules, and most importantly, take decisions faster than humans. As opposed to traditional tools that use statistical modeling to generate credit scores, machine learning aggregates data from a wide variety of structured and unstructured sources to deliver a more rigorous picture of the borrower’s credit worthiness. It replaces a one-to-one manual comparison of borrower data with a model than instantly captures the relationship between thousands of data points. In effect, the algorithms can identify gaps in borrower applications, predict bad loans, and identify new opportunities – all within a fraction of a second. The result: increased accuracy, reduced costs, and enhanced efficiencies across the lending process.
  2. Enhance compliance. Compliance is a major source of costs and delays in the lending cycle. Current compliance systems identify and reduce errors by kicking non-compliant loans for further manual review, escalating costs as well as turnaround time. A deep learning system, on the other hand, automatically checks for compliance with federal and state requirements in real time. It combines domain expertise with borrower data, business rules, and predictive analytics to identify errors and flag compliance violations. AI-based systems also offer advice on corrective actions to be taken with respect to procedures and workflows – to help fix the gaps. In essence, they transform compliance without sacrificing speed or efficiency.
  3. Boost customer experience. In a survey conducted in March 2018, Ellie Mae reported that it took 41 days to close a mortgage during the month. Lenders can reduce this time by deploying online portals for application submission and using machine learning to verify and track borrower information. AI-based tools such as chatbots and virtual assistants are adept at analyzing data to efficiently guide borrowers through the lending process and deliver personalized experiences.

As more tech-savvy individuals enter the mortgage market, lenders will be increasingly judged on the user-friendly experiences they offer. To thrive in the new reality, lenders must put customers at the heart of their operations, and reimagine their lending process using AI and machine learning.  While organizations can choose to build in-house capabilities for developing and implementing AI and machine learning based solutions, it is a time consuming and expensive process that requires investments in infrastructure and resources. Partnering with an outsourcing service provider who offers the right combination of domain expertise and proprietary tools can help lenders fast track machine learning benefits with little upfront investments.

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