MReport May 2019

TheMReport — News and strategies for the evolving mortgage marketplace.

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TH E M R EP O RT | 23 FEATURE positives and spot questionable transactions within seconds, and then score risk against thousands of variables. Embracing this technology often requires a new managerial mindset toward risk, and such analytics are not an overnight fix. However, they can yield significant benefits within a reasonable time period and reduce the costs of soley relying on legacy systems. Strengthening Compliance M anaging compliance risk is a herculean task due to the matrix of mortgage lending regulations and housing finance programs that the government put in place after the 2008 financial crisis. Lenders now answer to a lot more government entities that want information sliced and diced differently depending on what part of the business they scruti- nize. With the help of big data, lenders can integrate systems and models—which have been siloed in different departments—to speed up compliance and reporting and upgrade reporting quality. To promote regulatory compliance, lenders have traditionally managed their day-to- day business by focusing on one loan application at a time, as it's processed through the mortgage cycle. Operations departments are tasked with feeding application data into systems that are then maintained by risk management. Machine-learning models can draw upon massive volumes of data delivered in real time to test a portfolio against a broad range of compliance criteria, and more accurately predict problems before regulators point them out. Analytics and Big Data in Action E very lender knows that selling to existing customers is less costly than acquiring new ones, but success depends on improving the borrower experience. In early 2018, a Minnesota credit union used big data analytics to target only 1,400 members after calculat- ing the amount of money they'd save by converting into short-term mortgages. By refining its target market, the lender maximized its marketing dollars and wrote nearly $30 million in new loans. Using third-party data for income and asset verification, instead of requiring documents from borrowers directly, is increasingly common to ease the documentation demands on borrowers. In March, a Georgia- based nonbank mortgage lender integrated an application through which it receives employment status, income, and W2s from a major credit bureau into its loan origination system. It reported that within six months, it had automatically validated borrower financials for about three-quarters of some 25,000 loan applications, valued at $6.5 billion. In doing this, the lender said it sped up its pipeline by about a third and cut closing times by about five days. Cost containment is the biggest challenge in the servicing business. An industry leader headquartered in Texas is using big data analytics to boost the productivity of its customer call centers. With more robust cost-benefit analyses, it's helping employees make the most of the time they spend talking to customers to prevent delinquencies and defaults. Using output generated by machine- learning models, the servicer has determined the best day and time to call customers whose payment activity deviates from prior months. For example, employees might wait a week to call a customer who has a high FICO score and regularly makes a direct deposit on the 12th of the month for payment due on the 15th. However, they might phone a borrower a day after her balance is past due if she has a history of late payments. A New Frontier T he success of any mortgage lender hinges on the scope and quality of its borrower data, the speed at which it can verify and process such data, the technology it uses to approve its loans, and the degree to which it can meet evolving customer expectations. With the help of big data and the analytics that make sense of it, lenders stand to extend credit beyond their traditional client base and do it more quickly and efficiently than they've been able to thus far. To take full advantage of advanced analytics, a lender should treat both internal and third-party data as equally valuable and leverage them across all its functions. DENNIS TALLY is a Director in the Single- Family Data division at Freddie Mac, overseeing a team responsible for creating and enabling the Single-family data strategy, which includes managing several shared data assets such as the Big Data Analytic Platform and Single-Family Datamart. He is also the business owner of tools supporting data capabilities from business intelligence to machine learning and has represented Freddie Mac on data and analytic partners' customer advisory boards for several years. Tally recently expanded his focus to transition Single-family work to the cloud, and his team is actively working to deliver a hybrid-cloud capability in support of data science and analytics use cases. Machine-learning models can draw upon massive volumes of data delivered in real time to test a portfolio against a broad range of compliance criteria, and more accurately predict problems before regulators point them out.

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