MReport May 2019

TheMReport — News and strategies for the evolving mortgage marketplace.

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22 | TH E M R EP O RT FEATURE Better Assessments B y using the insights gleaned from big data, lenders can learn a lot more about borrow- ers with thin credit files—mean- ing people who haven't tapped enough credit to be judged on just a generic credit score. For exam- ple, a lot of millennials don't take out car loans, use credit cards, or work as salaried employees the way their parents did. However, they do have bank accounts, pay cell phone bills, and often use mobile payment apps—all of which can help lenders to decide whether they're a good credit risk. In the same way that lenders can build alternative credit pro- files for millennials, they can also do this when evaluating mortgage applicants from underserved communities, many of whom lack definitive credit histories. First Impressions A digital lending platform isn't just an online mortgage application form, but rather an in- teractive experience that meets or exceeds consumers' expectations. Would-be borrowers will quickly leave a lender's website if it asks them too many questions only to recommend generic loan prod- ucts. Big data usually draws on internal analytics and third-party information to come up with the best options almost immediately. If prospective customers begin shopping for loans on a lender's web-based platform, this initial interaction can accelerate the sales process, with loan officers or sales representatives following up with a phone call. Increasing Efficiency I n the face higher interest rates, declining volume, and high production costs, lenders need to control costs and preserve profit margins. They can integrate big data analytics into existing systems to digitally process applications, speed up underwriting, and better onboard customers. For example, with a customer's consent, a lender can more efficently gain a broader picture of borrower financials with third-party data providers, such as employers, banks, brokerage firms, and credit bureaus. In addition to enhancing data integrity, machine-learning models can help lenders prevent last-minute delays by flagging a data point that requires further investigation. For example, if the system uncovers a large deposit or withdrawal in a borrower's bank account, the pro- cessor or underwriter can request clarification via an account status alert, with the customer's answer feeding into the analytics applica- tion assessing her overall credit risk. The speed and efficiency of approving and closing a loan directly affect production and underwriting costs. With more comprehensive, better-organized, and easily searchable data loan processors can deliver higher- quality files more quickly to underwriters. Underwriters can focus on automated, flagged exceptions rather than having to discover them with "stare and compare" work. This can help shave days off the lending cycle timeline. Maximizing Servicing Returns S ervicing is a highly com- moditized, volume-driven, low-margin business. With big data analytics, servicers stand to maximize collections and better control costs by helping them to identify borrowers on the cusp of missing an upcoming payment and optimize customer outreach to reduce delinquencies. Within this new analytical paradigm, servicers can supple- ment existing customer data with borrowers' current credit card balances or payment status on student debt or car loans. Machine-learning models rate these variables against credit risk scoring methodologies to deter- mine the most effective, cost- efficient means of working with at-risk borrowers. When it comes to predicting actual delinquencies and defaults in a servicer's portfolio, a model is only as good as the data supporting it. Today, the volume of relevant data exceeds the storage capacity of traditional warehousing systems. However, with big data technology, servicers can run machine learning- driven models in a quicker span of time without consuming additional computing power. Getting More B y tapping into big data, lend- ers can gain additional insights into borrowers beyond what they learn from credit scores and tax returns. They can get a better, more recent picture of borrowers with credit card transactions and income fluctuations reflected in their bank accounts. Once they aggregate this data, it's easier to more precisely segment customers and market products best suited to their needs at any point in their lives. Models powered by machine-learning algorithms iden- tify correlations or reveal hidden trends to help a lender extend the reach of marketing campaigns to generate repeat business or win new customers. Detecting Fraud W ith their shift to digital services, lenders must stay on top of fraud, especially since the mortgage industry is the most frequently targeted sector in financial services. At the same time, they don't want to lose legitimate business or run afoul of regulators by rejecting applications too aggressively. Fortunately, big data analytics can help to balance these competing goals. Fintech vendors, lenders, and third-party data suppliers are moving beyond traditional detection measures dependent on siloed data and manual processes to distinguish actual fraud from anything flagged as suspicious activity. Their technology can help lenders to minimize false Embracing this technology often requires a new managerial mindset toward risk, and such analytics are not an overnight fix. However, it can yield significant benefits within a relatively short time period and reduce the costs of relying on legacy systems.

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