As a mortgage lender, you're likely aware that digitally transforming your lending processes offers several significant benefits. From reducing risk and operating costs to boosting scalability and profit margins and enabling you to deliver leading borrower experiences that breed 'customers for life.' Digitalization is a lever for the positive business outcomes that will allow you to compete profitably in a fast-changing world.
It’s no secret that credit score plays a crucial role in a lender's decision to assess a loan. However, does a high score really represent a creditworthy applicant? In this article we’ll explore why a credit score-centric model is the equivalent of judging a book by its cover. Then we’ll look at a few metrics to underwrite loans beyond the credit score.
Home buyers don't want to go through the hassle of waiting 30 days to close on their loan, which is the current average. The reason for this long wait is that much of the underwriting process in the industry is done without mortgage automation. Importing info from paper to digital is a problem since the error rate for manual data entry is 4%. Then there’s the rest of the steps involved in approving a mortgage.
Cross-selling for mortgage brokers happens when financial institutions, lenders, or individual brokers use information gathered during loan processing to pitch additional financial products. Cross-selling can be looked at as a form of customer retention. It's a hot topic in the industry because many mortgage brokers have questions about the pros, cons, and ethics of cross-selling financial products and banking products to get new customers that might actually be old customers.
Machine learning is in practically everything these days and is prevalent in the insurance industry as well as the financial services industry. When combined with artificial intelligence, machine learning provides businesses from all different types of industries with the ability to streamline workflows and automate much of the decision-making process. One sector that's set to benefit most from machine learning is underwriting.