Data is always available in abundance, and it is up to every company to make the best use of it. More and more organizations are realizing this point and becoming data driven. And companies in the mortgage industry are also utilizing data analytics services to determine if a loan should be sanctioned.
The loan lenders should be able to determine a customer’s financial background and intention to repay. Lenders in the past used limited data combined with a set of policies and processes to answer those questions. Today, companies use all types of data, ranging from transaction data to social media behaviors to make lending decisions. Besides, data analytics tools help to understand data better and use it in extensive ways. Here is an article on how data analytics is improving the mortgage industry.
1) Selecting customers
Data analytics helps lending companies and banks to segment their customers easily. It enables lenders to find customers’ financial status, spending patterns, and credit preferences. The lenders gain consumer insights when their business becomes more data driven. Besides, the lending organization can effectively direct their marketing pitch and offers to the correct customers.
2) Strategizing mortgage offers
Loans may not be offered the same way for every customer, because the characteristics of every customer are different. Mortgage businesses achieve attain optimized loan allocation and pricing when the offer is completely customized. They provide personalized interest rate, tenure, and loan amount. For example, a high-interest rate loan is offered to a borrower with a poor credit history, along with enough collaterals. Simultaneously, a low-interest rate loan is offered to a borrower with better credit history and stable income. Data analytics helps lenders to create customized loans in real-time. Consequently, conversion rates also increase drastically.
3) Determining the probability of delinquency
Sometimes borrowers posing as the perfect customers can turn out to be uneventful. They can display erratic payment behavior once the loan is approved. This is a difficult situation to predict, especially at the point of loan initiation. But this behavior risks the probability of full principal repayment along with the interest. As a result, lending institutions get into trouble.
Estimating the probability of delinquency helps to thwart the above situation. The prediction models include data of previous loans and transactions. It considers how many times the borrower has not paid the full amount previously. Also, it considers how many times the borrower has paid past the due date. These models help in determining the credit renewals and finding if the borrower can stick to the repayment schedule.
4) Understanding customers better
Mortgage companies apply data analytics to better understand their customers and maximize their collection yields. Traditionally, the lending businesses classify customers based on few risk categories and apply different contact strategies. Nowadays lending companies use advanced data analytics to understand their customers better. Also, the customers are organized into micro-segments to make faster decisions on loan approvals.
5) Detecting fraud
Credit fraud has always been a major concern for banks and mortgage institutions. Third-party customer data like government IDs, smartphone usage, and social media presence helps to validate the customer’s authenticity. It helps to keep a continuous check on the potential credit frauds, especially after the loan has been sanctioned.
6) Automating workflows with Machine Learning
Machine learning models enables better decision making at every point of the lender’s workflow. It helps to automate the end-to-end loan processing, from pricing approval to loan monitoring. Accordingly, there is a decrease in business costs and time consumed during internal loan processing.
Multiple data sources and tons of data to analyze necessitates data warehouses or data lakes with relevant analytics tools. An organization goes like a herd of sheep without a shepherd without data analytics on its side. This is very true for the mortgage industry too. The lending institutions need to keep up to the pace by integrating data analytics into their business processes.