By Gil Nizri
It doesn’t take a savvy businessman to know there is a risk associated with poor decisions and a reward for making better ones. For lending companies, however, the risk and reward is even more severe. Lending the right amount of money at the right interest rate to the right person could make the difference between a paid or delinquent loan. Predictive analytics replaces guesswork with science, informing users about the likelihood of certain situations transpiring. Therefore, predictive analytics can play an important role in the loss mitigation process.
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A solid algorithm, which is reactive to dynamic market shifts, can ensure you don’t lose money.
Automated predictive analytics can impact three aspects of the lender-borrower relationship:
Potential Borrowers
When it comes to loan applicants, predictive analytics can rank potential customers according to their likelihood to pay or default on a loan. Lenders can use that information to segment borrowers into groups with similar behaviors and optimize their lending, setting cutoffs, below which the lender will either give a higher interest loan or not give one at all.
Lenders can also customize their loan products, offering different types of loans based on life stages and choices, and potential risk of default. This enables them to recommend the loan products that are the best fit for each customer.
Current Borrowers
When it comes to those loans that have already been distributed, lenders can predict outcomes for individual loans by leveraging borrower behavior data. If lenders identify future problem loans, if it appears loan repayment may be at risk, they can either restructure potential delinquent loans or assign the best human resources to the at-risk borrower, increasing the possibility of a successful modification.
Predictive analytics can also help paint a picture of how much money will be lost if a certain borrower becomes delinquent.
Repeat Borrowers
The best customers should be rewarded with better loans. And they should be maintained as customers, by encouraging them to take out additional loans for other needs. Predictive analytics enables lenders to make better loans for their best customers and to identify customers with loans that are scheduled to runoff, so they can find ways to market to and retain them.
THE LENDING LANDSCAPE is rapidly changing. Whereas in the past lenders could rely on business intelligence insights to help steer business, today lending companies are in need of predictive analytics, which delivers action. Predictive analytics offers insight on future customer behavior, which can help identify the best action to take on every loan.
Today, algorithmic models can be automated, allowing the art and science of predictive analytics to be democratized, meaning in the hands of every subject matter expert. By automating the predictive analytics process, decisions that used to take hours, days or even months can be reduced to minutes or seconds. By making predictive analytics accessible, it will become pervasive and have a greater ability to impact lending companies’ day-to-day work.
Technology is ever-evolving. The quantity of accessible data on individuals is ever-increasing. As modeling capabilities continue to advance, the value of predictive analytics will only increase, particularly for lending companies that make their livings by making the right decisions at the right time.
