Imagine that Chris wants to buy a house and needs a mortgage. He applies online and receives an email from an intern asking to schedule time to discuss his interest. The intern conducts the initial screening conversation and then schedules an in-person interview, where Chris is interviewed by quite a few folks who ask many questions.
Chris is curious because nobody asks about his current job or how he expects to pay the mortgage. Nobody asks about his credit or past credit history. They seem more interested in his current zip code, engagement in his current community, and other demographics. They ask if it’s okay to review his social media data, which feels invasive and perhaps irrelevant, but he agrees because he really needs the mortgage.
Success! Mortgage Is Approved
Chris is surprised when the lender approves him for the loan. He's done his own budgeting and it seems risky. But he really wants the house. Approving mortgages is their business, so he places some value in their risk assessment process. Shouldn’t they know the factors that can predict who can pay off their mortgage?
Six months later, Chris struggles to pay his mortgage and begins to fall behind on payments. He explains his situation to the mortgage manager, who is able to restructure the mortgage, giving him more years to pay it off and smaller payments. He works with the lender’s credit counselors and even agrees to attend training to learn more. Yet he still struggles; he just can't pay his bills. Finally, Chris files for bankruptcy. He loses his home. The lender experiences a terrible financial loss but repeats the same exact process with the next person who wants a mortgage.
Obviously, this isn't how lenders make decisions about extending mortgages. They'd go out of business. Lending money is a repeatable process that produces predictive data. Lenders know that there are factors that can help to signal which loan candidates have a higher or lower probability of paying their mortgage.
How Do Lenders Predict Which Borrowers Can Pay Their Mortgage?
To stay in business and earn a profit, lenders need to predict which candidates are a good risk before extending an offer. Once the offer has been extended, all the company can do is restructure the mortgage and train and support borrowers to hopefully prevent borrowers from defaulting. How useful would it be to conduct a risk assessment of the borrowers after the mortgage has been extended?
It’s too late once they enter the lender’s ecosystem. Fortunately, lenders have a lot of data to study. They are able to find factors that predict the outcome they are looking for: borrowers with a greater probability of paying their mortgage.
How Does Credit Risk Relate to Hiring New Employees?
Every day, businesses extend job offers to new employees to join their business ecosystem. Like lenders, they take on a risk when a new employee is brought on board. Businesses hope the new employee will add value and not be a drain on the system, but they don’t know for sure. After three months, businesses can usually tell if the employee will be an asset or a liability. Yet at that point—like the credit risk example—all the business can do is support and train the employee to prevent that person from being a low-performing employee.
Like lenders, businesses need to be able to predict—before extending an offer—if candidates will likely be successful in their prospective role.
It Is Possible to Predict Employee Success
These same data analysis approaches used by lenders can be helpful in predicting employee performance before they’re hired. One thing data scientists know is that you don’t always have the data you need inside your own business. Marketing regularly licenses additional data about their prospects and customers to augment what they already have in house. Lenders pay for credit history and credit score information in order to have the most complete data set possible.
Deloitte’s 2014 Global Human Capital Trends report says that only 14 percent of HR departments “have the capabilities to utilize talent analytics—a critical function as HR becomes more data-intensive.” There’s clearly a better way. Lenders don’t take risks with financial capital, so why would you gamble on human capital?