Borrow Content Analysis to Curb Loan Defaults

When events such as the COVID-19 pandemic toss a wrench into the gears of the economy, lenders get scared. Even with government guarantees of payment to the lender in case of debtor default, money sources ranging from banks to brokers to crowdfunding sites prefer borrowers who will be able and willing to pay back the loan.
     Researchers at Columbia University and University of Delaware developed a novel tool for handling this challenge from what’s being called the novel coronavirus: In the text of the loan application, look for red flags.
     Some of this you’d expect as indicators of future default. Pleas which include tales of financial hardship and profound discouragement are understandably cause for the lender’s concern. Chronic pessimism or depression disrupts the ability to earn sufficient income to make loan payments over the long term.
     Mentions of God or family and a sense of high time pressure in the phrasing aren’t such obvious flags, but the research found them to also be telling. Among the more than 120,000 loan requests included in the study, talk of God and family seemed to be primarily intended to get into the good graces of the lender. The implication is of an effort to divert attention from the flawed quality of the application, which would account for why use of this phrasing portends payment default.
     A similar explanation applies to the finding that abundant use of words like “son” and “someone” were flags. Liars minimize mention of themselves, presumably to gain psychological distance from the falsehoods they spin. Liars also prefer talking about the short-term, presumably to postpone facing the consequences.
     The researchers intend an analysis of what the applicant says to supplement other criteria, such as credit history and loan amount. In this, the importance of the content analysis is greater than in the past. Lending platforms are moving online. The applicant may never have a give-and-take conversation with a loan officer. Past research indicates that such conversations not only allowed use of educated intuition in spotting likely defaulters. The interaction also built a commitment in the lender to pay back what was borrowed.
     Still, even when an interview with the loan officer does occur, explicit rubrics for evaluating the content help avoid unintentional discrimination. Those considering the loan are judging the applicant on the phrasing they use, not on their gender or residential neighborhood, for instance, in itself.

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