The Big Short: How the Credit Scoring World Has Shifted
By Infoglide Software CEO Mike Shultz
The hottest non-fiction book at the moment is The Big Short: Inside the Doomsday Machine. Best-selling author Michael Lewis explores and explains what went on behind the scenes during the years leading up to the big stock market crash in 2008 and answers a crucial question: “Who understood the risk inherent in the assumption of ever-rising real estate prices, a risk compounded daily by the creation of those arcane, artificial securities loosely base on piles of doubtful mortgages?” While misguided government policies together with greed and stupidity provide the larger answer, events during that time beg certain questions about the specific ways in which credit risk is evaluated.
For decades, several well-known organizations have assessed credit risk, i.e. the likelihood that a loan applicant will default on a loan. Financial services organizations like banks, credit card companies, and mortgage lenders base decisions to lend on credit scores like FICO. The scores are based primarily on a person’s financial history, including whether they have taken out loans and paid them back. Two major trends work together to hinder the effectiveness of traditional credit scores.
First, the use of credit cards as a form of payment has become ubiquitous, and a large percentage of people carry a balance from month to month. For example, according to creditcards.com the average credit card debt per household carrying a balance is over $16,000. It’s clear that an ever-increasing number of households depend on credit cards to manage cash flow.
The second trend is a behavioral one. For many years, past loan history was a reasonable predictor of future behavior. People in general were committed to paying off their mortgage, and if they were in a tight cash flow situation, their highest priority was keeping up their mortgage payments. Now, with zero interest down and interest-only loans, the lack of equity in the home translates to lower commitment, and defaulting on a loan is a less traumatic event. When credit cards are used for cash management and the penalty for mortgage foreclosure is not so high, it’s not hard to predict a much higher rate of foreclosures.
So, if past behavior is less predictive than before, expect a growing desire in the industry for more sophisticated measures that draw on both historical data and other sources, e.g. up-to-the-minute income and banking status. With its ability to combine and score disparate data, identity resolution technology is certain to play a key role in improving the financial industry’s ability to assess risk.