Category Archives: Reverse Reengineering of Risk

Reverse Reengineering of Risk

When credit-risk scoring came into usage in the 1970s, it ushered in an era of science-based decision making that was primed to end the judgmental and biased lending decisions of the past. As the recent mortgage crisis has exposed, the science of risk scoring needs some tweaking. The industry needs to add critical measures of financial soundness to scoring and re-embrace common-sense judgment. Doing this won’t turn back the clock to the days of restrictive lending-or demonize creative lending products-but will lead to sounder lending practices that will help the housing market recovery more quickly.

The magnitude of the current crisis makes it abundantly clear that there is significant room-and need-for improvement in current credit-assessment approaches. There are two fundamental problems that contributed to the weakened underwriting standards and degraded loan quality. First, credit scoring has not done an adequate job of assessing risk in the subprime mortgage market. Most subprime mortgage underwriting systems were not, in fact, capturing the full range of risk factors in the market. This was particularly true when their conventional risk models were applied to non-conventional loan products, which are associated with different payment terms and behavior. Lenders who depend on these credit-scoring systems were measuring credit risk inaccurately and incompletely. Second, there is a blind spot in today’s underwriting practices. Current practices rely too heavily on quantitative models and automated underwriting systems. Technology has a vital role to play in boosting efficiency and helping measure and monitor credit risk, and the models have their place and role to play. However, institutions must control the models instead of the other way around. Loans need first to be properly classified, and then risk rated. Today’s process has that backward.


As the accuracy and power of the FICO score continue to be debated, what’s needed are new and improved ways of addressing limitations of credit-scoring systems and better evaluating of credit risk. Simply recalibrating existing models and throwing technology at the problem will not fix it. A comprehensive new credit-risk framework is needed-a hybrid approach that combines the best that technology can offer with expert human judgment. Such an approach can help deal with the current crisis and may lessen the extent of, or even prevent, the next one. This approach is the comprehensive credit assessment framework (CCAF). The CCAF uses advanced computing technology and a sound, safe model development and validation process. The robust and flexible CCAF approach naturally affords a sustainable and sensible segmentation based on all primary credit factors and then offers a systematic means for taking appropriate actions relative to those identified segments. It also provides ongoing monitoring of the impact of those actions in a comprehensive and efficient manner.

CCAF accomplishes this by first expanding the boundaries of information. Our risk models need to include income and secondary examples of good payment behavior, like utility payment history. The industry also needs to factor in borrowers’ capital. And it needs to stop dunning people for getting a better-paying job by putting them in the credit “penalty box” if they’ve held the job for less than two years.

Second, CCAF appropriately segments loan applicants based on primary factors. A client with a lot of debt-and a lot of capital-should be in a different segment than a customer with the same debt load but little capital. The argument from the 1970s that income isn’t a good indicator due to inflation no longer holds water.

Next, CCAF will layer in needed secondary qualification factors. Research has shown that people who operate on a cash basis-they pay with cash and they save-don’t get the best terms as compared to those who carry installment debt. Yet “cash basis” borrowers will often be better risks. Bank balances and a history of automatically deposited savings need to be added considerations in modeling risk.

Fourth, CCAF assigns actions for each identified segment. In the recent past, lenders have focused on finding the loan with the monthly payment that a borrower could afford. Instead, lenders need to focus on matching the borrower to a loan product that the borrower will be most successful at paying off.

Fifth, CCAF puts in place an adaptable policy mechanism that is responsive to the evolving economic climate. Lenders need a model that has a feedback mechanism. It should factor in what segments are defaulting with what loan products and bring current economic conditions-interest rates, local unemployment rates and local housing price escalation or de-escalation-to bear in deciding who qualifies and under what conditions.

Finally, CCAF models future scenarios to determine whether the borrower can tolerate actions like interest rate resets. Credit scoring looks at the past. CCAF looks at the best case, worst case and most likely scenarios for different types of loan products. For instance, a lender can model the “worst-case” scenario for an interest rate increase, reset for an ARM and determine-based on the home’s value and the borrower’s assets today-whether the borrower could tolerate the reset. This could help lender and borrower avoid a loan that has a stronger potential for creating hardship and foreclosure.

Reverse Reengineering of Risk

Reverse Reengineering of Risk

The writedowns and bailouts are legendary, but some industry experts argue the credit crisis didn’t have to happen. Simply put, an overdependence on technology and absence of judgment fanned the flames that have burned investors and taxpayers alike. It began with traditional credit scoring and analytics that made some pretty silly assumptions, like incomes don’t matter much and people who prefer to pay in cash are riskier bets. After billions of writedowns, isn’t it time the industry learn to assess risk properly?

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