Adapting Credit Scores to Evolving Consumer Behavior and Data

By Huynh, Frederic | Suffolk University Law Review, Summer 2013 | Go to article overview

Adapting Credit Scores to Evolving Consumer Behavior and Data


Huynh, Frederic, Suffolk University Law Review


I. INTRODUCTION

Credit scores are a vital element of the lending ecosystem, providing lenders with an objective means to assess a consumer's creditworthiness. Broad-based credit scores, such as the FICO Score, are redeveloped periodically to capture changes in consumer risk patterns, leverage improvements in the reporting of information to the Credit Reporting Agencies (CRAs), and incorporate new technological enhancements into the score algorithm. (1)

To demonstrate how consumer risk patterns have changed over time, we might consider the number of credit cards a consumer has. The earliest incarnations of the FICO Score typically incorporated a predictive variable that measured the number of credit cards a consumer possessed. In the nascent days of FICO Scores, having many credit cards was risky and having few credit cards actually represented good risk. Over time, the risk pattern associated with that variable fundamentally changed.

In Figure 1, we observe a noticeable change in the number of credit cards and associated risk. In 1992, having many credit cards indicated a high level of credit risk; in fact, a consumer with eighteen or more credit cards was twice as risky as the total population. In 1998, consumers with many credit cards were actually slightly better credit risks than the total population. In both time periods, having no credit cards indicated a greater degree of credit risk, but elsewhere the risk pattern fundamentally changed. While most predictive characteristics employed by credit scores demonstrate more stable risk patterns, this is one example that demonstrates the benefit of redeveloping scores periodically in order to account for changing risk patterns.

[FIGURE 1 OMITTED]

In addition to redeveloping the scores to accommodate for changing risk patterns, the building blocks of the score--the characteristics and the treatment of the primitive data elements--should also evolve. This Article presents three different examples of how the predictive characteristics of the score evolved to adapt for changes in the credit landscape. The first study focuses on enhancements introduced to an earlier generation of the FICO Score, released in the early 2000s. The second study focuses on enhancements that were incorporated into the FICO 8 models. The last study focuses on research that determines whether short sales and other codes signifying different types of mortgage stress events should be treated less harshly by the FICO Score.

II. CHANGING INQUIRY ASSESSMENT TO MORE EFFECTIVELY ACCOMMODATE RATE-SHOPPING

Credit inquiries have long served as predictors in credit-scoring models. Although they contribute a relatively small percentage to the predictiveness of the final score, credit inquiries often attract a disproportionate amount of attention from consumers. This presumably has been due to the prominence they receive on credit reports. Since the mid-1990s, FICO Scores have employed logic in the treatment of inquiries that recognizes the presence of rate-shopping behavior. There are two components of this logic, a buffer and a deduplication window. The purpose of the buffer is to bypass any auto or mortgage inquiries made within the last thirty days. This prevents very recent auto or mortgage inquiries from influencing any current applications for credit. The deduplication window is a rolling timeframe in which multiple auto or mortgage inquiries, posted to the credit report during the deduplication window, will be counted as a single inquiry.

The following table illustrates the general concepts behind the earlier inquiry logic. In this example, all of the auto inquiries occur within the last thirty days and are ignored. The two mortgage inquiries fall outside of the thirty-day buffer, and are eligible to be counted. However, since both mortgage inquiries fall within fourteen days of each other, only one inquiry will be counted. Even though the department-store inquiry occurs within fourteen days of the first mortgage inquiry, it is counted separately; only auto and mortgage inquiries are deduplicated. …

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