CPCU panel probes the mechanics and the rationale for controversial tool

The Society of CPCU's annual meeting in Orlando last October included a panel on credit scoring which featured companies that are active in providing credit data to insurers. The panel consisted of the following persons:

William T. Atkins, CPCU (Moderator), V.P. Personal Lines for Pacific Insurance

Lamont D. Boyd, CPCU of Fair Isaacs, Inc.

Gary Skerl, Progressive Insurance (responsible for building their Credit Base Scoring algorithm)

John B. Wilson, Risk Modeller for Choice Point

Gregg L. Antenen, President, Convergent Data

Seminar moderator William Atkins admitted in his introduction that creditbased scoring is facing backlash. Several experts on credit-based scoring joined to discuss different aspects, with a focus on how to speak to the public about this critical issue. The seminar then opened with each panelist making some comments. A question and answer period followed.

Lamont Boyd, who has a great deal of experience in credit-based scoring through his work at Fair Isaacs, explained that their business purpose is to generate data and analytics to help businesses make more efficient decisions. In his opinion, the development of credit-based scoring falls precisely within that purpose. He emphasized a distinction between credit scores (predictors of creditworthiness) and credit-based insurance scores (predictor of loss propensity). Speaking of the 1996 NAIC White Paper, he pointed out that it referenced the Tower-Tillinghast study that independently confirmed a significant correlation between credit scores and loss ratios. Boyd also noted that nothing is used in credit-based scoring formulas that can negatively affect (unfairly discriminate against) insurance consumers.

Gary Skerl from Progressive Insurance Company said he is a believer in the correlation between credit-based scoring and loss predictability. Progressive uses its own algorithm to create its C-B scores and is developing the following:

1. New Generation Credit Scores-a simplified model that uses nine rather than 16 variables. The algorithm calculates scores by assigning everyone a base score of 100. Then they either deduct or add to the base to create a score ranging between 49-228, with the lower number being more desirable. Their model will be used nationwide and will be filed in those states requiring such plans to be filed.

2. How to deal with consumers who have either no credit history or insufficient credit histories ("no-hits or thin files")-Skerl mentioned that some states are requesting that this class of insureds be treated (grouped) as average or best credit score groups. The elderly no-hits, which is a small segment of no-hits, has better loss experience, so Progressive plans to break out this group vs. younger drivers who do not have established credit.

3. Credit Assistance Team-The team responds to consumers who call a toll-free number with concerns over how they've been affected by use of their credit history within an insurance transaction. The company will use the team to help consumers in the following manner:

a. Personal Insurance Credit Report-Skerl explained that this document is a two-page breakdown that shows an individual's variable score and how it compares with aggregate scores.

b. Progressive will consider extraordinary events that affect scores and also consider past credit history. For example, it will give full consideration to a crisis medical situation or the fact that a person's past history had been quite positive for an extended amount of time, but may have just recently deteriorated.

c. The company's Credit Assistance Team will help with correcting credit history errors by showing insureds/applicants whom to contact in order to take care of credit report problems.

John Wilson of Choice Point explained the development of his company's approach. Rather than using proprietary information, Choice Point chose to use sources for its scores that would allow it to share its model information with agents, regulators, consumers and others. A Power Point presentation explains credit-based underwriting scores to agents. Choice Point has met with regulators to discuss how they created their scoring model (in the hopes of gaining greater regulatory acceptance).

Wilson addressed the possibility that regulators might ban the use of credit-based scoring. In anticipation of that possibility, Choice Point is performing studies on alternatives, such as loss history on other lines of business (e.g., homeowner loss history as a predictor of auto loss) or information on prior coverage history.

Gregg Antenen noted that his company, Convergent Data-which is less than two years old-has a live product that uses sources other than credit to predict the likelihood of losses. Those sources include check writing and sub-prime data. Gregg pointed out that individuals write checks in many instances and their respondent companies collect information on:

* How many times an individual writes a check