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10:38 AM
Grant Brown, Profluent
Grant Brown, Profluent

3 Untapped Data Sources for Nonstandard Underwriting

The nonstandard auto insurance market can be a profitable growth area for insurers who recognize its unique traits.

While existing underwriting tools that use credit file data are effective when applied to the standard insurance market, these tools are not as effective when applied to a large segment of the nonstandard auto insurance demographic. Specifically, reliance on data from: the major credit bureaus and public files such as LexisNexis produce incomplete, inaccurate and often no results. This can translate into higher premiums, (which can make insurers less competitive in the market), weaker underwriting, and greater exposure to fraud and identity verification challenges. Lack of visibility into this often transient or discreet customer group becomes even more challenging and potentially costly when we take into context the growth of this population of consumers.

Grant Brown
Grant Brown, Senior Consultant Partner and CEO, Profluent ePayment Consulting

There is a high correlation between Non Standard Insurance customers and the underbanked. A recent report by Stone Ridge Advisors states:

...The [nonstandard] sector has evolved to include drivers who purchase insurance policies with the state mandated minimum limits, typically lower income drivers or recent immigrants to the United States. The customer base is also characterized as one that typically pays for an auto insurance policy on a monthly basis, makes purchasing decisions based primarily on the cost of the initial down payment for the policy, and has a high cancellation or non- renewal rate. Higher rates of insurance fraud and staged accidents are also more prevalent among the non-standard auto customer base than among the standard or preferred auto insurance customer base.

This last evolution of the nonstandard definition overlaps well with those within the underbanked segment who have financial challenges that disproportionately weight their insurance purchase decisions. When underwriting policies, many insurance organizations access credit reports' insurance scores, or use their own scoring tools which may included credit information as a variable within their models. Often the underbanked produce little to no information (thin file or no hit) when their credit report is accessed through the three major bureaus.

[Why American Family bought nonstandard insurer Permanent General]

So what's so bad about the credit bureau data and public file that we use now? When data on the underbanked is returned from credit bureaus, typically in the header field, it is usually stale and subsequently not as relevant. The data can be as much as seven years old. Using trade line data to illustrate this point can be elusive given that the underbanked, as mentioned earlier, often produce thin file or no hit returns. Time at a given address might be better means to demonstrate how applicable traditional bureau data is. According to FactorTrust's Underbanked Index (May 2013) the underbanked average two years at the same address. Given the frequency of changes among this demographic, traditional bureau data which can exceed two years, may no not be as effective at reducing fraud and executing other underwriting practices.

Existing bureau data also fails to use non-traditional data assets to develop a more accurate profile of the underbanked customer. Non-traditional information such as IP addresses and payroll data, can also support fraud prevention, improve employment, and Identity verification along with other stability measures. Payroll data, for example may be good to consider when attempting to improve premium stability. IP address can contribute to fraud preventions efforts.

[From I&T sister publication Bank Systems & Technology: How banks use mobile to reach the underbanked]

Finally, public data that is available via products like LexisNexis often does not provide visibility into recent immigrants as they may overlap with the nonstandard auto insurance customer. A recent FDIC Study (2011 National Survey of Unbanked and Underbanked Households) claims that Hispanics — one of the fastest growing consumer segments — make up 28.6% of the underbanked population. Their participation in the nonstandard market makes having the most accurate up to date data critical to success. As the Stone Ridge Advisors report says: "Given the projected growth in the Hispanic population in the U.S. and their growing share as part of the non-standard auto sector, we would expect continued growth in the non-standard auto market."

Growth in the underbanked market will require better data tools to predict behavior, identify consumers and reduce fraud. Here are three ideas:

1. Supplement credit bureau data with non traditional data

Many insurance organizations have integrated third-party data, such as claims records, into their predictive models. Adding non-traditional data is a continuation of this logic. Most nonstandard insurance providers are well versed in handling, interpreting and capitalizing on data, but even the most sophisticated ones are starting to identify and integrate additional third party data in order to improve their models. Actively identify and partner with the kinds of organizations that can furnish your organization with meaningful data assets.

2. Work with a bureau that specializes in the underbanked

Leverage third-party models scoring and data ingredients as you execute your process. Working directly with an FCRA Compliant Credit Bureau with data on the underbanked can improve fraud reduction, identity verification, stability and marketing in a meaningful way. FactorTrust not only provides regular updates on the underbanked, it also develops predictive models and scoring and and raw data elements that insurers can use as ingredients in their home grown modeling and predictive tools.

3. Obtain Real Time Data

Individuals within the underbanked segment tend to operate in time frames of weeks rather than months given their cash constraints. Having current data is important for making underwriting decisions. As you overhaul your systems, work towards real-time updates. Obtaining real-time data should be an objective both for internal systems as well as partner and vendor technology. Having a current update on the underbanked consumer that your servicing, will support your efforts to minimize risk, fraud, and cost. It can also mean the difference between being able to price a product and not having a consumer that meets your underwriting requirements.

About the Author: Grant Brown is senior consultant partner and chief executive officer of Raymore, Mo.-based Profluent ePayment Consulting.

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User Rank: Author
9/30/2013 | 4:34:23 PM
re: 3 Untapped Data Sources for Nonstandard Underwriting
This is where there are some interesting strategy vs. tech capabilities vs. regulation/politics discussions come into play. As you both noted, the technology tools to underwrite this kind of risk much more accurately exists. After all, as part of big data strategies companies will be eager to tap in all kinds of unstructured data & at least some of that is going to be untraditional credit info.But just because your CAN do it doesn't always mean you SHOULD do it, and the nonstandard market is a perfect example of this. Perhaps the opportunity is that better use of data will enable this market to be segmented in different ways that reflect new ways of evaluating risk. The underbanked model is helpful -- keep in mind that underbanked is a broad term, doesn't only mean poor people, or immigrants -- could mean students, freelancers, etc. More and more people of all incomes are bypassign banks for financial services.
User Rank: Apprentice
9/26/2013 | 4:28:18 PM
re: 3 Untapped Data Sources for Nonstandard Underwriting
Very interesting read. I also tend to agree that given the recent economic crash that credit scoring and its link to loss frequency and severity may not apply as much in today's market. But the one thing you must consider as a non-standard writer is that if you are not doing as your competition does, which is factor in credit, you will end up getting the business that no one else wants. You end up being the target of everyone who does not have good credit which will, to some degree, still leave you with risks that are proven to be linked to high loss ratios. I hate to say "when in Rome" but when all of your competition is offering discounts based on what you describe as "aged" information, you cannot simply ignore the same data and not comply or you lose your competitiveness for the risks you want. A smart carrier will factor in other reporting like A-Plus, CLUE, and newer products from TLO to mitigate the unreliable credit data and increase their automated underwriting efforts.
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