Podcast Center
Video Center
Webcasts
Resource Center
Events
November 14, 2016 at 03:05 AM
Compliance isn’t the only pet peeve of agents and advisors. Another is underwriting.
The rating of a life insurance policy, which can make a purchase untenable for the client, is one source of annoyance. Potentially more aggravating is the wait time — the worst cases extend to weeks or months — in procuring physician statements or other health data needed to bind a policy application.
Related: How to disrupt the high-tech disrupters
Such delays may soon be a thing of the past. A myriad of “insurtech” and “fintech” players are joining a burgeoning market for solutions that automate underwriting. These include software and services that not only assess the policy risk but also provide workflow, audit and data analytics capabilities.
A long-established company in this area, Alpharetta, Georgia-based LexisNexis Risk Solutions, is betting that software which speed policy transactions times by dispensing with medical underwriting will prove transformative to the market. The company’s claim to fame, Risk Classifier, aggregates a mountain of information — billions of public records and more than 20,000 data sources — to assess mortality risk for a policy applicant.
Related: Aequalis: leveling the playing field for those living with HIV
The kicker: The tool can use this data to approve preferred policies — no blood work of doctor’s record needed. For advisors and their clients, the benefit can be measured not only in fewer hassles and speedier policy binding, but also greater convenience. When paired with a self-service web portal offering other digital capabilities — product education, a needs analysis, online application processing — the tool can secure coverage in minutes.
To learn more about the offering, LifeHealthPro interviewed Elliot Wallace, a vice president and general manager for life insurance at LexisNexis. The wide-ranging conversation — conducted in advance of the National Association of Independent Life Brokerage Agencies‘s annual meeting, taking place Nov. 17-19 in Dallas, where the executive will be representing the company — explored topics likely to be of keen interest to life insurance brokerages and affiliated agents.
Among them: data analytics capabilities underpinning the tool; the ability to assess mortality risk using motor vehicle record records; and how “accelerating underwriting” may evolve amid other trends buffeting the industry. The following are excerpts:
Related: S&P panelists: Insurance industry is ‘ripe for disruption’
Policy applicants with multiple points on their motor vehicle records are more likely than not to have an underlying health issue that could evolve into a life-threatening condition, such as liver cancer. (Photo: iStock)
LHP: What are LexisNexis’ objectives at the NAILBA annual meeting? How might NAILBA’s members benefit from the LexisNexis data?
Wallace: LexisNexis Risk Solutions main objective is to lead discussions around accelerated underwriting and meet with carriers, distributors and brokers to answer their questions about data, analytics and how the distribution broker community can leverage new processes to increase their businesses.
Brokers, distributors and the entire life insurance community are going to benefit from the transformation we’re experiencing now. Consumers have different expectations today. The more data we can use to improve marketing, application fulfillment, underwriting and policy owner services and the more we can align life insurance products with their needs with how consumers want to do business, the more success we can expect.
LHP: How are motor vehicle records, a key component of the LexisNexis tool, predictive of mortality experience?
Wallace (pictured below): Motor vehicle records or MVRs, have “protective value,” that is, they provide insights into an individual’s lifestyle risk. By examining the number and severity of moving violations on an applicant’s MVR, life insurance underwriters can accurately assess the mortality risk associated with a policy applicant.
Related: Innovative thinking vs. innovation: key differences uncovered
If you have multiple points in your MVR for driving under the influence, then you’re living a risky lifestyle. You’re also more likely than not to have an underlying health issue that could evolve into a life-threatening condition, such as liver cancer.
So motor vehicle records let life insurers price policies more accurately. We put these MVRs and other data sources into our Life Risk Classifier predictive modeling software for determining a policy applicant’s relative mortality.
This model is key to doing streamlined or accelerated underwriting online. After a prospect submits an application, an insurer can underwrite and bind the policy — generally within 15 minutes. We have carrier clients doing this today.
Related: Eye on genomics: 9 questions for Dr. Craig Venter
LHP: Can you elaborate on the application process? How many steps are involved?
Wallace: There are three. The first is identity verification: checking the accuracy of information the prospect provides — name, address, date of birth and so on. In the next step, we pre-fill fields in the online app based on the applicant’s data.
Thereafter, our predictive model, a proprietary software algorithm compliant with the Fair Credit Reporting Act, assigns a score to the individual’s “relative mortality.” This value tells us whether he or she represents an above- or below-average risk of dying by a certain age compared to the general population.
The insurer then coverts this score into a risk class — super-preferred, preferred, standard, etc. — all in real time and without time-consuming medical underwriting. You can dispense with blood samples, attending physician statements other health records about the applicant.
Related: 4 ways to improve your digital presence
The 2012 LexisNexis-RGA study evolved into a three-year conversation with carriers to convince them the public records data is real and actionable. (Photo: iStock)
LHP: Let’s talk now about clients. Which life insurers are using the LexisNexis solution to speed underwriting? Or is this proprietary information?
Wallace: Most of our partnering carriers don’t want to be named. One I can disclose is the Savings Bank Life Insurance Co. of Massachusetts. SBLI is using our data to underwrite a block of new policies.
Related: Why the insurance industry needs digital marketing
LHP: Why not the whole book of business? Is there skepticism as to the accuracy of the data?
Wallace: Let’s call it due diligence. SBLI is looking to validate the data by insuring that future claims experience aligns with the predictive model. In a 2012 benchmarking study of more than 7.4 million motor vehicle records, LexisNexis and RGA Reinsurance Co. found that MVRs can reliably predict an individual’s “all-cause mortality,” or the death rate from any cause.
For example, people with major violations, such as alcohol-related infractions and excess speeding, have all-cause mortality that is 70 percent higher than individuals who don’t. The presence of 6 or more driving violations on an MVR elevates all-cause mortality by 80 percent.
Individuals with a higher number of violations represent the worst risks. For women, the all-cause mortality rate is 100 percent greater among those with these violations than for those who don’t. In short, our predictive model held its own against conventional underwriting. The 2012 study evolved into three-year conversation with carriers to convince them the data is real and actionable.
Related: Is genetic testing good for the insurance industry?
LHP: But just how accurate? If you were to plot mortality experience on a chart, there would surely be some outliers — policy claims that don’t dovetail with the predictive model — yes?
Wallace: Correct. But we can identify many of these outliers by combining the model with a drug history check. If the policy applicant is taking prescription medications for certain health conditions that garner a low score — making life expectancy harder to forecast — then the carrier may opt for traditional underwriting.
LHP: I imagine also the insurer may prefer medical underwriting for particularly large policies. What face amount ranges are insurers underwriting using the data?
