Digital transformation is a high priority for many insurance carriers, and initiatives are being carried out across the organizations we work with. Underwriting is a vital function that is ripe with opportunities for innovation. Every day, new sources of insurability data are being developed and vetted. In this post, we’ll examine a few examples, providing our perspectives on how these electronic sources of data can be leveraged in the underwriting process and where they are in the adoption lifecycle.
The prescription history database report, “Rx check” for short, has been in the U.S. market for quite some time now (10+ years). With the popularity of simplified issue and final expense products, which rely on speedy turnaround times and requirements, it’s hardly a surprise as to why this tool is so valuable. The report itself contains a listing of an applicant’s prescription medication ‘fills’ across a variable period (often up to 5 years in the past). The cost is low, and the protective value is high. So high that many fully underwritten products – which have the luxury of waiting for medical requirements and attending physician statements (APS) – also incorporate the Rx check into their routine requirements. Currently, this product is only successful at retrieving data on approximately 70-80% of applicants, and it is available only in the U.S as there are regulatory constraints to Rc checks being available in other countries.
MIB checks have been around for what seems to be since the dawn of risk selection in North America. Whether you’re dealing with traditional fully underwritten products, simplified issue products, or more novel accelerated underwriting paradigms, you can expect to see an MIB check. MIB is a North American database that collects adverse medical history, in a coded format, sourced from a person’s insurance application. The data collected is often from multiple sources, such as the application itself, laboratory reports completed for insurance purposes, or an APS (Attending Physician Statement) obtained during the process. This history is then reported to insurance carriers if the same individual were to reapply with them, within a specified time window. The fraud protection and the risk protective value of the MIB is well-established, so it’s not expected that our reliance on this check will change in the foreseeable future. What is expected to evolve, however, are the services that MIB provides. The database of codes is routinely updated, with new indices and alerts becoming available to draw from. Already, we have the insurance activity index and follow-up reporting service (i.e., Plan F), but watch for new services on the horizon including its Jumbo Service in collaboration with TAI.
Numerous studies have shown links between debt and mortality rates, and some carriers have adopted credit data to serve in some capacity or another. Credit data has historically been used alongside inspection reports as a routine and/or cause requirement in the large case business, in order to rule out risky financial behavior. The potential predictive power of this tool makes it an attractive data source to be employed in accelerated underwriting and any underwriting paradigm that involves predictive analytics. Check out TransUnion TrueRisk® Life, a credit-based insurance score, validated by RGA, that models credit-based behavior and is predictive of mortality and lapse risk.
A broad array of wearable devices have been on the market for many years, from those that track physical activity to more advanced tech to monitor vitals such as blood pressure and blood glucose levels. The amount of data these devices can provide healthcare providers and insurers is almost dizzying. The key to unlocking the promise of wearable devices will be to determine which use-cases have the greatest impact and are most likely to engage policyholders. To answer these questions, RGAX is working with many insurtech innovators in this space. One prime example among many is REACHhealth, an app in the U.K. that helps users implement healthy lifestyle changes to combat five of the most common diseases.
Electronic Health Records
Attending physician statements (APS) are still heavily relied on for risk assessments of older individuals and policies with higher face values. Electronic health records (EHR) present an opportunity to offset the need for the traditional APS, at a reduced cost in terms of money and time; however, they are still some distance away from seeing significant action in risk assessment. The crux of the EHR is to provide an applicant’s medical history in a coded format. Whereas a quality APS is often at least 20 pages (but can be hundreds, if not thousands), an EHR is a minute fraction of that length. Most elements of the APS, such as medical diagnoses, medications, and laboratory data, can be translated into coded form in the EHR; however, underwriting does lose out on clinical notes, which are extremely valuable in assessing cases such as those involving mental health or substance abuse issues. Lack of older medical history and questionable consistency of coding amongst different doctors/facilities/regions present other challenges for EHRs finding their mark in the risk assessment process. There is a lot of attraction to solving these issues though, in that EHRs can function very well within automated underwriting processes, whereas APS’ do not – by virtue of the time it takes to obtain (and review) traditional medical records. Although they may not be a true substitute for the APS, we can expect EHRs to eventually make their way to some age/amount requirement charts, and potentially even form part of the triage process for Simplified Underwriting programs.
Facial recognition as a source of insurability data is on the cutting edge of underwriting trends. This approach makes use of the copious quantity of images individuals voluntarily post on public sites. In some geographies, access to CCTV footage may also have a role to play. For instance, an individual applies for a policy as a non-smoker, yet a scan of the internet and facial recognition algorithms shows them recently smoking at a local nightclub. The application is submitted for further review.
There are significant barriers to developing this software for use in an insurance risk assessment. Facial recognition software is not perfect, which raises the risk of false identification. However, we can expect that issue to be resolved as technology advances. Of more significance in some geographies are the concerns over consumer privacy. We don’t expect this approach to take root in geographies like North America that place a high value on individual privacy. However, at least one insurer in China is employing similar facial recognition technology during the applicant interview process. In the setting of a recorded camera interview, the software is designed to pick up on telltale cues that can identify when the applicant is knowingly lying or omitting material information.
Learn More About Underwriting Trends
These are just a few snapshots of current and potential electronic sources of information, below are some additional links that may help. If you’d like to discuss how RGAX can help you transform your underwriting process or foster innovation across your organization, reach out to us. We’d love to explore the opportunities with you.
- Emerging Underwriting Methodologies and their Impact on Mortality Experience
- A Score for All Seasons: Big Data and Credit Scores Bring Big Changes to Life Underwriting
- Credit scores, cardiovascular disease risk, and human capital
- Electronic Health Records – Are We Now In Prime Time?
- What Facial Recognition Experts Want You to Know Before You Use This Technology to Curb Serious Threats to Safety
- Facial recognition is the new polygraph test for insurers
- Risk Adviser: Digital Health Data Can Streamline the Underwriting Process