Last Updated: 4/7/2026
Underwriting in insurance is the process an insurer uses to examine risks and determine the appropriate rate for coverage provided. Life underwriters examine all the data gathered in the application process to classify and group the risk to charge accurate premiums. Historically, this application data included a physical exam with doctor’s notes, blood work, and urine analysis. As consumers increasingly expect on-demand digital services, some insurers are using accelerated underwriting (AU) techniques to forgo a physical exam and supplement the application process with data from external sources along with new analytics and modeling techniques. This can effectively reduce the length of the application process from several weeks to just a few hours.
Traditional life insurance underwriting requires the collection of extensive medical information including a physical exam and fluids testing (blood, urine, and saliva). The timeline from the start of the application process to the issuance of a policy can be up to a few months. Some insurers also use simplified underwriting techniques which allow an applicant to forgo the medical exam and collection of fluids in exchange for generally higher premiums.
Insurers have found the traditional underwriting process to be an obstacle to purchase for consumers shopping for life insurance, in large part due to the physical exam and long application time. In response, the industry is implementing AU techniques that eliminate the physical exam and incorporate data from external sources, like prescription drug history and motor vehicle records, to process applications in a matter of hours. In addition to reducing the application time, the use of these techniques offer benefits to both insurers and consumers, including increasing sales, reducing administrative costs, and minimizing fraud.
Besides the information provided on the application, the data for AU techniques come from many sources including credit reports, motor vehicle records, and the Medical Information Bureau. In addition, insurers increasingly rely on external data sources, predictive models and algorithmic or machine learning techniques to evaluate risk and determine eligibility for accelerated pathways.
NAIC regulatory guidance highlights that these models must rely on sound actuarial principles, transparent data inputs, and be evaluated for unfair discrimination. However, AU alone does not always lead to issuance of a policy. For some applicants, the available data will be insufficient to adequately evaluate their risk profile, so they will still need to complete the traditional underwriting process, including the physical exam. While AU offers operational and consumer benefits, the increased use of external data, predictive models, and AI introduces regulatory considerations related to data quality, governance, transparency, and potential unfair discrimination. These considerations are central to NAIC guidance and oversight efforts.
