For a long time, life-term underwriting has grappled with a dwindling baby boomer generation and growing millennial customer base. Amidst these circumstances, accelerated underwriting has experienced phenomenal growth thanks to its ability to skip lengthy medical examinations. Machine learning technologies have been driving these shifts by simplifying application procedures and improving the speed of underwriting.
Some insurers have been offering accelerated underwriting for years, and new entrants have been driven to the market by social dynamics like the Covid-19 pandemic, busy work lives, and limited personal contact. Indeed, a recent survey released by Milliman showed that 26 out of 34 surveyed participants used accelerated underwriting for term life policies. (1)
Why Accelerated Underwriting?
Many industry experts agree that the current overreliance on the baby boomer generation is slowly fading away. Many insurers will soon be grappling with a largely uninterested market full of price-conscious younger generations. In a study targeting respondents aged 25-54 years, researchers from Deloitte discovered that over 70% had a higher motivation to buy a life insurance policy if application processes were faster, more simplified, and connected with their priority life events. (2)
Generally, consumers in the younger demographics want easy access to information and buy their insurance policies using digital channels. (3) Because this generation is practically built for the digital world, insurers need to adopt fast-to-market strategies and extend their reach in the digital marketplace to attract this customer segment.
The decision to buy a life insurance policy typically rests on an individual's awareness, financial priorities, and the complexity of the application process. While traditional methods take between 2-12 weeks, accelerated underwriting allows the insurer to bring this down to 24-48 hours, and in some cases, instant approval. Buyers can complete the entire process over the phone or using the internet. Insurers can do their risk scoring using real-time data from credit bureaus, the medical information bureau, pharmaceutical records, and the DMV.
Machine learning algorithms can improve the prediction models, bringing together crucial data from other sources like social media and consumer apps to score an applicant. (4) The increased scoring accuracy and reduced administrative overheads allow the insurers to provide low-priced premiums.
Who Is a Good Candidate?
With accelerated underwriting, all applicants subject to the age range are considered eligible at the time of application once they allow the insurer to look into their medical history and credit information. However, not all will qualify. Those who don't gain approval can be rerouted to traditional underwriting should they choose to continue with the policy, and they might still enjoy the same premiums.
The ideal candidate should fall between the insurer's specified age range, usually falling between 18-60 years, going by most underwriters. They should not have major pre-existing medical conditions. They need to prove that none of their biological parents or siblings have suffered from cancer or heart disease while aged below 60.
Insurers also look at other factors that aren't related to health, like criminal records, traffic violations, and credit history. The push to check an individual's financial standing builds on proven studies, including a recent one by Gladstone and Whillans, showing that people with good credit scores were generally happier and tended to live healthier lives. (5)
Some insurers restrict the policies to specified BMI ratios, and they might also look into an individual's lifestyle for high-risk hobbies like skydiving. An underwriter may limit the face amounts according to different age categories. It's usually high for lower age brackets and low for higher brackets. For example, the face value may be set at $800,000 for ages 18-40 and $500,00 for 41-60. (6)
Challenges of Using AI in Accelerated Underwriting
The incorporation of machine learning technologies into accelerated underwriting brought concerns about fairness and transparency in the scoring process. When used ineffectively, it might introduce hints of prejudice when behavioral data creates biases that might not correlate with the facts on the ground. For instance, while a high-income individual is likely to have better medical care, their income increases their propensity for dangerous hobbies and even illegal drug use.
The National Association of Insurance Commissioners (NAIC) devised frameworks to control potentially discriminatory underwriting (7) practices from big data analytics and artificial intelligence, which include:
- Ensuring the accuracy, reliability, and transparency of data
- Enforce actuarial integrity in predictive modeling by validating the claimed causal relationships against physical evidence
Transparency demands that the insurer provides the reason for poor scoring or rejection and shares information when required by regulators or policy bodies.
Machine learning models in term life insurance are not new technologies, and they have kept improving over different releases. When the underwriter intervenes to correct any deviations, the self-learning systems pick up these mistakes and improve themselves to deliver more accurate results in the future.
The Bottom Line
AI technologies are leading the way into the future of underwriting, and there's no doubt that accelerated life insurance is one of its biggest beneficiaries. It has shifted the insurance application process from invasive and dragged-out medical procedures to a product that can be quickly purchased by clicking a mobile app. With a highly reduced waiting period, insurers can start collecting premiums almost immediately while pricing them at very competitive rates.
Even when they're in perfect health, some customers genuinely detest medical examinations, fearing rejection should an issue be found during an exam. And among the no-exam life policies available, accelerated underwriting presents one of the most affordable solutions. The insurer achieves a larger risk pool in shorter spans because of a simplified onboarding process. And customers enjoy quicker approvals as long as they can prove to be in excellent health coupled with a good credit history and clean driving record.
Accelerated Underwriting with Pilotbird's Insurance Analytics
Pilotbird's predictive analytics tool uses innovative AI technologies to deliver more precise risk scoring and reduce claims fraud. Our lifestyle analytics solution leverages social data points to give insurers more profound insights into customer risk profiles, hence speeding up the underwriting process.
Contact us for a demo and discover how Pilotbird's predictive analytics solutions can transform your insurance business.
- "Milliman: 28 Of 34 Insurers Endorse Accelerated Underwriting For ...." 4 Jan. 2022, https://insurancenewsnet.com/oarticle/milliman-survey-reveals-28-out-of-34-companies-use-or-plan-to-use-accelerated-underwriting-in-term-life-insurance. Accessed 4 Feb. 2022.
- "Life insurance consumer purchase behavior - Deloitte." https://www2.deloitte.com/content/dam/Deloitte/us/Documents/strategy/us-cons-life-insurance-consumer-study.pdf. Accessed 4 Feb. 2022.
- "Millennials, What Will It Take for You to Buy Life Insurance?." 30 Jul. 2021, https://www.theatlantic.com/technology/archive/2021/07/great-life-insurance-rebrand/619603/. Accessed 2 Mar. 2022.
- "Life Insurance Risk Prediction using Machine Learning Algorithms." 10 Jan. 2021, https://towardsdatascience.com/life-insurance-risk-prediction-using-machine-learning-algorithms-part-i-data-pre-processing-and-6ca17509c1ef. Accessed 2 Mar. 2022.
- "Credit Scores Predict Subjective Well-Being - Working Paper." https://www.hbs.edu/faculty/Pages/item.aspx?num=54673. Accessed 4 Feb. 2022.
- "Adult BMI Calculator | Healthy Weight, Nutrition, and Physical Activity." 21 Jan. 2022, https://www.cdc.gov/healthyweight/assessing/bmi/adult_bmi/english_bmi_calculator/bmi_calculator.html.
- "Special (EX) Committee on Race and Insurance - NAIC." https://content.naic.org/cmte_ex_race_and_insurance.htm. Accessed 4 Feb. 2022.
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