09/08/2022
The process of obtaining life insurance can feel like purchasing a home and having a comprehensive annual health exam simultaneously. It’s an arduous, multi-faceted health and financial screening process that can cause customers to throw up their hands and question if obtaining life insurance is even worth it. After a customer decides to obtain life insurance, the underwriting process can begin. During this process, underwriters seek to assign a risk classification based on a predicted life expectancy through the evaluation of both medical and non-medical information. The underwriter sends questionnaires, reviews medical exams, and uses complex mathematical algorithms and classification models to determine what the insurance company will cover and at what cost.
The underwriter is not a legally trained physician and underwriting is not clinical medicine, but there are parallels at work here. Physicians assess the health in the present and continuously monitor the physical condition for the life of the patient. Life insurance companies perform a point-in-time assessment of health to predict a customer’s probable health in the future. It’s no wonder why this process must be thorough, but leaves customers stressed and fatigued.
However, thanks to the proliferation of new insurance intelligence – like artificial intelligence (AI) – insurance underwriting is changing for the better. AI is already improving how underwriters predict risk and calculate cost, making the process more efficient and cost-saving. Let’s explore how AI impacts underwriting through the lens of two individuals, John and Joe.
Traditional Underwriting for Life Insurance
John is applying for life insurance and goes with a tried-and-true insurance company that uses a traditional underwriting process. He selects a policy from the available offerings and fills out the initial application and questionnaire. However, to complete the process, a few significant steps remain:
1. The Medical Exam: John’s medical exam reveals a pre-existing condition, which is flagged in his results and requires a statement from his physician, adding weeks to the underwriting process.
2. Records Review: An underwriter reviews John’s records as a part of the risk management process, including all medical records, driving history, and all prescribed medications. This adds resource hours for the insurance company and weeks to the process. John reaches out to customer support for an update on the underwriting status, but they aren’t able to provide any real-time information.
3. Actuarial Tables and the Final Rating: Now that John’s information is collected and verified, the underwriter determines John’s risk to the company using actuarial tables and the data collected. The underwriter assigns a final rating and premium.
In total, the underwriting process has taken nearly two months and countless resource hours. John receives a much higher premium than expected based on specific factors the underwriter felt merited a higher rate.
Insurance Intelligence Is Changing the Future of Underwriting
Similarly, Joe is applying for life insurance and selects an insurtech disruptor based on a social media ad he read. There are notable differences in Joe’s insurance underwriting experience, thanks to the inclusion of AI.
An Improved, Customized, Consumer Experience: The inclusion of AI allows Joe to customize his potential policy choices and identify additional coverage options based on information automatically sourced from external data sources that leverage his profile information. As a result, his policy is customized to his particular needs.
While Joe is waiting for his underwriting process to complete, he has access to real-time status updates. AI tools allow the underwriter to address any discrepancies on Joe’s application quickly and pass that status along in real time. AI even enables customer experience automation through the use of chatbots to serve both Joe and the underwriter as issues arise for quick and convenient communication without a face-to-face or video conference.
Intelligent Data Curation: The underwriter servicing Joe uses has an AI-enabled platform that automatically obtains insights from data from various structured and unstructured data sources through advanced organization and integration techniques. This platform, powered by a multi-layered convolutional neural network trained to recognize patterns, classify data, and predict future results, establishes a risk profile, and determines insurance classification in minutes.
Deep learning tools improve the accuracy of traditional actuarial models, providing Joe with an optimal rate and coverage suited specifically for him. For example, Joe configured his wearable device to upload his daily workout results and vitals to his healthcare provider’s patient portal. Using the Fast Healthcare Interoperability Resources (FHIR) standard framework, the platform is able to access this wellness data in Joe’s medical records–those early morning jogs are really paying off now.
Improved Efficiency and Cost Savings: Unlike John, Joe is able to complete the entire process in a fraction of the time. AI enables the insurance company to collect and collate structured and unstructured data automatically, reducing resource hours. Additional time is saved by automating many aspects of the risk management process and flagging specific tasks for manual intervention only when necessary. This not only provides Joe with an active policy faster, but also saves the insurance company cost in time and resources.
More Options, Increased Revenue: AI was able to help the underwriter determine that Joe qualified for this more affordable life insurance policy based on a risk assessment algorithm that performs more sophisticated permutations over time. In fact, even though John had a preexisting condition, he would have also qualified for this product because AI could have taken the analysis of his condition to a deeper level. By providing more options and more precisely assessing risk, insurers can give countless individuals access to affordable types of life insurance and related products that they didn’t have access to before. This also means an increase in revenue by making more products available to a broader customer base.
Removing Bias and Maintaining Information Privacy is Paramount
AI provides a unique opportunity to move closer to true objectivity in the risk management process. However, the deep learning algorithms that are a part of its makeup, are ultimately dependent upon the humans that develop them. The National Institute of Standard and Technology (NIST) recommends widening the scope of bias detection to include machine learning processes, data used to train AI algorithms, and societal factors that can influence how the technology is developed. Insurance companies will need to remain vigilant to eradicate bias as the proliferation of AI in the underwriting space continues to grow.
The next generation of smart devices and highly connected solutions will generate an estimated 14.4 billion active connections in 2022. As the amount of available data continues to grow, developers will need to implement safeguards in how personal healthcare information (PHI) is collected, stored, and made secure. This may create an additional burden on HIPAA compliance for insurance companies as these AI tools expand.
Are you ready for the future of health insurance underwriting? If you’re looking to reduce costs, improve your underwriting efficiency, and streamline your customer experience, Sendero can help. Fill out the form below to connect with one of our consultants.
