Automated Disability Insurance Screening System
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Solution Overview
Problem
Existing processes for screening disability insurance applicants are cumbersome and prone to errors, particularly for those with low risk factors, leading to delays and incorrect acceptance or rejection due to manual classification of occupations and incomplete underwriting.
Innovation Solution
A computer-implemented method and system for real-time screening of disability insurance applicants, which receives application data via an electronic network, calculates body mass index, checks prescription drug history, and classifies occupations to automatically accept or reject applicants and set premiums, reducing human intervention and enhancing the online customer experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual underwriting and occupation classification is used, then comprehensive assessment can be performed, but the process becomes cumbersome and time-consuming
Solution Approach 1:
The system performs preliminary automated screening actions by automatically classifying occupations using AI algorithms and pre-evaluating risk factors before manual underwriting is required. This preliminary action filters out low-risk applicants for instant acceptance and identifies high-risk cases for manual review, reducing overall screening time while maintaining assessment completeness.
Solution Approach 2:
The patent replaces manual mechanical classification processes with automated AI-based occupation classification systems. The machine learning models automatically analyze occupation data, assign risk categories, and make underwriting decisions, eliminating the need for manual review of standard applications and significantly reducing screening duration.
2Measurement precision
If manual occupation classification is performed, then detailed assessment can be made, but errors and delays occur
Solution Approach 1:
The system replaces manual occupation classification with automated AI-based classification algorithms that process occupation data more accurately and consistently. The machine learning models reduce human error in classification while maintaining detailed assessment capabilities, leading to both higher precision and reliability in occupation categorization.
Solution Approach 2:
The system incorporates feedback mechanisms where automated classification results are validated against known occupation patterns and risk profiles. The AI models continuously learn from correct classifications and adjust their algorithms, improving accuracy over time while reducing errors in occupation assessment.
3Reliability
If full underwriting is performed on all applicants, then thorough evaluation is achieved, but processing time increases
Solution Approach 1:
The underwriting process is segmented into different levels: automated preliminary screening for all applicants, instant acceptance for low-risk candidates, and selective manual underwriting only for high-risk cases. This segmentation maintains thorough evaluation for necessary cases while dramatically improving overall processing speed by avoiding unnecessary full underwriting of low-risk applicants.
Solution Approach 2:
The system performs preliminary automated risk assessment on all applications before routing to manual underwriting. This preliminary action uses AI algorithms to evaluate risk factors and automatically classify occupations, enabling the system to quickly identify applicants who qualify for instant acceptance and those requiring full manual review, thus optimizing processing speed without sacrificing evaluation thoroughness.
Data Source
AI summary
A system and method for automatically screening applicants for disability insurance in real-time, and if the results are favorable, automatically communicating acceptances and issuing policies via wireless communication. An applicant's BMI is calculated, and the applicant is rejected if the BMI is too high or low. The applicant's prescription drug history is obtained, and the applicant is rejected if the history shows use of a drug associated with a serious medical condition. The acceptance or rejection and a premium for the insurance product may be set based upon the real-time screening results and communicated to the applicant. Additionally, an occupation class assigned to the applicant may be confirmed, a motor vehicle report for the applicant may be obtained, and an insurance information report for the applicant may be obtained. If the applicant is rejected, they may be allowed to apply for the insurance product using an alternate non-real-time process.