Predictive Model for Expedited Life Insurance Underwriting
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Solution Overview
Problem
Current life insurance underwriting processes are inefficient and prone to human bias, leading to increased costs and cycle times, as they rely heavily on traditional methods that require extensive data collection and lab work, which can be time-consuming and error-prone.
Innovation Solution
A predictive modeling system that employs machine learning algorithms to analyze consumer data from electronic applications and telephonic interviews, generating scores to determine eligibility for expedited underwriting, thereby reducing the need for lab work and eliminating human bias by using a combination of data from various sources, including medical and public records.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional underwriting methods are used with extensive data collection and lab work, then underwriting accuracy is maintained, but underwriting cycle time increases and costs increase
Solution Approach 1:
The system performs preliminary data collection and processing through electronic applications and telephonic interviews before the formal underwriting process. This preliminary action gathers necessary information in advance, allowing the traditional underwriting process to proceed more quickly without sacrificing accuracy.
Solution Approach 2:
The patent replaces manual, mechanical underwriting processes with an automated computer-based system that uses machine learning algorithms and predictive models. This substitution eliminates human bias and accelerates the underwriting cycle while maintaining consistent accuracy standards through automated decision-making rules.
2Reliability
If traditional underwriting methods with extensive manual processes are used, then thorough assessment is achieved, but underwriting costs increase
Solution Approach 1:
The system enables applicants to complete portions of the underwriting process themselves through electronic applications, where they provide personal information, medical history, and other necessary data. This self-service approach reduces the need for manual data collection by underwriters, lowering costs while maintaining thorough assessment through automated validation.
Solution Approach 2:
Manual underwriting processes are replaced with automated computer-based systems that use machine learning models and predictive algorithms to assess risk. This substitution reduces labor costs and operational expenses while maintaining or improving assessment thoroughness through consistent application of underwriting criteria.
3Adaptability or versatility
If human underwriters conduct traditional reviews, then nuanced decision-making is possible, but human bias is introduced
Solution Approach 1:
The patent replaces human underwriters with an automated computer-based system that uses machine learning algorithms and predictive models. This substitution eliminates human bias in decision-making while maintaining adaptability through programmable business rules and flexible underwriting criteria that can be adjusted without human intervention.
Solution Approach 2:
The system incorporates feedback mechanisms where the outcomes of automated underwriting decisions are continuously analyzed and used to refine and improve the machine learning models. This feedback loop ensures the system adapts to new patterns and maintains decision-making flexibility while consistently eliminating human bias through automated processing.
Data Source
AI summary
A predictive insurance underwriting system. An electronic life insurance policy application processing apparatus accepts data describing a consumer. An underwriting desktop processing apparatus receives the data. An underwriting rules processing apparatus determines, based on the data describing the consumer, whether the consumer is eligible for expedited underwriting of a life insurance policy covering the consumer. If it is determined that the consumer is eligible for expedited underwriting, a tele-interview processing apparatus collects first additional data relating to the consumer during a telephonic interview and, in response to a request from a processing unit, one or more third party databases transmit second additional data relating to the consumer. The processing unit is configured to: process the data collected via the electronic life insurance policy application, and the first additional data, using a predictive model that employs machine learning algorithms and generate a first score; process the second additional data to generate a mortality risk score; if the first score at least meets a first threshold score, and the mortality risk score meets at least a second threshold score, and at least certain business rules are met, generate data describing terms of a life insurance policy. A policy issuance system offers the terms of the life insurance policy to the consumer.