Machine Learning Benefits Utilization Engine
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Solution Overview
Problem
Insured individuals often fail to utilize their insurance benefits due to unawareness or forgetfulness about the coverage provided by their policies, leading to underutilization of available services and potential savings.
Innovation Solution
A machine-learning driven data processing system that analyzes demographic and insurance information to predict available benefits and generate personalized recommendations for users, utilizing a benefits utilization engine to provide real-time insights and reminders for utilizing insurance benefits.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If insured persons are provided with comprehensive insurance and benefits information, then their awareness of available coverage improves, but their ability to understand and utilize the minutiae of coverage deteriorates due to complexity
Solution Approach 1:
The system extracts specific, actionable benefits from comprehensive insurance policies by using machine learning models to identify underutilized benefits and generate targeted recommendations. Instead of presenting all coverage information, the system extracts only the most relevant and actionable items for each user based on their claims history and demographic data.
Solution Approach 2:
The system transforms complex insurance coverage information into simplified, personalized recommendations by changing the parameters of information delivery. The machine learning models analyze multiple data points (claims history, demographics, policy terms) and transform this complex data into simple, actionable insights presented through user interfaces in various formats (reports, notifications, reminders).
2Loss of information
If detailed insurance policy information is made available to users, then completeness of coverage information improves, but ease of understanding and utilization deteriorates
Solution Approach 1:
The system introduces machine learning models and recommendation engines as intermediaries between the insurance policies and users. These intermediaries process complex policy information and translate it into personalized, easy-to-understand recommendations. The system acts as a mediator that bridges the gap between comprehensive but complex policy terms and user-friendly guidance.
Solution Approach 2:
The system enables users to automatically receive personalized benefit recommendations and reminders without needing to manually review complex policy documents. The machine learning models continuously analyze user data and automatically generate tailored guidance, allowing users to benefit from comprehensive coverage information without the effort of understanding it themselves.
Data Source
AI summary
A data processing system for machine-learning driven data analysis and reminders implements obtaining an electronic copy of demographic information and an electronic copy of insurance and benefits information associated with a user; providing the demographic information and the insurance and benefits information to a first machine learning model; analyzing the demographic information and the insurance and benefits information with the first machine learning model to output a first benefits utilization prediction that the one or more benefits are available to the user; providing the first benefits utilization prediction as an input to a recommendation engine; generating, using the recommendation engine, a benefits usage summary recommendation report that presents the information regarding the one or more benefits available to the user based on the first benefits utilization prediction; and causing a user interface of a display of a computing device to present the benefits usage summary recommendation report.