Insurance Plan Recommendation Model With Actuarial Feedback Learning
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Solution Overview
Problem
Recommending medical plans for employees is a complex, manual, and potentially error-prone process, as employees may not fully understand the plans provided by their employer, leading to inadequate selections.
Innovation Solution
A data processing system utilizing a plan recommendation model trained with actuarial labeled data to generate insurance plan recommendations that consider various user factors, including income, healthcare utilization, preferred providers, and pre-existing conditions, mimicking actuary recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual plan recommendation process is used, then employer can provide plan options, but the process is complex and error-prone with inadequate user understanding
Solution Approach 1:
The patent replaces the manual mechanical recommendation process with an automated machine learning system. The ML model automatically processes user data and plan information to generate recommendations, eliminating manual analysis errors and complexity while improving recommendation accuracy and consistency.
Solution Approach 2:
The system enables self-service plan recommendations by automatically analyzing user needs, preferences, and plan options without requiring employer intervention. The ML model independently processes data and generates personalized recommendations, allowing employees to receive accurate advice without manual assistance.
2Productivity
If employees search for plans manually, then they can review options, but extensive searching is required and time-consuming
Solution Approach 1:
The system performs preliminary analysis by automatically evaluating user needs, comparing plan options, and generating recommendations before the employee needs to make a decision. This pre-processing eliminates the need for extensive manual searching and provides ready-to-review personalized plan options.
Solution Approach 2:
The system provides feedback-driven recommendations by continuously analyzing user data, plan options, and selection patterns to refine future recommendations. This feedback loop ensures increasingly accurate recommendations reduce the iterative search process and time spent reviewing plans.
3Extent of automation
If automated recommendation system is implemented, then plan recommendations can be generated automatically, but model accuracy needs continuous improvement through training data
Solution Approach 1:
The system implements feedback mechanisms where actual plan selections and user responses are fed back into the training data. This continuous feedback loop enables the ML model to learn from real-world outcomes and progressively improve recommendation accuracy, balancing automation with ongoing reliability enhancement.
Solution Approach 2:
The system performs preliminary model training and validation before deployment, using historical data to establish accurate recommendation baselines. This preliminary preparation ensures the automated system starts with high accuracy and can adapt to changing conditions without requiring continuous manual intervention.
Data Source
AI summary
A data processing system implements a system for training and fine-tuning a plan recommendation model that recommends one or more insurance plans for a user based the cost of the insurance plans and the needs of the user. The plan recommendation model is trained and/or fine-tuned using training data that has been labeled by a human actuary to improve the accuracy of the model. The plan recommendation model is also fine tuned based on feedback received on recommendations made by the model to further improve the performance of the model.