Insurance Recommendation Engine with Explainable ML and Life Event Tracking
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Solution Overview
Problem
The current insurance recommendation process is complex and often fails to accurately reflect clients' changing needs, as it relies heavily on advisors to collect and update Personal Needs Assessment (PNA) forms, leading to inadequate insurance coverage due to lack of systematic life event tracking and personalized recommendations.
Innovation Solution
A computer-implemented method using trained policy recommendation models that apply PNA data, life stage milestones, demographic data, and historical purchase data to generate personalized insurance policy recommendations, accompanied by explainability models to provide human-understandable justifications, and a contact plan for updating coverage as life events occur.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional PNA forms are used to collect client information, then advisors can obtain basic insurance needs data, but the system fails to accurately track changing life events and provide timely updated recommendations
Solution Approach 1:
The system performs preliminary actions by proactively monitoring life events and triggering recommendation updates before clients realize their needs have changed. Life event tracking continuously monitors for changes in employment, family status, and financial conditions, automatically initiating the recommendation process without waiting for clients to complete new PNA forms.
Solution Approach 2:
The system implements continuous feedback loops where recommendation outcomes and client interactions are fed back into the model to refine future recommendations. The system tracks whether recommendations were accepted or rejected and uses this feedback to improve the accuracy of subsequent assessments and timing of updates.
2Loss of information
If manual PNA form completion is required, then detailed financial and personal data can be collected, but clients find the process cumbersome and fail to update information regularly
Solution Approach 1:
The system enables self-service by automatically collecting and updating client information through integration with external data sources such as employment databases, financial institutions, and family registry systems. Clients benefit from automated data collection without manual form completion, while the system maintains comprehensive data through these self-updating connections.
Solution Approach 2:
The system performs multiple functions through a unified platform: it collects initial PNA data, continuously monitors life events, tracks financial changes, generates recommendations, and updates assessments automatically. This multi-functional approach consolidates what would otherwise require multiple separate manual processes into a single integrated system.
3Adaptability or versatility
If multiple data sources are integrated for comprehensive analysis, then personalized recommendations can be generated, but system complexity increases significantly
Solution Approach 1:
The system segments the complex recommendation process into distinct functional modules: life event monitoring, data collection and fusion, recommendation generation, explainability analysis, and outcome tracking. Each module handles specific tasks independently, allowing the system to process multiple data sources comprehensively while maintaining manageable complexity through modular architecture.
Solution Approach 2:
The system introduces an intermediary explainability layer that translates complex model outputs into human-understandable explanations. This intermediary component bridges the gap between sophisticated multi-source data analysis and client comprehension, allowing the system to leverage complex algorithms without overwhelming users with their complexity.
Data Source
AI summary
An insurance recommendation engine receives customer data and using trained models recommends one or more insurance products that are suitable for the customer. The recommendation engine also provides an explanation as to why the particular products have been recommended. The recommendation models are incorporated into a system that can improves the customer's experience.