Explainable Underwriting Estimator Model with Random Forest
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Solution Overview
Problem
Conventional systems for providing quick quotes in underwriting processes, especially in life insurance, are labor-intensive and lack transparency, making it difficult to explain decision-making processes to users, and existing machine learning solutions fail to provide adequate interpretability of risk assessments.
Innovation Solution
A predictive machine learning model that receives limited user inputs, generates immediate underwriting estimates, and provides qualitative and quantitative explanations using random forest models with smoker and non-smoker components, incorporating additive feature attribution and natural language explanations to explain underwriting decisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If machine learning algorithms are used to provide quick quotes, then processing speed and precision are improved, but transparency and explainability of decisions deteriorate
Solution Approach 1:
The patent introduces an explanation generation module as an intermediary component that sits between the machine learning model and the user. This module receives model predictions and generates human-readable explanations, bridging the gap between complex algorithmic decisions and user understanding. The explanation generator acts as a mediator that translates black-box model outputs into transparent, actionable insights without compromising the model's processing speed or accuracy.
2Productivity
If machine learning algorithms are used to provide quick quotes, then processing speed is improved, but trust in the process deteriorates
Solution Approach 1:
The patent implements a feedback mechanism where the system provides explanations to users about the factors influencing their underwriting decisions. This feedback loop allows users to understand the rationale behind algorithmic decisions, building trust in the process. The explanation generator delivers personalized feedback that shows users which risk factors were considered and how they impacted the outcome, making the automated process more transparent and credible.
3Productivity
If conventional software algorithms are used, then processing speed is improved, but detail and precision of risk assessment deteriorate
Solution Approach 1:
The patent segments the underwriting process into distinct components: a machine learning model for rapid risk prediction and an explanation generation module for detailed analysis. The ML model handles the high-speed classification into risk categories, while the explanation generator provides granular details about specific risk factors. This segmentation allows each component to optimize for its specific function, maintaining both speed and precision.
Data Source
AI summary
An underwriting estimator predictive machine learning model receives as inputs a limited number of details about an applicant, and outputs an immediate underwriting estimate of risk class. A preliminary pre-screening review redirects applicants with one or more screening impairments to a human-in-the-loop quick quote process. Model inputs include estimator inputs data that are pre-selected from the dataset of impairments data after excluding the screening impairments from the dataset of impairments. The underwriting estimator model may incorporate alternative pathways that output individualized underwriting estimates for some applicants and cohort-level marginal distributions for other applicants. Model outputs also include explanation files providing interpretability of underwriting estimates. The explanation files may include additive feature attribution data and rule based natural language explanations. The underwriting estimator predictive model may apply random forest models with smoker and non-smoker components to model inputs.