Machine Learning Damage Severity Prediction Using SHAP Explainer Values
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Solution Overview
Problem
Conventional insurance claims processing systems are time-consuming and inaccurate due to their reliance on single-variable approaches for determining item damage severity, which limits the analysis of multiple impacting factors and results in large amounts of data that are difficult to interpret effectively.
Innovation Solution
A provider computing system utilizing machine learning models, such as Shapley Additive Explanations (SHAP), to analyze claims data and predict item damage severity based on multiple variables, providing a user-friendly interface that filters and sorts severity data to highlight the most relevant information, and continuously updates predictions using current data for improved accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional single-variable approaches are used to analyze claims data, then the analysis process is simpler, but the accuracy of item damage severity determination deteriorates
Solution Approach 1:
The patent segments the complex multi-variable analysis into distinct components: the machine learning model processes multiple claim variables, while the explainer values break down the contribution of each variable. This segmentation allows accurate multi-factor analysis while managing complexity through modular processing.
Solution Approach 2:
The patent introduces explainer values as an intermediary between the complex machine learning model and the user interface. These explainer values translate the complex multi-variable analysis into interpretable metrics that show the impact of each claim variable, bridging the gap between model complexity and user understanding.
2Measurement precision
If multiple variables are analyzed to determine item damage severity, then the accuracy of predictions improves, but the time required for analysis increases
Solution Approach 1:
The patent performs preliminary processing by pre-calculating explainer values for each claim variable during the model training and data preparation phases. This preliminary action ensures that when claims are analyzed, the multi-variable impact is already quantified, reducing the time required for actual severity determination while maintaining high accuracy.
Solution Approach 2:
The patent replaces traditional mechanical data analysis methods with machine learning models that can process multiple variables simultaneously and efficiently. The automated calculation of explainer values substitutes manual or sequential analysis, dramatically reducing processing time while analyzing multiple impacting factors.
3Loss of information
If large amounts of claims data are processed, then the comprehensiveness of analysis improves, but the difficulty of data interpretation increases
Solution Approach 1:
The explainer values serve as an intermediary that translates comprehensive multi-variable claims data into interpretable metrics. Each explainer value quantifies the impact of a specific claim variable on damage severity, making the comprehensive analysis results easily understandable and actionable for users.
Solution Approach 2:
The patent uses visual indicators (such as color coding in the user interface) to represent the impact of different claim variables. This visual representation transforms complex numerical data into easily interpretable visual information, where colors indicate the direction and magnitude of variable impacts on damage severity.
4Productivity
If traditional claims analysis methods are used, then the system is easier to implement, but the productivity of claims processing deteriorates
Solution Approach 1:
The patent replaces traditional sequential claims analysis with automated machine learning models that process multiple claim variables simultaneously. The system automatically calculates explainer values and determines damage severity, dramatically increasing processing speed while the modular architecture manages system complexity.
Solution Approach 2:
The machine learning model performs self-service by automatically processing claims data, calculating explainer values, and generating severity determinations without manual intervention. This automation increases productivity while the system manages its own complexity through integrated processing.
Data Source
AI summary
Systems and methods for explaining year over year changes in claim variables are provided. A computing system is configured to receive claim datasets corresponding to one or more time periods, and parse a plurality of claim variables from each claim dataset. The computing system is also configured to cause one or more machine learning models to parse a plurality of explainer values from each of the claim datasets, determine an average explainer value for each of the plurality of explainer values, and determine percent impact values that each correspond to a particular claim variable. The computing system is also configured to generate and render a user interface having one or more selectable features that each represent one of the percent impact values. The computing system is also configured to filter and sort the one or more selectable features based on the percent impact values.