Data Model Management System for Insurance Ratemaking
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Solution Overview
Problem
Existing technologies face challenges in efficiently building and combining data models, particularly for Generalized Linear Models (GLMs) used in property and casualty insurance ratemaking applications.
Innovation Solution
A computer-implemented method and system that enables users to input assumptions and parameters for building data models, automatically generating model build partitions, and facilitating the combination and selection of 'champion' data models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple data models are built and combined manually, then model accuracy can be improved, but the complexity of the process increases significantly
Solution Approach 1:
The patent combines multiple data models (benchmark model and challenger models) into a unified modeling output through automated combination techniques. This merging approach allows the system to leverage the strengths of multiple models while reducing the manual complexity of managing them separately, thereby improving model accuracy without proportionally increasing process complexity.
Solution Approach 2:
The system creates a multi-functional platform that handles model building, combination, selection, and management through a single integrated interface. This universal approach allows users to perform multiple modeling tasks through one system, reducing the need for separate tools and processes while maintaining high model accuracy through comprehensive analysis capabilities.
2Productivity
If automated model building is implemented, then productivity increases, but control over modeling parameters decreases
Solution Approach 1:
The system dynamically adjusts the balance between automation and user control by allowing users to specify modeling parameters and assumptions while the system automatically handles the complex process of model building and combination. This dynamic approach enables users to maintain control over critical parameters while benefiting from automated efficiency in executing the modeling process.
Solution Approach 2:
The system acts as an intermediary between user intent and model output by providing a structured interface where users can input parameters and assumptions, and the system translates these into automated model building operations. This intermediary layer preserves user control while enabling automated productivity gains through systematic parameter management.
3Measurement precision
If comprehensive model evaluation is performed, then model selection accuracy improves, but the time required for analysis increases
Solution Approach 1:
The system performs preliminary actions by automatically generating both benchmark and challenger models with pre-defined evaluation criteria before final model selection. This preliminary modeling and pre-evaluation approach allows for comprehensive model assessment to be conducted systematically, improving selection accuracy while reducing the time required for final decision-making by having analyses prepared in advance.
Data Source
AI summary
Techniques for building and managing data models are provided. According to certain aspects, systems and methods may enable a user to input parameters associated with building one or more data models, including parameters associated with sampling, binning, and other factors. The systems and methods may automatically generate program code that corresponds to the inputted parameters and display the program code for review by the user. The systems and methods may build the data models and generate charts and plots depicting aspects of the data models. Additionally, the systems and methods may combine data models and select champion data models.