Dynamic AI Model Retraining for Premium Financing Scoring
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Solution Overview
Problem
Conventional data intake software solutions use static algorithms for calculating results, which are inefficient and require manual revision by users with programming knowledge, making them time-consuming and error-prone.
Innovation Solution
A system and method using an analytic server to train an AI model based on historical customer data, dynamically adjusting algorithms to calculate scores, and automatically updating the model and graphical user interface without user intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional software solutions use static algorithms to calculate results, then the software is simple to implement, but the results become outdated and require manual revision
Solution Approach 1:
The patent implements dynamic algorithms that automatically adjust and update based on new data and user feedback. The system transitions from static predetermined algorithms to dynamic machine learning models that continuously learn and adapt, resolving the contradiction between algorithm adaptability and system complexity by automating the update process.
Solution Approach 2:
The system employs self-updating mechanisms where the algorithm automatically revises itself through machine learning without requiring manual intervention. The software serves itself by automatically training on new data and updating its own parameters, eliminating the need for users to manually revise algorithms while maintaining high adaptability.
2Adaptability or versatility
If end users manually revise the underlying algorithm including various weight factors, then the algorithm can be customized, but the process is time-consuming and error-prone
Solution Approach 1:
The system automatically performs algorithm revision through machine learning processes. Instead of requiring users to manually adjust weight factors and algorithm parameters, the system self-updates by training on new data and automatically optimizing its own structure, thereby achieving customization without manual time investment.
Solution Approach 2:
The system incorporates feedback mechanisms where user interactions and new data automatically feed into the machine learning model. The algorithm continuously learns from feedback and automatically adjusts its parameters, eliminating the need for manual revision while maintaining adaptability to changing requirements.
3Adaptability or versatility
If conventional software solutions require end users to be familiar with complex coding to revise the underlying algorithm, then the software maintains flexibility, but most employees have little or no software programming knowledge
Solution Approach 1:
The patent replaces manual coding operations with automated machine learning processes. Instead of requiring users to write and modify code, the system uses automated algorithms and statistical learning methods that do not require programming knowledge, thereby maintaining algorithmic flexibility while dramatically improving ease of operation.
Solution Approach 2:
The system performs algorithm revision automatically without user intervention. The machine learning model self-updates based on incoming data and feedback, eliminating the need for users to possess programming skills while maintaining full algorithmic flexibility through automated adaptation.
4Ease of operation
If a central server automatically revises the algorithm used by spreadsheets, then user experience is improved, but the system complexity increases
Solution Approach 1:
The patent introduces a central server as an intermediary that handles the complexity of algorithm revision. The server acts as a mediator between data sources and local spreadsheet applications, automatically performing machine learning and algorithm updates while presenting a simple interface to users. This centralizes complexity in a dedicated component while maintaining simplicity at the user level.
Data Source
AI summary
Disclosed herein are embodiments of systems, methods, and products comprises an analytic server, which evaluates user data for premium financing status and dynamically renders graphical user interfaces. The server trains an artificial intelligence model based on historical user data. The artificial intelligence model comprises one or more data points with each data point representing one of a plurality of attributes and applies a logistic regression algorithm to identify a weight factor for each attribute. The server uses a dynamic algorithm to generate a score by combining the plurality of attributes based on the weight factors. The server receives responses regarding the scores that indicate the premium financing status of each case. The server retrains the artificial intelligence model to identify new weight factors based on negative responses data. The server automatically displays new scores calculated based on the new weight factors.