AI Risk Model for XaaS Pricing
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Solution Overview
Problem
Current Anything as a Service (XaaS) models face challenges in accurately predicting and pricing user support costs, leading to potential undercharging of high-risk users and overcharging of low-risk users, due to flat pricing approaches that disregard unique user characteristics.
Innovation Solution
Implementing an explainable artificial intelligence (XAI) risk model that differentiates between immutable and mutable features using a causal tree model, allowing for personalized predictions and recommendations to optimize pricing based on user-specific data, including site conditions and product usage patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If flat pricing approaches are used for XaaS models, then pricing simplicity is maintained, but accuracy in predicting and pricing user support costs deteriorates
Solution Approach 1:
The patent segments users into different risk groups based on their characteristics and behavior patterns. By dividing the user base into segments with similar support cost profiles, the system can apply differentiated pricing to each segment, improving overall pricing accuracy while maintaining simplicity through standardized segment-based pricing rules rather than individualized assessments.
Solution Approach 2:
The patent changes the pricing parameter from a flat uniform rate to a risk-adjusted variable rate based on user characteristics. By introducing risk scores derived from multiple user attributes (company size, product usage, support history), the system transforms pricing from a static parameter to a dynamic one that reflects actual support cost variations across different user segments.
2Measurement precision
If risk-based personalized pricing is implemented, then support cost prediction accuracy is improved, but system complexity increases
Solution Approach 1:
The patent performs preliminary actions by pre-calculating risk scores and segmenting users before the actual pricing process. By conducting clustering analysis and risk assessment in advance, the system prepares user segments and their corresponding pricing multipliers beforehand, so that during service delivery, pricing can be applied automatically based on pre-determined segments without real-time complex calculations.
Solution Approach 2:
The patent introduces risk scores and user segments as intermediary concepts between raw user data and final pricing decisions. These intermediaries simplify the complexity by aggregating multiple user attributes into a single risk score, which then maps to predefined pricing segments, creating a layered approach that manages complexity at each stage rather than processing all raw data directly.
3Ease of manufacture
If user characteristics are disregarded in pricing, then pricing fairness is simplified, but user satisfaction deteriorates due to overcharging low-risk users
Solution Approach 1:
The patent applies local quality by tailoring pricing to specific user segments rather than applying a uniform rate to all users. Each user segment receives pricing optimized for their specific risk profile and characteristics, allowing low-risk users to be charged less and high-risk users to be charged more, thereby improving overall user satisfaction while maintaining a structured approach through segment-based differentiation.
Data Source
AI summary
A system can train an artificial intelligence risk model to produce a trained model, wherein labeled training data for the training comprises respective features of users and products, and corresponding labels of respective support costs, wherein the trained model comprises a causal tree model that is configured to differentiate between first features that are immutable to an entity that utilizes the trained model and second features that are mutable to the entity. The system can, in response to applying a input to the trained model, wherein the input comprises a feature of a user and a product, produce an output that indicates a predicted support cost that corresponds to the input.