ML Model Predicts Financial Assessment Scores
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Solution Overview
Problem
Existing financial health assessment surveys face challenges such as low participation rates due to time and effort requirements, intrusive nature, and sampling biases, which hinder reliable assessment of financial well-being over time and the establishment of causal relationships between survey responses.
Innovation Solution
A system and method using a machine learning model to predict survey responses based on personal data, generating a predictive model during training, correlating personal data sets of users to predict assessment scores, and automatically reporting these scores to users, thereby eliminating the need for repeated surveys.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional assessment surveys are used to evaluate financial well-being, then assessment scores can be obtained, but participation rates remain low due to time and effort requirements
Solution Approach 1:
The patent creates a virtual copy of the survey assessment process by using a machine learning model that predicts survey responses and generates assessment scores without requiring actual survey completion. The ML model replicates the assessment function while eliminating the need for users to manually complete the survey, thereby maintaining assessment reliability while dramatically improving participation rates.
Solution Approach 2:
The patent replaces the mechanical process of manual survey completion with an automated machine learning system. Instead of requiring users to physically respond to survey questions, the system uses ML algorithms to automatically generate assessment scores based on predicted responses, substituting the mechanical survey-taking process with an automated computational approach.
2Duration of action of moving object
If repeated surveys are administered to track changes over time, then longitudinal data can be collected, but sampling biases increase and causal relationships cannot be established
Solution Approach 1:
The patent enables continuous longitudinal tracking by continuously updating the machine learning model with new personal data without requiring repeated survey administrations. The system maintains continuous assessment capability through automated model updates, eliminating the discontinuous nature of repeated surveys and the associated sampling biases that prevent causal relationship determination.
Solution Approach 2:
The patent implements feedback mechanisms where the machine learning model continuously learns from new personal data and adjusts its predictions accordingly. This feedback loop allows the system to track changes over time and establish causal relationships by observing how changes in personal data affect assessment scores, something that cannot be achieved through traditional repeated surveys.
3Ease of operation
If survey completion is made optional, then user convenience is improved, but sampling bias is introduced in the aggregate results
Solution Approach 1:
The patent creates a virtual copy of the assessment process that operates independently of actual survey completion. The machine learning model generates assessment scores based on predicted responses derived from personal data, creating a parallel assessment system that maintains aggregate result accuracy without requiring mandatory survey participation, thus preserving both convenience and reliability.
Data Source
AI summary
A computing system is configured to generate a predictive model during training of a machine learning program using a training data set including a personal data set of a plurality of first users. The predictive model is configured to generate a predicted assessment score with respect to a second user by correlating a personal data set of the second user to the personal data set of at least one of the first users, with the generating of the predicted assessment score occurring automatically when a data entry of the personal data set of the second user is determined to have changed by the computing system. The computing system is configured to report the automatically generated predicted assessment score to the second user via a user device of the second user.