Machine Learning Activity Adjustment for Survey-Free Financial Assessment
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Solution Overview
Problem
Financial health assessment surveys face challenges such as low participation rates due to time and effort requirements, sampling bias, and inability to establish causal relationships, leading to unreliable and untimely interventions.
Innovation Solution
A computing system using a machine learning model with unsupervised learning to predict survey responses based on personal data, enabling continuous assessment without direct user input, and adjusting settings or sending communications based on detected changes in user relationships.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional survey methods are used to assess financial health, then comprehensive data can be collected, but participation rates are low and sampling bias occurs
Solution Approach 1:
The patent replaces the mechanical survey completion process with an automated machine learning system that analyzes existing personal data from transaction histories, account information, and behavioral patterns. This substitution eliminates the need for active user participation while maintaining assessment accuracy through algorithmic analysis of objective financial data.
Solution Approach 2:
The system creates a virtual representation of the user's financial health by training a machine learning model on their personal data. This digital twin or copy of the user's financial profile allows continuous assessment without requiring the user to repeatedly complete surveys, thereby eliminating sampling bias from non-participation.
2Measurement precision
If surveys are repeated to track changes over time, then updated assessment data can be obtained, but respondent fatigue increases and data reliability decreases
Solution Approach 1:
The system enables continuous financial health assessment by automatically and continuously analyzing the user's personal data from transaction histories and account information. This continuous monitoring replaces discrete survey repetitions, providing updated assessments without requiring additional user time investment or risking respondent fatigue.
Solution Approach 2:
The machine learning system performs self-service by automatically analyzing the user's existing financial data without requiring their active participation. The system autonomously detects changes in financial behavior and updates assessments, eliminating the need for users to repeatedly invest time in survey completion.
3Loss of information
If detailed survey questions are asked to establish causal relationships, then deeper insights can be gained, but the survey becomes more intrusive and time-consuming
Solution Approach 1:
The patent replaces intrusive survey questions with automated analysis of objective transaction data and behavioral patterns. The machine learning model infers causal relationships from this existing data, such as identifying how spending patterns affect financial health, without requiring users to answer sensitive or time-consuming questions about their personal finances.
Solution Approach 2:
The system uses an intermediary machine learning model that acts as a mediator between the user's personal data and the assessment results. This intermediary analyzes transaction histories, account information, and behavioral patterns to derive causal relationships, eliminating the need for direct user questioning while still obtaining deep insights.
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 predict a first predicted assessment score at a first instance and a second predicted assessment score at a second instance with respect to a second user. The computing system determines whether the first predicted assessment score is different from the second predicted assessment score, and whether a first data entry of the personal data set of the second user changed between the first instance and the second instance. The computing system takes or recommends an action corresponding to a reversal in the change in the first data entry in order to alter the predicted assessment score of the second user.