Financial Conscientiousness Score via Visual Choice ML
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Solution Overview
Problem
Current methods for determining creditworthiness are inefficient for individuals without access to traditional credit data, as they often rely on language-dependent psychological tests that can be manipulated and do not account for creditworthiness validation.
Innovation Solution
A processor-implemented method using a machine learning model trained on visual choices from a client device, which correlates trait scores derived from a hybrid psychological framework with credit history or loan repayment records to determine a financial conscientiousness score, incorporating numeracy questions and time constraints to prevent manipulation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional credit scoring methods are used, then creditworthiness can be determined for individuals with credit history, but the method fails for individuals without access to traditional credit data (young people, rural residents)
Solution Approach 1:
The patent introduces psychological traits as an intermediary measure to assess creditworthiness. Instead of directly using credit history (which is unavailable for many), the system uses psychological assessments to infer traits like conscientiousness, integrity, and emotional stability that correlate with credit behavior. This intermediary approach enables creditworthiness assessment for individuals without traditional credit data while maintaining reliability through validated psychological frameworks.
Solution Approach 2:
The patent changes the assessment parameters from traditional financial metrics (credit history, income, employment) to psychological parameters (conscientiousness, integrity, emotional stability). By measuring different parameters that are more universally accessible and less susceptible to manipulation, the system can assess creditworthiness for a broader population including young people and rural residents who lack traditional credit records.
2Ease of operation
If language-based psychological tests are used, then psychological traits can be assessed, but the tests can be manipulated by users and require translation to multiple languages
Solution Approach 1:
The patent replaces language-based mechanical response systems with visual choice-based assessment. Instead of requiring users to read and interpret text questions in their native language (which requires translation and allows for manipulation), the system presents visual scenarios and measures automatic emotional and cognitive responses. This substitution eliminates language barriers and reduces manipulation opportunities while maintaining assessment accessibility.
Solution Approach 2:
The patent inverts the traditional psychological testing approach. Instead of presenting text questions and asking for verbal/written responses (which users can manipulate), the system presents visual stimuli and measures automatic physiological and behavioral responses. This inversion captures more authentic psychological traits by measuring what users cannot consciously control or fake.
3Measurement precision
If users are given sufficient time to evaluate psychological test questions, then they can provide thoughtful responses, but they can also manipulate their answers to obtain desired scores
Solution Approach 1:
The patent uses time constraints as a periodic action mechanism to prevent manipulation. By limiting the time users have to respond to visual scenarios, the system captures automatic, authentic psychological responses before users can engage in deliberate manipulation. The time-bound nature of the assessment ensures responses reflect genuine psychological traits rather than calculated answers designed to achieve desired scores.
Data Source
AI summary
A method of deriving a financial conscientiousness score from visual choices, using a machine learning model that is trained at a server is provided. The method includes (i) obtaining visual selection as an input from the user that is selected from one or more different visual choices that are displayed to the user, (ii) determining trait scores for a subset of traits based on the points that are attributed to the visual choices, (iii) training, a machine learning model by correlating the trait scores for the subset of traits with a previous credit history or a loan repayment record of the user to obtain a trained machine learning model, and (iv) determining, using the trained machine learning model, a financial conscientiousness score based on the trait scores for the subset of traits.