A Fuzzy Logic Model for Loan Recommendations in Online Lending Systems Using the California Psychological Inventory
Bibliographic record
Abstract
This study presents the development of a fuzzy rule-based system leveraging California Psychological Inventory (CPI) values as input variables to provide loan percentage recommendations.By analyzing data to determine the weights of relevant input variables, the findings reveal that significant weights were assigned to Control (0.498), Ego Strength (0.315), Social Presence (0.288), Warmth (0.187), Self-Acceptance (0.278), Empathy (0.572), Achievement via Conformance (0.524), Responsibility (0.374), Flexibility (0.085), and Realistic (0.072).The implementation of these weights allowed for the creation of 1,024 fuzzy rule bases using the IF-Then function.The analysis illustrates that applying CPI in loan amount recommendations can reduce losses from bad loans by up to 20%.The research emphasizes the importance of integrating psychometric assessments into credit evaluations, leading to improved decision-making for potential debtors and enhanced financial stability within the online lending sector.Furthermore, the findings provide a structured framework for future research, which should include additional variables to refine the assessment of loan suitability for borrowers.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".