METODOLOGÍA PARA UN SCORING DE CLIENTES SIN REFERENCIAS CREDITICIAS
Bibliographic record
Abstract
Las decisiones de otorgamiento de crédito son cruciales en la administración deriesgos. Las instituciones financieras han desarrollado y usado modelos de creditscoring para estandarizar y automatizar las decisiones de crédito, sin embargo,no es común encontrar metodologías para aplicarlos a clientes sin referencias cre-diticias, es decir clientes que carecen de información en los burós nacionales de crédito. En este trabajo se presenta una metodología general para construir unmodelo sencillo de credit scoring enfocado justamente a esa población, la cual havenido tomando una mayor importancia en el sector crediticio latinoamericano. Seusa la información sociodemográfica proveniente de las solicitudes de crédito deuna pequeña institución bancaria mexicana para ejemplificar la metodología.Palabras clave: Scorecard, CHAID, logit, administración de riesgos, crédito.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".