METODOLOGÍA PARA UN SCORING DE CLIENTES SIN REFERENCIAS CREDITICIAS
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it