Who Benefits from Alternative Data for Credit Scoring? Evidence from Peru
Bibliographic record
Abstract
The World Bank estimates that 1.4 billion individuals worldwide are unbanked, lacking access to credit due to the absence of traditional credit scores. In this article, the authors demonstrate how retail transaction data can be used to construct an alternative credit score, potentially expanding credit access for these individuals. The study utilizes a unique dataset obtained through a partnership with a Peruvian company. The authors merge customer loyalty data and credit card repayment data with administrative records from the Peruvian financial system that provide individuals’ detailed financial histories. This comprehensive dataset allows the authors to construct credit scores for people both with and without a credit history. Through simulations of credit card approval decisions, they find that incorporating retail data increases approval rates for individuals without a credit history, from 16% to between 31% and 48%. In contrast, for those with an established credit history, approval rates remain largely unchanged, at around 88%. The authors investigate why retail data particularly benefits people without a credit history and discuss the broader implications of this credit scoring methodology for consumers, firms, and policy makers. The findings highlight the methodology’s potential to transform credit access for millions of previously unbanked individuals.
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How this classification was reachedexpand
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 | 0.030 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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; both teacher heads agree on what is shown here.
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".