The Value of Open Banking Data for Application Credit Scoring: Case Study of a Norwegian Bank
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
Banks generally use credit scoring models to assess the creditworthiness of customers when they apply for loans or credit. These models perform significantly worse when used on potential new customers than existing customers, due to the lack of financial behavioral data for new bank customers. Access to such data could therefore increase banks’ profitability when recruiting new customers. If allowed by the customer, Open Banking APIs can provide access to balances and transactions from the past 90 days before the score date. In this study, we compare the performance of conventional application credit scoring models currently in use by a Norwegian bank with a deep learning model trained solely on transaction data available through Open Banking APIs. We evaluate the performance in terms of the AUC and Brier score and find that the models based on Open Banking data alone are surprisingly effective in predicting default compared to the conventional credit scoring models. Furthermore, an ensemble model trained on both traditional credit scoring data and features extracted from the deep learning model further outperforms the conventional application credit scoring model for new customers and narrows the performance gap between application credit scoring models for existing and new customers. Therefore, we argue that banks can increase their profitability by utilizing data available through Open Banking APIs when recruiting new customers.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 it