Loss Rate Forecasting Framework Based on Macroeconomic Changes:\n Application to US Credit Card Industry
Bibliographic record
Abstract
A major part of the balance sheets of the largest US banks consists of credit\ncard portfolios. Hence, managing the charge-off rates is a vital task for the\nprofitability of the credit card industry. Different macroeconomic conditions\naffect individuals' behavior in paying down their debts. In this paper, we\npropose an expert system for loss forecasting in the credit card industry using\nmacroeconomic indicators. We select the indicators based on a thorough review\nof the literature and experts' opinions covering all aspects of the economy,\nconsumer, business, and government sectors. The state of the art machine\nlearning models are used to develop the proposed expert system framework. We\ndevelop two versions of the forecasting expert system, which utilize different\napproaches to select between the lags added to each indicator. Among 19\nmacroeconomic indicators that were used as the input, six were used in the\nmodel with optimal lags, and seven indicators were selected by the model using\nall lags. The features that were selected by each of these models covered all\nthree sectors of the economy. Using the charge-off data for the top 100 US\nbanks ranked by assets from the first quarter of 1985 to the second quarter of\n2019, we achieve mean squared error values of 1.15E-03 and 1.04E-03 using the\nmodel with optimal lags and the model with all lags, respectively. The proposed\nexpert system gives a holistic view of the economy to the practitioners in the\ncredit card industry and helps them to see the impact of different\nmacroeconomic conditions on their future loss.\n
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".