A survey - data mining frameworks in credit card processing
Bibliographic record
Abstract
During the last two decades, the credit card system has been widely used as a mechanism to drive the global economy to grow dramatically. A credit card provider has issued millions of credit cards to its customers. However, issuing credit cards to wrong customers can be a crucial factor of a financial crisis, e.g., the ones happened in 1997 and 2008. This paper presents a systematic analysis and a comprehensive review of data mining techniques and their applications in the credit card process which we divide into 4 main activities. We have studied research works which were published between 2007 and the first quarter of 2015 inclusively. Our work focuses on data mining techniques applied specifically in the credit card process, and this makes our review different from others' which emphasize much wider areas. As a result, this survey can be useful for any credit card provider to select an appropriate solution for their problem and, also, for researchers to have a comprehensive view of the literature in this area.
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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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.011 | 0.024 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".