Understanding Credit Card User's Behaviour
Bibliographic record
Abstract
In the last few years, a large number of companies are starting to realize the value of their databases. These databases, which usually cover transactions performed over several years, may lead to a better understanding of the customer’s profile, thus supporting the offer of new products or services. The treatment of these large databases surpasses the human ability to understand and efficiently deal with these data, creating the need for a new generation of tools and techniques to perform automatic and intelligent analyses of large databases. The extraction of useful knowledge from large databases is named knowledge discovery. Knowledge discovery is a very demanding task and requires the use of sophisticated techniques. The recent advances in hardware and software make possible the development of new computing tools to support such tasks. Knowledge discovery in databases comprises a sequence of stages. One of its main stages, the data mining process, provides efficient methods and tools to extract meaningful information from large databases. In this chapter, data mining methods are used to predict the behavior of credit card users. These methods are employed to extract meaningful knowledge from a credit card database using machine learning techniques. The performance of these techniques are compared by analyzing both their correct classification rates and the knowledge extracted in a linguistic representation (rule sets or decision trees). The use of a linguistic representation for expressing knowledge acquired by learning systems aims to improve the user understanding. Under this assumption, and to make sure that these systems will be accepted, several techniques have been developed by the artificial intelligence community, using both the symbolic and the connectionist approaches.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".