A Model Based on Clustering and Association Rules for Detection of Fraud in Banking Transactions
Bibliographic record
Abstract
In recent years, fraud in banking transactions has turned into a serious problem for which different supervised and unsupervised algorithms have been suggested.In this paper, a semi-supervised combined model based on clustering algorithms and association rule mining is devised in order to detect frauds and suspicious behaviors in banking transactions.To this end original and non-fraud transaction data of the customers is collected for the analysis.Next, repetitive patterns of customer behaviors are extracted through association rules and used as normal rules so that any new transaction must conform to at least one of these rules.In behavior analysis component, a fuzzy clustering algorithm is employed to extract the normal behavior patterns of customers.Abnormal transactions belong to none of these clusters and will be recognized as high risk.The final understanding of a transaction will be gained through combining the results of association rules and clustering patterns.Findings suggest that the employment of both rule-based and clustering-based components leads to the detection of more frauds while fewer alarms will go off.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".