Enhancing Financial Fraud Detection Through Chimp-Optimized Long Short-Term Memory Networks
Bibliographic record
Abstract
The proliferation of online shopping has led to a substantial increase in payment card transactions, accompanied by a parallel rise in fraudulent activities.Such frauds impose significant financial burdens on both businesses and banking institutions annually.In response to this growing concern, a novel hybrid methodology has been developed, integrating a metaheuristic optimization algorithm with a neural network classifier, aimed at the automatic detection of financial transaction fraud.This method, termed Chimp-Optimized Long Short-Term Memory Networks (ChOpt+LSTM), operates in two sequential phases.Initially, an optimization algorithm based on chimp behavior is utilized for the selection of the most pertinent features for fraud detection.Subsequently, these features inform the training of a Long Short-Term Memory (LSTM) classifier model, specifically designed for the identification of credit card fraud.An extensive comparative analysis reveals that the proposed ChOpt+LSTM method surpasses existing techniques in several key performance metrics.Notably, it achieves a classification accuracy of 99.18%, a mean absolute error (MAE) reduction to 25.7, a mean squared error (MSE) reduction to 16.3, alongside precision, recall, and F1 scores of 98.54%, 98.47%, and 96.58%, respectively.These findings underscore the efficacy of combining chimp optimization algorithms with LSTM classifiers in enhancing the accuracy and reliability of financial fraud detection systems.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".