Hybrid Machine Learning for Fraud Detection: Balancing Accuracy and Security in Digital Transactions
Bibliographic record
Abstract
Nowadays, frauds would occur by hackers and non-legitimate users, which would create losses for specific users and damage their identity.The kinds of fraud that popularly happen are transaction fraud, card fraud (card not present), phishing, and account takeover.To prevent and minimize the losses, the hybrid model is designed and demanded.The combination of random forests, LightGBM, and Ensemble is used to improve overall performance and accuracy improvement and ensure privacy and security concerns.In this methodology, random forests reduce overfitting, support large datasets, prefer ranked features, are less sensitive to noise, and result in improvement in accuracy.The role of LightGBM is to ensure boosting in speed and memory usage, support large datasets and imbalanced datasets, and ensure reduced false positives and false negatives.The necessity of an ensemble strategy in this scenario is to combine the benefits of random forest and LightGBM, ensure overall performance, and eliminate flagging legitimate transactions as fraudulent.The performance measures are evaluated and compared against the considered models in this domain.
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 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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| 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".