Fidelity-Driven Evaluation of XAI Techniques for Fraud Detection in Decentralized Loyalty Platforms
Bibliographic record
Abstract
Blockchain-based loyalty platforms can enhance brand loyalty, boosting customer retention. Yet just like any software they remain vulnerable to fraud. We present a transparent fraud-detection framework for PointXchange, a blockchain-based customer loyalty platform that enables brands to collaboratively manage and exchange reward points in a decentralized environment. The study evaluates three gradient boosting machines and two neural networks on one public and one private dataset. To interpret our model, we apply SHAP, LIME, and LRP, in addition to introducing a surrogate-based fidelity assessment. The top performing predictors on two datasets achieve up to 0.89 and 0.93 F1-scores, respectively, while the top performing explanations yield a fidelity score of complete agreement. These results demonstrate the potential of explainable fraud detection in blockchain-based loyalty systems, contributing to both security and transparency by not just empirically evaluating existing approaches but tailoring them to a real-world system.
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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 | 0.011 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".