Journal of Artificial Intelligence and Digital Health

ABSTRACT

Background

Mobile money platforms in low‑trust environments face audit trail forgery risks. Existing methods rely on server logs that can be altered by insiders.

Methods

We propose transaction fingerprinting using temporal, network, device, and spatial features, grounded in Tamper Constraint Theory (TCT)a formal framework positing that forgeries violate temporal continuity, spatial feasibility, device persistence, or behavioural entropy constraints. We evaluate on a Kenyan dataset (2.5M transactions, 12 months) with adversarially constrained simulated forgeries (temporal shift, agent swap, device spoofing) filtered via expert realism scoring (n=3 fraud analysts), plus 212 real fraud cases for external validation. A random forest classifier is benchmarked against rule‑based, logistic regression, and isolation forest models. We report PR‑AUC, calibration, group‑aware validation (leave‑device‑out, leave‑agent‑out), cost‑sensitive performance, and adversarial robustness.

Results

The fingerprinting method achieves AUC = 0.97 (95% CI: 0.96–0.98), PR‑AUC = 0.94 (0.92–0.96), sensitivity = 94% at 3% FPR, Brier score = 0.04. Temporal entropy (STi=0.41) and device consistency (STi=0.33) dominate. Group‑aware validation shows minimal overfitting (AUC drop ≤0.012). Under adaptive adversarial attacks, AUC degrades to 0.85–0.91. Expected monetary loss reduction is 82%, ROI = 480% for a regulator. 

Conclusion

TCT‑grounded transaction fingerprinting provides a robust, server‑independent audit trail. Regulators can deploy it cost‑effectively.

Keywords: Artificial Intelligence; Solar Energy Management; Optimization Algorithms; Forecasting Efficiency; Photovoltaic Systems