AI Against Smishing in Kenya: Culturally Adapted SMS Scam Detection for Digital Trust
Bibliographic record
Abstract
Kenya’s mobile-financial ecosystem, driven by M-Pesa and a mobile penetration rate above 133%, has advanced commerce and financial inclusion but also exposed users to SMS-based scams exploiting linguistic diversity, cultural trust, and psychological manipulation. Existing fraud-reporting mechanisms such as keyword filtering and manual reporting, remain reactive and inadequate against these evolving threats. To fill this gap, this study develops a culturally adapted, machine learning–driven approach to SMS scam detection tailored to Kenya’s multilingual environment. Using a crowdsourced dataset of 738 SMS messages (427 scams, 311 legitimate), XGBoost achieved 83.8% accuracy with strong precision and F1-scores, while Logistic Regression offered superior recall (91.8%) in detecting fraudulent content. SHAP analysis revealed urgency cues, code-switching features, and social proof language as the most influential predictors of scams. The proposed model, designed for integration via APIs into USSD platforms, enables real-time detection and fosters user confidence through explainable alerts. This study provides a scalable, context-aware framework for fraud prevention in mobile-first economies and actionable insights for telecom operators, fintech platforms, and policymakers seeking to strengthen digital trust.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".