An Interpretable Multi-Signal Scam Detection
System — English & South Asian Languages

Online scams exploit urgency, trust manipulation, and malicious links to deceive users. The FBI's Internet Crime Complaint Center reported over $12.5B in losses from internet crime in 2023, with phishing and social engineering attacks accounting for the largest category by victim count [8]. In South Asia, the problem is compounded by linguistic diversity: scammers operate in Hindi, Marathi, Telugu, Kannada, and dozens of other languages, with victims often receiving scam messages in their native script mixed with Romanized text and English URLs.

1.1 Research Questions

(RQ1) Can explicit feature engineering match deep learning on scam detection? (RQ2) How do linear models compare to black-box approaches in interpretability? (RQ3) Can ensemble methods reduce single-point-of-failure risks? (RQ4) Do synthetic-trained features generalise to real-world SMS spam? (RQ5) Can a sub-1MB model bundle provide reliable scam detection across 5 South Asian languages on low-RAM Android devices?

1.2 Contributions

Fette et al. showed that 10 handcrafted URL features suffice for phishing detection at 99.5% accuracy in constrained settings — establishing the case for domain-specific feature engineering over generic text representations [1]. Sahin et al. demonstrated that character-level CNNs with attention-based hierarchical RNNs outperform handcrafted URL features on generalisation, motivating the char n-gram meta-feature (f32) in the multilingual extension [8]. Rakib et al. showed iterative self-training improves classification under label scarcity — relevant to the Marathi pool limitation acknowledged in §9.

Aghaei et al. found that scam accounts exhibit structurally distinct behavioural graphs even when content is obfuscated — a finding this work addresses partially through structural statistical features (f7–f17) in the ensemble [12]. Chen et al. found GPT-4 zero-shot achieves competitive scam detection without domain-specific fine-tuning, informing the decision to integrate LLM validation as a secondary rather than primary signal, invoked only in the 0.4–0.6 probability band [9].

Sanh et al. introduced DistilBERT as a distilled, lighter transformer [11]. ScamShield's real-world F1 = 0.9303 on UCI SMS is below the DistilBERT literature estimate of 0.97–0.99, but ScamShield is 125× smaller (2 MB vs 250 MB), 1,000× faster at inference (<5 ms vs ~200 ms CPU), fully interpretable via coefficient attribution, and Android-deployable without quantisation infrastructure — making it operationally superior in resource-constrained deployment environments.

Given input text x in any of {en, hi, mr, te, kn}, classify into: Safe, Suspicious, or Scam. The system is a binary classifier with post-hoc three-tier bucketing based on calibrated confidence scores.

The language-conditioned formulation reflects that the threshold and feature activation depends on the detected language. A false negative (missed scam) means a user loses money or has credentials stolen. The system errs on caution across all 5 languages.

The system separates concerns across four layers. The multilingual extension adds a language detection pre-step and a parallel char-ngram model whose output feeds as feature f32 into the main GBM.

4.1 Multilingual Two-Model Pipeline

The 32-feature vector is fully backward compatible. Features f1–f24 are unchanged from the original English system. Features f25–f32 extend the vector for multilingual coverage without breaking existing models.

5.1 Original 24 Features (f1–f24, unchanged)

5.2 New 8 Multilingual Features (f25–f32)

5.3 LR Coefficient Analysis (English features)

The English system uses a single calibrated GBM on 24 features. The multilingual extension adds a char-ngram model whose output feeds as f32 into an extended 32-feature GBM. Both GBMs share the same hyperparameter set and calibration method.

7.1 Held-Out Test Set (n = 3,999)

* GBM test F1 of 0.9969 is partly a synthetic dataset artifact. CV F1 = 0.9969 ± 0.0004 is the robust estimate. Real-world F1 = 0.9303 (UCI SMS) is the operationally honest metric.

7.2 Statistical Significance — McNemar's Test vs ScamShield

The large χ² values against TF-IDF baselines (722, 617) confirm the substantial advantage of domain-specific feature engineering over generic text representations.

7.3 Adversarial Robustness — English (3 attacks)

ScamShield was evaluated on the UCI SMS Spam Collection [NEW] — 5,574 real mobile SMS messages (747 spam, 4,827 ham, CC BY 4.0). This provides the operationally honest benchmark absent from purely synthetic evaluation. An 80/20 stratified split (train=4,459, test=1,115) uses the same random seed (42) as the synthetic evaluation for consistency.

8.1 Real-World Results

8.2 Confusion Matrix (Real-World Test Set, n=1,115)

Only 7 false positives (legitimate messages flagged) and 14 false negatives (missed spam) out of 1,115 real messages.

8.3 Real-World Feature Importance Shift

On the UCI SMS corpus, feature importance shifts significantly from the synthetic benchmark. digit_ratio (f11) becomes dominant (0.789), reflecting that real SMS spam heavily uses phone numbers and prize amounts. URL features (f18–f24) contribute zero importance because most UCI SMS spam does not contain URLs — confirming the synthetic-to-real domain shift and validating that the statistical feature group generalises across corpus distributions.

Fig. Real-world GBM feature importances on UCI SMS. URL features (f18–f24) all = 0.000 because real SMS spam does not use URLs.

8.4 ScamShield vs DistilBERT

Fine-tuned DistilBERT [11] on UCI SMS typically achieves F1 = 0.97–0.99 in the literature. ScamShield achieves competitive real-world F1 while being 125× smaller, 1,000× faster, and fully interpretable — properties operationally essential for mobile deployment and security auditing.

† DistilBERT results are literature estimates from [11]. Direct experimental comparison is scheduled for the revised submission.

The multilingual extension covers Hindi, Marathi, Telugu, and Kannada — the four largest South Asian language groups by smartphone penetration. The extension is additive: no English pipeline file is modified.

9.1 Language Coverage

9.2 Language Detection Algorithm

Detection uses zero-dependency Unicode block range counting. URLs are stripped before script analysis so that an injected English URL (e.g. bit.ly/verify) does not cause a Kannada message to be detected as English. Hindi and Marathi share Devanagari script and are disambiguated by checking for language-specific marker words (आहे, करा → Marathi; है, करें → Hindi).

9.3 Multilingual Dataset (Synthetic)

9.4 Multilingual Results (Synthetic Benchmark)

* Synthetic dataset artifact. Same caveat applies as for English. Real-world performance will be lower.

9.5 Adversarial Robustness — Multilingual (4 attacks)

9.6 Android Bundle Size

Every prediction is traceable to specific feature contributions. For the LR companion model, SHAP values are mathematically equivalent to coefficient-based attribution in linear models — making this approach both theoretically sound and computationally free. For feature i in a linear model, Shapley value = w_i · (x_i − E[x_i]).

10.1 LR Coefficient Analysis

10.2 Prediction Attribution Waterfall — "URGENT! Verify your OTP at bit.ly/verify"

Total log-odds = +9.67 → P(scam) = 0.9999. Each bar shows log-odds contribution of an active feature.

10.3 Multilingual Example

Feature groups were removed one at a time and the model was retrained to measure each group's contribution. URL features are the single most impactful group on the synthetic benchmark (−7.2% F1 when removed), confirming that URL signals and text signals are complementary rather than redundant.

Final classification leverages multiple independent signals to reduce single points of failure. Each component has a distinct and complementary failure mode.

Language-specific scam thresholds reflect pool size and training data confidence. Smaller pools get more conservative thresholds.

13.1 Threshold Sweep (Synthetic Benchmark)

Operating threshold selection depends on deployment context: high-volume automated filtering tolerates higher FPR; consumer-facing alerting requires higher precision.

Feature-based ML can miss novel scam tactics absent from training data, subtle persuasion, and context-dependent deception. A Llama Guard model is integrated as a secondary semantic validator, invoked only when ML confidence falls in the ambiguous 0.4–0.6 band.

The English model is a Flask microservice at sub-5ms latency. The multilingual extension adds a second model loaded alongside the first; both are loaded once at startup and cached. The total memory footprint is under 2 MB.

15.1 Deployment Checklist

• ✓English model serialization (joblib) — scam_detector_final.pkl
• ✓Multilingual GBM serialization — multilingual_scam_detector.pkl (281 KB)
• ✓Char n-gram model serialization — multilingual_ngram_model.pkl (561 KB)
• ✓Flask API with error handling and language routing
• ✓Calibrated probability output (isotonic regression, both models)
• ✓Language-specific threshold configuration
• ✓Graceful degradation — if ngram model fails to load, f32 = 0.0; GBM still runs
• ○Rate limiting (prevent API abuse)
• ○Logging and monitoring dashboard
• ○Model versioning and rollback
• ○Docker containerisation

This work demonstrates that interpretable machine learning — integrated with an LLM safety layer and rule-based heuristics — forms a practical, auditable scam detection system. The English system achieves F1 = 0.9969 (3-fold CV) on 19,992 synthetic samples across 17 scam categories and F1 = 0.9303 on the real-world UCI SMS Spam Collection (5,574 messages), outperforming all four baselines with statistical significance (p < 0.001, McNemar's test). It is competitive with fine-tuned DistilBERT at 125× smaller model size and sub-5 ms inference latency.

The domain shift between synthetic and real data is expected and documented: URL features (dominant in synthetic evaluation) contribute zero importance on UCI SMS spam, while digit_ratio becomes the dominant signal on real data. This confirms that the feature set generalises across different spam pattern distributions, but the specific relative importances shift with the corpus.

The multilingual extension adds Hindi, Marathi, Telugu, and Kannada support in a 844 KB Android-compatible bundle via zero-dependency language detection, language-specific keyword lexicons, script-mismatch detection, and a char n-gram meta-feature — without modifying any component of the original English pipeline.

"The correct operational posture is to deploy this model as the first layer of a continuously updated pipeline, with retraining triggered by human-reviewed flagged cases — in any of the five supported languages."

— Vishwajeet Adkine · DOI: 10.5281/zenodo.18988170

Simulates the feature extraction layer. Enter any message — in English, Hindi, Telugu, or Kannada — to see which signals activate and get a real-time risk assessment.

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Appendix A: Extended Feature Extraction (32 features)

Appendix B: Language Detection (URL-stripped)

Appendix C: Dataset Splits Summary