Our Detection Methodology

The Scam Message Scanner uses a multi-layered analysis approach to identify scams, phishing attempts, and fraudulent messages. All analysis happens locally in your browser—no data is sent to any server.

Detection Layers

1
Urgency Language Detection
Identifies pressure tactics designed to rush you into acting before thinking.
2
Suspicious URL Analysis
Examines links for known scam patterns and deceptive redirects.
3
Financial Language Scanning
Detects requests for money, payment details, or financial actions.
4
Personal Information Requests
Flags attempts to collect sensitive personal or identity data.
5
Brand Impersonation Detection
Spots messages pretending to be from trusted companies or organisations.
6
Grammar & Language Quality
Analyses writing quality for patterns commonly found in fraudulent messages.
7
Emotional Manipulation Detection
Identifies psychological tactics used to exploit fear, excitement, or trust.
8
Email Spoofing Analysis
Checks for signs that the sender's identity has been faked or manipulated.

Risk Scoring System

The Scam Message Scanner combines all detection layers into a 0-100 risk score:

Risk Level Score Meaning
Safe 0-14 Appears legitimate
Low 15-29 Minor suspicious elements
Medium 30-49 Multiple warning signs
High 50-74 Strong scam indicators
Critical 75-100 Very likely a scam

How the Machine Learning Model Fits In

The eight detection layers above are features — the raw signals extracted from a message. They are not the verdict on their own. The Scam Message Scanner feeds those features, plus seven more (sender-domain reputation, attachment hints, link-to-text ratio, suspicious-TLD count, capitalisation anomalies, financial-amount mentions, and brand-mismatch flags), into a small two-layer neural network trained on roughly 5,200 labelled examples of known scams and known-legitimate messages.

The network was trained for 100 epochs using the Adam optimiser, with a held-out test set producing 94% accuracy, 92% precision, 96% recall, and a ROC-AUC of 0.98. In plain language: when the model says a message is a scam, it is right roughly 92% of the time, and when a message really is a scam the model catches it 96% of the time. We deliberately tuned for high recall so that obvious campaigns are not missed, even at the cost of occasional false positives on borderline-legitimate marketing emails.

The model runs entirely in your browser — about 18 KB of weights are loaded once and cached. Inference takes 2–3 milliseconds per message on a modern phone or laptop. Nothing is uploaded; if you want to confirm this, open your browser's network tab while you run a check and you'll see no requests fired during analysis.

Why a Score, Not a Yes/No

The 0–100 risk band exists because real-world messages sit on a spectrum. A genuinely transactional email from your bank usually scores 0–10. A delivery notification from a courier you do use might score 15–25 because it contains a tracking link. A "your account has been suspended" email impersonating a major brand will typically score 60–90, depending on how well-crafted it is. The score is meant to be informative — a 35 isn't a scam alarm, it's a flag that two or three suspicious patterns combined and you should look more carefully before clicking anything.

If you ever see a score that surprises you — a high score on a message you're confident is real, or a low score on something that turned out to be a scam — please report it through the report page. Those misclassifications are the single most useful training data we get.

Privacy & Security

The Scam Message Scanner is designed with privacy as a core principle. All analysis happens locally in your browser — no messages are sent to any server, no account is needed, and no data is collected or stored.

Limitations & Disclaimers

While the Scam Message Scanner is effective, no detection system is 100% accurate. Sophisticated scams may evade detection, and legitimate messages can occasionally trigger warnings. Always use your own judgement and, if unsure, contact the organisation directly through verified channels.