Scam Checker
Solo design, build, and ongoing operation
Scam Checker started from a pattern I kept seeing at work: the rise in phishing and scam SMS targeting Australians, and how few tools explain *why* a message is suspicious rather than just giving a verdict. I designed, built, and shipped the entire product — from the scam-indicator research through the scoring engine to the live site at scamchecker.app.
The core design constraint is privacy: every analysis runs in the browser. Text, emails, URLs, images (via OCR), and uploaded files are processed client-side, so nothing a user pastes into the tool ever leaves their machine. The detection engine works off a hand-compiled indicator library — urgency phrases, suspicious URL shapes, impersonation patterns, financial pressure tactics, and social-engineering markers — and a weighted scoring system that surfaces a confidence score alongside a per-signal breakdown. The goal is educational as much as protective: users learn what to look for, not just whether to trust one message.
Built With
Highlights
- 100% client-side analysis — text, email, URL, image, and file checks run in the browser with zero server uploads and zero tracking.
- Weighted multi-signal risk scoring with a per-signal breakdown, built on a curated library of urgency phrases, URL shapes, impersonation patterns, and social-engineering markers.
- Multi-input pipeline: dedicated analysis paths for raw text, emails, links, OCR-scanned images, and uploaded documents (PDF, DOCX, TXT).
- Grew into a destination site: custom domain (scamchecker.app), scam-awareness guide library, FAQ schema, and a directory of international scam-reporting bodies.
- Organic growth driven by SEO content strategy targeting high-intent search queries, monitored via analytics and expanded based on top queries.
Security Considerations
- Privacy by design — no payloads ever leave the client, removing the data-handling and retention risk entirely.
- Client-side rate limiting and file-size limits guard the OCR and file-scanning paths against abuse and browser lockups.
- Indicator library modelled on real-world phishing and smishing campaigns observed targeting Australian users.
Project Timeline
Problem Identification
Mar 2024Tracked the rise in phishing and scam SMS targeting Australians. Mapped common scam patterns, red flags, and the gaps in existing detection tools.
Scam Pattern Database
Apr 2024Compiled an indicator library: urgency phrases, suspicious URL shapes, impersonation patterns, financial pressure tactics, and social-engineering markers.
Scoring Algorithm
May 2024Designed a weighted scoring system that surfaces a confidence score with a per-signal breakdown so users learn what to look for, not just whether to trust a message.
Core Checker Interface
Jun 2024Built the main checker with text input, real-time analysis, and visual risk indicators. All processing runs in the browser to preserve user privacy.
Multi-Input Support
Aug 2024Extended the checker to handle emails and URLs, with dedicated pages for each input type and tailored analysis rules.
Image & File Scanning
Sep 2024Added OCR-based image scanning and file upload support (PDF, DOCX, TXT) with size limits and client-side rate limiting.
SEO & Content Strategy
Nov 2024Wrote scam-awareness guides and how-to articles. Implemented FAQ schema and optimised pages for high-intent search queries.
Custom Domain & Branding
Dec 2024Registered scamchecker.app and rebranded from "Is It a Scam?" for cleaner search visibility. Full meta-tag and OG-image overhaul.
Global Reporting Directory
Jan 2025Added a directory of international reporting bodies and a contact/disclaimer page for compliance.
Analytics & Growth
Feb 2025Wired up Google Analytics, monitored organic-traffic growth, and expanded the educational guide library based on top search queries.