ScamGuards Malaysia 🛡️

Check before you trust. — A production-grade, AI-powered scam prevention platform built for Malaysia.

ScamGuards is a community-driven fraud detection system that allows users to check identifiers (phone numbers, emails, bank accounts) against a crowdsourced database of scam reports. The platform uses AI to analyze patterns, detect duplicates, and provide confidence-based risk assessments.

🇲🇾 Malaysia-First — Localized for Malaysian phone formats, banks, e-wallets, and common local scam types.

📐 Architecture Overview

System Design

┌─────────────────────────────────────────────────────────────────────────────┐
│                              CLIENT LAYER                                    │
│  ┌─────────────┐  ┌─────────────┐  ┌─────────────┐  ┌─────────────────────┐ │
│  │   Search    │  │   Report    │  │   Dispute   │  │   Admin Dashboard   │ │
│  │    Page     │  │ Submission  │  │    Form     │  │   (Email Auth)      │ │
│  └──────┬──────┘  └──────┬──────┘  └──────┬──────┘  └──────────┬──────────┘ │
└─────────┼────────────────┼────────────────┼────────────────────┼────────────┘
          │                │                │                    │
          ▼                ▼                ▼                    ▼
┌─────────────────────────────────────────────────────────────────────────────┐
│                           MIDDLEWARE LAYER                                   │
│  ┌──────────────────────────────────────────────────────────────────────┐   │
│  │                    Rate Limiting & Abuse Prevention                   │   │
│  │  • IP-based cooldowns (60s between reports)                          │   │
│  │  • Auto-ban after threshold (20 submissions → 24hr ban)              │   │
│  │  • In-memory store for Edge Runtime compatibility                    │   │
│  └──────────────────────────────────────────────────────────────────────┘   │
└─────────────────────────────────────────────────────────────────────────────┘
          │
          ▼
┌─────────────────────────────────────────────────────────────────────────────┐
│                              API LAYER                                       │
│  ┌────────────┐  ┌────────────┐  ┌────────────┐  ┌────────────────────────┐ │
│  │  /search   │  │  /submit   │  │  /dispute  │  │  /analyze-report       │ │
│  │            │  │            │  │            │  │  (Multi-Scammer AI)    │ │
│  └─────┬──────┘  └─────┬──────┘  └─────┬──────┘  └───────────┬────────────┘ │
│        │               │               │                     │              │
│        │               ▼               │                     │              │
│        │    ┌──────────────────┐       │                     │              │
│        │    │ Duplicate Check  │       │                     │              │
│        │    │ & Smart Merge    │       │                     │              │
│        │    └────────┬─────────┘       │                     │              │
└────────┼─────────────┼─────────────────┼─────────────────────┼──────────────┘
         │             │                 │                     │
         ▼             ▼                 ▼                     ▼
┌─────────────────────────────────────────────────────────────────────────────┐
│                            DATA LAYER (Supabase)                             │
│  ┌─────────────────────────────────────────────────────────────────────┐    │
│  │                        PostgreSQL + RLS                              │    │
│  │  ┌─────────┐ ┌─────────────┐ ┌─────────┐ ┌────────────────────────┐ │    │
│  │  │ reports │ │ data_points │ │disputes │ │ reporter_reputation    │ │    │
│  │  └────┬────┘ └──────┬──────┘ └────┬────┘ └────────────┬───────────┘ │    │
│  │       │             │             │                   │             │    │
│  │       └─────────────┴─────────────┴───────────────────┘             │    │
│  │                              │                                       │    │
│  │  ┌───────────────────────────┴───────────────────────────────────┐  │    │
│  │  │              Materialized Views (Pre-computed)                 │  │    │
│  │  │  • platform_stats    • scam_type_stats   • daily_stats        │  │    │
│  │  │  • scammer_search_stats (confidence + heat level)             │  │    │
│  │  └───────────────────────────────────────────────────────────────┘  │    │
│  └─────────────────────────────────────────────────────────────────────┘    │
│                                                                              │
│  ┌─────────────────────────────────────────────────────────────────────┐    │
│  │                    Supabase Storage (evidence)                       │    │
│  └─────────────────────────────────────────────────────────────────────┘    │
└─────────────────────────────────────────────────────────────────────────────┘
         │
         ▼
┌─────────────────────────────────────────────────────────────────────────────┐
│                           AI LAYER (Qwen via DashScope)                      │
│  ┌──────────────────────────┐  ┌────────────────────────────────────────┐   │
│  │    Search Detective      │  │         Report Analyst                  │   │
│  │  • Data point extraction │  │  • Multi-scammer detection              │   │
│  │  • Smart Paste for search│  │  • Grouped preview with user confirm    │   │
│  │  • Type classification   │  │  • Risk scoring & scam type inference   │   │
│  └──────────────────────────┘  └────────────────────────────────────────┘   │
└─────────────────────────────────────────────────────────────────────────────┘

🚀 Evolution: From MVP to Production

This project demonstrates iterative architectural improvement, evolving from a basic MVP to a production-grade system.

Phase 1: MVP Foundation

Goal: Functional prototype with core search/report capabilities.

Component	Implementation	Status
Database	Basic tables (reports, data_points, disputes)	✅
Search	Exact match only	✅
AI	Single prompt for risk scoring	✅
Security	None	⚠️
Admin	None	❌

Phase 2: Production Hardening

Goal: Add search intelligence, security, and abuse prevention.

Component	Improvement	Impact
Search	Fuzzy matching via `pg_trgm` + full-text search	3x more matches
Security	Row Level Security (RLS) on all tables	Data isolation
Analytics	Materialized views for platform stats	100x faster queries
Abuse Prevention	IP-based rate limiting in middleware	Spam blocked
Admin	Secure email/password auth with whitelist	Controlled access
Functions	`SECURITY DEFINER SET search_path = ''`	SQL injection prevention

Phase 3: Intelligence Layer

Goal: AI-powered features and unified scammer profiling.

Component	Innovation	Impact
Smart Paste	AI extracts data points from pasted paragraphs	80% faster input
Multi-Scammer Detection	AI identifies multiple scammers in single narrative	Batch processing
Duplicate Detection	Smart merge with report count tracking	Data deduplication
Confidence Scoring	`confidence = 50 + (report_count * 10)`	Trust signals
Heat Levels	CRITICAL/HIGH/MEDIUM/LOW based on reports	Priority triage
Scammer Profiles	Unified view aggregating all data points	Entity resolution

🏗️ Database Schema Evolution

Initial Schema (Migration 001)

-- Basic normalized structure
reports (id, scam_type, description, platform, evidence_url)
data_points (report_id, type, value, normalized_value)
disputes (report_id, reason, contact_email, status)
audit_logs (action, ip_hash, metadata)

Production Schema (Migration 002-003)

-- Added for performance & security
+ reports.reporter_hash          -- Anonymous tracking
+ reports.amount_lost            -- Financial impact
+ reports.description_tsv        -- Full-text search vector
+ rate_limits                    -- Abuse prevention
+ moderation_queue               -- Auto-flagging
+ reporter_reputation            -- Trust scoring
+ Materialized Views             -- Pre-computed analytics

Intelligence Schema (Migration 004)

-- Added for duplicate detection & profiling
+ data_points.report_count       -- How many times reported
+ data_points.first_reported_at  -- Temporal tracking
+ data_points.last_reported_at   -- Recent activity
+ data_points.confidence_score   -- Calculated trust
+ report_submissions             -- Per-datapoint rate limiting
+ scammer_profiles (VIEW)        -- Aggregated entity view
+ scammer_search_stats (MATVIEW) -- Pre-computed search enhancement

Confidence & Heat Level Algorithm

Confidence Score = min(100, 50 + (unique_reports × 10))

Heat Level:
  CRITICAL = 10+ reports (100% confidence)
  HIGH     = 5-9 reports (90-99% confidence)  
  MEDIUM   = 3-4 reports (70-89% confidence)
  LOW      = 1-2 reports (50-69% confidence)

🔐 Security Architecture

Defense in Depth

Layer 1: Middleware (Edge)
├── IP-based rate limiting
├── Submission cooldowns (60s)
├── Auto-ban thresholds (20 → 24hr ban)
└── Request validation

Layer 2: API Routes
├── Input sanitization
├── Type validation (Zod)
└── Error boundary handling

Layer 3: Database (Supabase)
├── Row Level Security (RLS)
├── Function search_path hardening
├── Prepared statements (no SQL injection)
└── Audit logging

Layer 4: Admin Access
├── Supabase Auth (email/password)
├── Environment-based whitelist
└── Session management

Security Decisions

Concern	Decision	Rationale
Authentication	Public submit, admin-only verify	Balance accessibility with control
Rate Limiting	In-memory (Edge compatible)	Vercel Edge Runtime constraint
IP Tracking	SHA-256 hash, not raw IP	PDPA compliance
Admin Auth	Email whitelist + Supabase Auth	Simple, secure, auditable
SQL Injection	`SET search_path = ''` on all functions	Supabase linter compliance

🤖 AI Architecture

Dual-Persona Design

The system uses two specialized AI personas optimized for different tasks:

1. Search Detective (Data Extraction)

Input:  "got scammed by john at 0123456789 on telegram @scammer123"
Output: [
  { type: "name", value: "john", confidence: 85 },
  { type: "phone", value: "0123456789", confidence: 95 },
  { type: "telegram", value: "@scammer123", confidence: 90 }
]

2. Report Analyst (Multi-Scammer Detection)

Input:  Paragraph describing scam with multiple perpetrators
Output: {
  isMultiple: true,
  scammers: [
    { name: "Scammer A", dataPoints: [...], riskScore: 85 },
    { name: "Scammer B", dataPoints: [...], riskScore: 78 }
  ]
}

Smart Hybrid Workflow

User pastes scam story
        │
        ▼
┌───────────────────┐
│   AI Analysis     │
│  (Qwen qwen-max)  │
└────────┬──────────┘
         │
    ┌────┴────┐
    ▼         ▼
Single    Multiple
Scammer   Scammers
    │         │
    ▼         ▼
Standard  Grouped
  Form    Preview
    │         │
    ▼         ▼
 Submit   Select &
          Confirm
             │
             ▼
        Batch Submit
        (N reports)

📊 Performance Optimizations

Optimization	Implementation	Improvement
Fuzzy Search	`pg_trgm` GIN indexes	Sub-100ms on 100K records
Full-Text Search	`tsvector` with GIN	Semantic matching
Pre-computed Stats	Materialized views	100x faster dashboard
Composite Indexes	`(status, created_at DESC)`	Optimized common queries
Connection Pooling	Supabase built-in	Handles concurrent load

🇲🇾 Localization

Malaysia-Specific Features

Phone Validation: 01X-XXXXXXX format with carrier detection
Banks: Maybank, CIMB, Public Bank, RHB, Hong Leong, etc.
E-Wallets: Touch 'n Go, GrabPay, Boost, ShopeePay
Scam Types: Macau, Love, Parcel, Job, Investment, Loan, Collectibles (TCG)
Currency: MYR with RM formatting
Languages: English + Bahasa Malaysia with browser auto-translate hints

🛠️ Tech Stack

Layer	Technology	Why
Framework	Next.js 14 (App Router)	Server components, edge-ready
Language	TypeScript	Type safety, better DX
Styling	Tailwind CSS + shadcn/ui	Rapid, consistent UI
Database	Supabase (PostgreSQL)	RLS, real-time, storage
AI	Qwen via DashScope	Cost-effective, fast inference
Deployment	Vercel	Edge functions, auto-scaling
Auth	Supabase Auth	Built-in, secure

📁 Project Structure

scamguard/
├── app/
│   ├── api/
│   │   ├── search/           # Fuzzy + exact + full-text search
│   │   ├── submit/           # Report submission with duplicate detection
│   │   ├── dispute/          # Challenge reports
│   │   ├── extract/          # AI data point extraction
│   │   ├── analyze-report/   # Multi-scammer AI analysis
│   │   └── admin/            # Protected admin endpoints
│   ├── admin/
│   │   ├── login/            # Email/password auth
│   │   └── dashboard/        # Report management
│   ├── search/               # Search interface
│   ├── submit/               # Smart Report paste
│   └── results/              # Search results display
├── components/
│   ├── ui/                   # shadcn/ui components
│   ├── search/               # SmartSearchPaste
│   └── submit/               # SmartReportPaste (multi-scammer)
├── lib/
│   ├── ai/
│   │   ├── scam-analyzer.ts  # Search extraction
│   │   └── report-analyzer.ts# Multi-scammer detection
│   ├── supabase/             # Client (browser + server)
│   └── utils/                # Normalization, validation
├── middleware.ts             # Rate limiting, abuse prevention
└── supabase/
    └── migrations/
        ├── 001_initial_schema.sql
        ├── 002_production_upgrade.sql
        ├── 003_production_10_of_10.sql
        └── 004_duplicate_detection.sql  # Latest

🚀 Getting Started

Prerequisites

Node.js 18+
Supabase account
DashScope API key (Alibaba Cloud)

Quick Start

# Clone
git clone https://github.com/nicuk/scamguards.git
cd scamguards

# Install
npm install

# Configure
cp .env.example .env.local
# Edit .env.local with your keys

# Database setup (in Supabase SQL Editor)
# Run: supabase/FULL_SCHEMA.sql
# Then: supabase/migrations/004_duplicate_detection.sql

# Create storage bucket: "evidence" (public)

# Run
npm run dev

Environment Variables

Variable	Description
`NEXT_PUBLIC_SUPABASE_URL`	Supabase project URL
`NEXT_PUBLIC_SUPABASE_ANON_KEY`	Supabase anon key
`DASHSCOPE_API_KEY`	Alibaba Cloud DashScope key
`ADMIN_EMAILS`	Comma-separated admin emails

📈 Roadmap

Real-time notifications for new reports matching saved searches
Batch report verification for admins
Public API for third-party integrations
Mobile app (React Native)
ML-based scam pattern prediction

📄 License

Elastic License 2.0 — Free to use, modify, and self-host. Commercial SaaS requires separate license.

🙏 Acknowledgments

Built with modern best practices for security, performance, and user experience. Contributions welcome.

Protecting Malaysians from scams, one check at a time.

Name		Name	Last commit message	Last commit date
Latest commit History 22 Commits
app		app
components		components
lib		lib
public		public
supabase		supabase
.env.example		.env.example
.gitignore		.gitignore
Context.md		Context.md
LICENSE		LICENSE
PROJECT_PLAN.md		PROJECT_PLAN.md
README.md		README.md
middleware.ts		middleware.ts
next.config.mjs		next.config.mjs
next.config.ts		next.config.ts
package-lock.json		package-lock.json
package.json		package.json
postcss.config.mjs		postcss.config.mjs
tailwind.config.ts		tailwind.config.ts
tsconfig.json		tsconfig.json

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ScamGuards Malaysia 🛡️

📐 Architecture Overview

System Design

🚀 Evolution: From MVP to Production

Phase 1: MVP Foundation

Phase 2: Production Hardening

Phase 3: Intelligence Layer

🏗️ Database Schema Evolution

Initial Schema (Migration 001)

Production Schema (Migration 002-003)

Intelligence Schema (Migration 004)

Confidence & Heat Level Algorithm

🔐 Security Architecture

Defense in Depth

Security Decisions

🤖 AI Architecture

Dual-Persona Design

1. Search Detective (Data Extraction)

2. Report Analyst (Multi-Scammer Detection)

Smart Hybrid Workflow

📊 Performance Optimizations

🇲🇾 Localization

Malaysia-Specific Features

🛠️ Tech Stack

📁 Project Structure

🚀 Getting Started

Prerequisites

Quick Start

Environment Variables

📈 Roadmap

📄 License

🙏 Acknowledgments

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