FraudShieldNG

Real-time fraud detection for Nigerian transactions – rules + anomalies.

Score: 8.2/10NigeriaMedium BuildReady to Spawn
Brand Colors

The Opportunity

Problem

Nigerian entrepreneurs face a severe shortage of skilled RegTech professionals, preventing them from developing and maintaining advanced AML and fraud detection systems.

Solution

FraudShieldNG monitors transaction streams with Nigeria-tuned rules for unusual patterns like BVN mismatches or geo-fencing. It flags suspicious activities instantly via API/webhook, helping fintechs block fraud without data scientists. Dashboards show fraud rates and prevention stats for compliance reporting.

Target Audience

Nigerian entrepreneurs in fintech, banking, or compliance-heavy startups needing AML and fraud systems

Differentiator

Pre-built rules for NG payment schemes (NIP, POS fraud) + lightweight anomaly detection, solo-dev friendly.

Brand Voice

supportive

Features

Transaction Ingestion

must-have20h

API to log transactions with amount, BVN, IP, device.

Rule Engine

must-have18h

Configurable rules e.g., velocity checks, blacklists.

Anomaly Detection

must-have25h

Simple ML (isolation forest) for outlier txns.

Real-time Alerts

must-have12h

Webhook/email for fraud scores > threshold.

Fraud Dashboard

must-have15h

Charts of blocked/approved txns, loss prevented.

Review Queue

nice-to-have10h

Manual review for flagged txns.

Export Logs

nice-to-have5h

CSV of all decisions.

Device Fingerprint

future20h

Track repeat fraud devices.

Total Build Time: 125 hours

Database Schema

users

ColumnTypeNullable
iduuidNo
emailtextNo

organizations

ColumnTypeNullable
iduuidNo
user_iduuidNo
api_keytextNo

Relationships:

  • user_id -> users(id)

transactions

ColumnTypeNullable
iduuidNo
org_iduuidNo
amountintNo
bvntextYes
iptextYes
fraud_scoreintYes
decisiontextYes
created_attimestampNo

Relationships:

  • org_id -> organizations(id)

rules

ColumnTypeNullable
iduuidNo
org_iduuidNo
nametextNo
conditionsjsonbNo

Relationships:

  • org_id -> organizations(id)

API Endpoints

POST
/api/txns

Log transaction for scoring

🔒 Auth Required
GET
/api/txns

Fetch org txns

🔒 Auth Required
POST
/api/rules

Create custom rule

🔒 Auth Required

Tech Stack

Frontend
Next.js 14 + Tailwind + shadcn/ui
Backend
Next.js API + Supabase Edge Functions
Database
Supabase Postgres
Auth
Supabase Auth
Payments
Stripe
Hosting
Vercel
Additional Tools
Vercel AI SDK for anomalies

Build Timeline

Week 1: Setup and auth

35h
  • DB schema
  • API key gen

Week 2: Txn ingestion + rules

40h
  • Basic scoring
  • Rule UI

Week 3: Anomalies + alerts

40h
  • ML model
  • Webhooks

Week 4: Dashboard + payments

35h
  • Charts
  • Stripe

Week 5: Testing + polish

25h
  • Load tests
  • UI fixes
Total Timeline: 5 weeks • 195 hours

Pricing Tiers

Free

$0/mo

Basic rules

  • 1k txns/mo

Pro

$25/mo
  • 10k txns
  • Anomalies
  • Alerts

Enterprise

$149/mo
  • Unlimited
  • Custom ML
  • SLA

Revenue Projections

MonthUsersConversionMRRARR
Month 1803%$60$720
Month 64006%$576$6,912

Unit Economics

$35
CAC
$400
LTV
4%
Churn
88%
Margin
LTV:CAC Ratio: 11.4xExcellent!

Landing Page Copy

Stop Nigerian Transaction Fraud Instantly

AI rules catch BVN swaps & velocity fraud – integrate in minutes, $25/mo.

Feature Highlights

Real-time scoring
NG-specific rules
Loss prevention dashboard
Webhook alerts

Social Proof (Placeholders)

"'Cut fraud 40% day 1.' – Payments Startup"
"'Easy API for our app.' – Fintech Dev"

First Three Customers

Join Nigerian Payments Forum on WhatsApp, offer free Pro for 1 week to 15 members with txn logs. Email cold to Opay/Moniepoint clones from LinkedIn. Demo at Lagos Fintech Meetup.

Launch Channels

Product Huntr/fintechTwitter NG FintechHacker News

SEO Keywords

fraud detection Nigeriatransaction monitoring software NGBVN fraud preventionfintech fraud tool Nigeria

Competitive Analysis

Usage-based $0.10/txn
Strength

Advanced ML

Weakness

Not tuned for Africa, high cost

Our Advantage

NG rules + flat pricing

🏰 Moat Strategy

Transaction data moat for better anomaly models over time.

⏰ Why Now?

2024 NIBSS fraud reports up 50%, fintechs scaling transactions.

Risks & Mitigation

technicalmedium severity

High txn volume overload

Mitigation

Queueing + edge functions

legalhigh severity

Data privacy (NDPR)

Mitigation

Anon data, consent flows

Validation Roadmap

pre-build5 days

Survey 15 fintechs on fraud losses

Success: Avg $5k/mo loss

mvp21 days

Beta with synthetic data

Success: 95% accuracy

Pivot Options

  • POS terminal monitoring
  • Expand to merchant fraud
  • KYT for ongoing monitoring

Quick Stats

Build Time
195h
Target MRR (6 mo)
$800
Market Size
$75.0M
Features
8
Database Tables
4
API Endpoints
3