Machine learning API for real-time payment fraud prevention targeting SMB eCommerce. Reduces fraud by 42% with lightweight integration and behavioral analytics. Tiered SaaS pricing starting at €29/month.
PayGuard delivers enterprise-grade fraud protection to small merchants through an API-first solution. By analyzing transaction patterns in real-time with machine learning, it identifies suspicious payments before processing. Designed specifically for resource-constrained SMBs, the platform requires zero infrastructure management while reducing payment fraud by an average 42% based on pilot data.
Core functionality
PayGuard processes transaction data through machine learning algorithms to detect anomalies in real-time. The API assigns risk scores to payments, flags suspicious activities, and integrates via lightweight SDKs. Merchants access a dashboard for visualizing fraud patterns and customizing rules without infrastructure overhead.
Target user and segment
Primary users are operations managers at small-to-medium eCommerce businesses (1-50 employees) processing 500-10k monthly transactions. Initial focus on fashion, electronics, and digital goods verticals where fraud rates are highest.
Recommended tech stack
- ML Engine: Python (Scikit-learn/TensorFlow) with Redis for scoring
- API Layer: Node.js + Express.js (JWT authentication)
- Data Pipeline: Apache Kafka + Spark Streaming
- Storage: PostgreSQL + Amazon S3
- Cloud: AWS serverless architecture
- Frontend: React.js dashboard
Estimated MVP hours and costs
Total development: 480 hours at €100/hour = €48,000
Breakdown:
- ML model training: 120h
- API core: 80h
- Real-time scoring: 100h
- Dashboard UI: 60h
- Payment integrations: 40h
- Security audit: 30h
- Testing/deployment: 50h
SWOT-analysis
- Strengths: Zero-config ML detection, 200ms latency SLA, 42% fraud reduction
- Weaknesses: Limited historical data for new merchants, PCI compliance complexity
- Opportunities: Shopify/WooCommerce partnerships, emerging market growth
- Threats: Enterprise players moving downstream, regulatory changes
First 1000 customers strategy
Acquisition channels:
- Marketplace integrations (Shopify App Store)
- Performance marketing at €20-30 CPA
- ‘Fraud Prevention Score’ diagnostic tool
- Co-marketing with payment processors
Conversion metrics:
- 7% free-to-paid conversion
- €225 customer acquisition cost
- €225,000 total acquisition budget
Monetization
Pricing: Tiered SaaS – Free (500 tx/mo), €29 (2,500 tx), €99 (10k tx)
Breakeven: 700 paying customers at €29 tier
Projected revenue: €340,000 at 1,200 customers (12 months)
Core team: 1 ML engineer, 2 full-stack devs, 1 fraud analyst, 0.5 customer success
Market positioning and competitors
Operating in €2.1B EU SMB fraud prevention market (14% CAGR). Key competitors include Signifyd (enterprise focus) and FraudLabs Pro (legacy solutions). Differentiation through pre-trained vertical models and no per-transaction fees. Targeting micro-niches like electronics retailers and fashion boutiques with product-led growth strategy.
