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Key Objective

FraudSlash’s advanced risk scoring engine combines AI, machine learning, and rule-based logic to accurately detect fraud—while reducing false positives and manual reviews.

Our proprietary FraudBlend Score™ analyzes 100+ data points in real-time, including:
✅ Behavioral biometrics (device, typing patterns, session flow)
✅ Transaction context (location, velocity, purchase history)
✅ Network intelligence (proxy/VPN detection, linked fraud rings)
✅ Custom rule engine (tuned to your business’s risk tolerance)

Result: A single, actionable score (0-100) that tells you exactly when to block, review, or approve.

Continuous Optimization

Hybrid AI +
Human Expertise

Machine Learning Models

Self-learning algorithms adapt to new fraud patterns.

High Accuracy

Fewer false declines, more legitimate sales.

Advanced Rule Engine

Built from 15+ years of fraud investigation experience to catch edge cases.

Why FraudSlash’s Risk Scoring Wins

✔ Adaptive learning helps stays ahead of fraudsters.
✔ Higher approval rates stops fraud without hurting revenue.

FraudSlash

FAQs
Answered Right Here

Reach us at support@fraudslash.com if you want to dig deep into how we prevent false-declines and fraud

FraudSlash boosts your revenue by reducing false declines by up to 90% while catching real fraud. Our AI-powered risk scoring:
✅ Approves more good customers – Stop losing sales to overly aggressive fraud filters
✅ Slash operational costs – Cut manual review time by 60%+
✅ Adapts to new threats – Self-learning AI stays ahead of fraud tactics
✅ Protects your reputation – Fewer customer frustrations from wrongful declines
FraudSlash provides AI-powered fraud prevention solutions designed to protect your revenue while ensuring a seamless customer experience. Our key services include:

1. Real-Time Fraud Risk Scoring

  • Proprietary FraudBlend Score™ (0-100) for instant approve/decline decisions

  • Analyzes 100+ behavioral, transactional, and network signals

  • Accurately stops false-declines

2. Automated Decisioning

  • Millisecond responses – Zero checkout friction

  • Smart workflows – Auto-block fraud, flag suspicious, approve trusted

4. Chargeback Prevention

  • Predictive alerts for high-risk transactions

  • Evidence collection for dispute resolution

5. Industry-Specific Protection

  • Specialized models for e-commerce, fintech, marketplaces, crypto, and SaaS

6. Integrations & Support

  • Plug-and-play APIs for Shopify, WooCommerce, Magento, and custom platforms

  • Dedicated fraud analysts for onboarding and strategy

Outcome:
✔ Higher approval rates – Stop rejecting good customers
✔ Lower fraud losses – Block 95%+ of fraudulent transactions
✔ Reduced operational costs – Cut manual reviews by 60%

Getting started with FraudSlash is quick and seamless:

1. Choose Your Path

2. Integrate

  • Self-Service: Use our pre-built plugins (Shopify/WooCommerce) or REST API.

  • Guided Setup: Our engineers assist with custom integrations (typically <2 hours).

3. Go Live & Optimize

  • Test Mode: Validate scoring with historical transactions.

  • Launch: Tune settings in your backend, then flip to live protection.

Next Steps:
➡️ For Developers: API Docs
➡️ For Business Teams: Contact Us

FraudSlash leverages cutting-edge AI and machine learning to deliver precise, adaptive fraud detection:

1. Deep Learning Models

  • Behavioral Biometrics: Analyzes user interaction patterns (mouse movements, typing speed) to detect bot attacks or impersonation.

  • Anomaly Detection: Identifies outliers in transaction velocity, location shifts, or purchase history.

2. Supervised Machine Learning

  • Ensemble Models: Combines decision trees, neural networks, and gradient boosting for high-accuracy risk scoring.

  • Historical Training: Learns from your past transactions to reduce false positives.

3. Unsupervised Learning

  • Clustering Algorithms: Detects emerging fraud patterns (e.g., coordinated fraud rings) without labeled data.

4. Graph Network Analysis

  • Entity Linking: Maps connections between devices, emails, and payment methods to uncover synthetic identities.

5. Natural Language Processing (NLP)

  • Fraudulent Text Detection: Scans product reviews, support chats, and account notes for scam signals.

6. Explainable AI (XAI)

  • Transparent Decisions: Provides reason codes for every decline (e.g., "high-risk IP + mismatched billing address").

Why It Matters:
✔ Self-Learning: Improves accuracy with every transaction.
✔ Low False Positives: Balances fraud detection with revenue protection.
✔ Real-Time Adaptation: Updates models to counter new attack vectors.

Have Questions? Get in Touch!