Fraud Detection & Prevention
Real-time fraud scoring achieving 0.1-0.3% fraud rate vs 1-2% with rules, blocking 95%+ fraud while approving 98%+ legitimate transactions.
Why This Matters
What It Is
Real-time fraud scoring achieving 0.1-0.3% fraud rate vs 1-2% with rules, blocking 95%+ fraud while approving 98%+ legitimate transactions.
Current State vs Future State Comparison
Current State
(Traditional)- Static fraud rules check (velocity limits, AVS mismatch, high-risk countries).
- All flagged transactions automatically declined.
3. 15-25% false positive rate (legitimate customers declined). 4. 1-2% fraud rate slips through rules. 5. Manual review queue for borderline cases creates 24-48 hour delays.
Characteristics
- • Payment Gateways (e.g., PayPal, Stripe)
- • Fraud Screening Platforms (e.g., Kount, Riskified)
- • Tokenization and Encryption Systems
- • Manual Dashboards (e.g., custom monitoring interfaces)
- • Spreadsheets (e.g., Excel for reporting)
- • Email for communication and documentation
- • ERP Systems (e.g., SAP, Oracle)
Pain Points
- ⚠ Siloed systems leading to blind spots and inconsistent enforcement of fraud rules.
- ⚠ High false positive rates resulting in unnecessary declines of legitimate transactions.
- ⚠ Slow response times due to fragmented processes and lack of real-time decision-making.
- ⚠ Documentation burden for compliance and audit requirements.
- ⚠ Integration complexity when adding new fraud detection tools or payment providers.
- ⚠ Scalability constraints as transaction volumes grow or companies expand into new markets.
- ⚠ Manual labor intensity requiring significant human effort for monitoring and reporting.
- ⚠ Regulatory risk due to inconsistent compliance across different payment channels.
Future State
(Agentic)1. Real-Time Fraud Agent analyzes 100+ behavioral and transactional signals. 2. ML Scoring Agent assigns fraud probability score (0-100).
- Contextual Analysis Agent evaluates customer history and device fingerprint.
- Risk-Based Decision Agent approves, declines, or steps up authentication.
- Adaptive Learning Agent updates models based on confirmed fraud.
Characteristics
- • Real-time transaction data
- • Historical patterns and analytics
- • Customer profiles and behavior
- • External data signals
- • ML model predictions
Benefits
- ✓ 40-95% improvement in key metrics
- ✓ 80-95% automation of manual tasks
- ✓ Real-time vs batch processing
- ✓ Continuous learning and optimization
- ✓ Reduced labor costs by 60-80%
Is This Right for You?
This score is based on general applicability (industry fit, implementation complexity, and ROI potential). Use the Preferences button above to set your industry, role, and company profile for personalized matching.
Why this score:
- • Applicable across multiple industries
- • Moderate expected business value
- • Time to value: 3-6 months
- • (Score based on general applicability - set preferences for personalized matching)
You might benefit from Fraud Detection & Prevention if:
- You're experiencing: Siloed systems leading to blind spots and inconsistent enforcement of fraud rules.
- You're experiencing: High false positive rates resulting in unnecessary declines of legitimate transactions.
- You're experiencing: Slow response times due to fragmented processes and lack of real-time decision-making.
This may not be right for you if:
- Requires human oversight for critical decision points - not fully autonomous
Parent Capability
Shrink & Loss Prevention
Reduces inventory loss through AI video analytics, real-time exception detection, and continuous monitoring achieving significantly lower shrink rates.
What to Do Next
Related Functions
Metadata
- Function ID
- function-fraud-detection-prevention