Why Structured Validation Logic Design Matters
Building effective validation logic requires more than just configuring thresholds. The most successful implementations combine visual design tools with structured decision-making frameworks to create clear, maintainable, and auditable workflows.
This guide provides practical methodologies, tools, and frameworks for designing validation logic that balances fraud protection with operational efficiency while ensuring stakeholder alignment and compliance requirements.
Key Benefits of Structured Approach
Stakeholder Alignment: Visual tools enable non-technical teams to understand and validate logic
Audit Compliance: Clear decision paths provide transparent audit trails
Maintenance Efficiency: Well-structured logic is easier to update and optimize
Risk Management: Systematic approach reduces gaps and edge case failures
Decision Trees: The Foundation
Why Decision Trees Work Best for Validation Logic
Visual Clarity: Every possible fraud scenario and outcome is clearly mapped
Stakeholder Communication: Non-technical team members can easily understand and validate logic
Implementation Translation: Direct conversion to code or Business Rules
Audit Documentation: Complete decision path documentation for compliance
Gap Identification: Visual representation reveals missing scenarios and edge cases
Basic Decision Tree Structure
Root: Document Submitted
├── Document Classification Check
│ ├── AI Generated?
│ │ ├── YES → Tag: "reject"
│ │ └── NO → Continue
│ ├── Not a Document?
│ │ ├── YES → Tag: "reject"
│ │ └── NO → Continue
│ └── Screenshot/LCD?
│ ├── YES → Tag: "screen_capture"
│ └── NO → Continue to Content Analysis
├── Content Analysis
│ ├── Handwriting Detected?
│ │ ├── YES: Which fields?
│ │ │ ├── Total/Subtotal → Tag: "financial_modification"
│ │ │ ├── Date Only → Tag: "date_alteration"
│ │ │ ├── Line Items → Tag: "product_modification"
│ │ │ └── Multiple Fields → Tag: "extensive_handwriting"
│ │ └── NO → Continue
│ ├── Digital Tampering?
│ │ ├── YES: Which fields?
│ │ │ ├── Total → Tag: "digital_fraud"
│ │ │ └── Date Only → Tag: "date_alteration"
│ │ │ ├── Line Items → Tag: "product_modification"
│ │ └── NO → Continue
│ └── Similarity Analysis
│ ├── Score >0.95 → Tag: "duplicate"
│ ├── Score 0.8-0.95 → Tag: "similar_submission"
│ └── NO → Continue
└── Final Classification
├── High Risk Tags → Action: "reject"
├── Medium Risk Tags → Action: "manual_review"
└── Low Risk Tags → Action: "approve"
Visual Design Tools and Platforms
While Veryfi Workflows Design tools are being developed, we recommend using any open-source option like Draw.io, Lucidchart, MS Visio, Miro/Mural.
Each Rule you create in Veryfi can be exported in JSON it means that you can visualize and build decision-making trees in many tools, including LLMs, to prove the design and outcome.
Structured Approaches for Complex Logic
Risk Scoring Matrix Method (another approach)
Create a systematic point-based evaluation:
Fraud Signal Scoring:
┌─────────────────────────────┬─────────┬──────────────────────┐
│ Fraud Signal │ Points │ Confidence Level │
├─────────────────────────────┼─────────┼──────────────────────┤
│ AI Generated Document │ 60 │ High │
│ Handwritten Total/Subtotal │ 50 │ High │
│ Digital Tampering (High) │ 45 │ High │
│ Similarity Score >0.95 │ 40 │ Medium │
│ Digital Tampering (Medium) │ 30 │ Medium │
│ Handwritten Line Items │ 25 │ Medium │
│ LCD Photo │ 20 │ Low │
│ Similarity Score 0.8-0.95 │ 15 │ Low │
│ Handwritten Date Only │ 10 │ Low │
└─────────────────────────────┴─────────┴──────────────────────┘
Risk Level Thresholds:
- 0-15 points: Green (Auto-approve)
- 16-40 points: Yellow (Manual review)
- 41-60 points: Orange (Supervisor review)
- 61+ points: Red (Reject/Escalate)
Advantages:
Quantifiable risk assessment
Easy to adjust individual signal weights
Clear escalation thresholds
Audit-friendly scoring rationale
Implementation in Business Rules:
score = 0;
if (ai_generated_detected) score += 60;
if (handwritten_fields.includes("total")) score += 50;
if (digital_tampering_confidence === "high") score += 45;
if (similarity_score > 0.95) score += 40;
if (score >= 61) tag = "reject";
else if (score >= 41) tag = "supervisor_review";
else if (score >= 16) tag = "manual_review";
else tag = "approve";
Multi-Dimensional Analysis Framework
Consider fraud signals across multiple dimensions:
Dimension 1: Technical Fraud Indicators
Document authenticity (AI, tampering, etc.)
Visual anomalies and artifacts
Metadata inconsistencies
Dimension 2: Behavioral Patterns
Submission timing and frequency
Device and location patterns
Historical fraud indicators
Dimension 3: Business Context
Campaign rules and restrictions
Merchant/vendor patterns
Amount and category thresholds
Great Topics to discuss internally with your team when building Fraud Framework:
For each fraud signal Veryfi Returns:
• Define detection conditions
• Map all possible outcomes
• Consider signal combinations
• Document exception handling
• What happens when multiple signals trigger?
• How to handle conflicting indicators?
• Define fallback rules for undefined scenarios
Best Practice: Implementation Frameworks
Iterative Development Approach
MVP: Minimum Viable Protection
Objective: Deploy basic fraud protection immediately while building advanced logic
Priority 1 Rules (Deploy Immediately):
• AI-generated documents → Reject
• Digital tampering → Reject
• Perfect similarity matches (>0.98) → Reject
• Clear non-documents → Reject
• Everything else → Manual review queue
Benefits:
Immediate fraud protection
Simple implementation
Low false positive risk
Baseline performance measurement
Success Metrics:
Fraud detection rate >70% of known fraud types
False positive rate <5-10%
Manual review queue manageable (<30% of submissions)
Phase 2: Intelligent Routing
Objective: Add context-aware logic to reduce manual review burden
Enhanced Rules:
• handwritten total + amount <$300 → Approve
• Approved vendors + any handwriting → Review
• Any fraud signals → Strict review
Implementation Approach:
A/B test new rules on subset of traffic
Compare performance against Phase 1 baseline
Gradually increase traffic percentage
Monitor operational impact on review teams
Success Metrics:
Manual review rate reduced by 40-60%
Maintained fraud detection effectiveness
Improved processing times
Stakeholder satisfaction with accuracy
Phase 3: Advanced Optimization
Objective: Implement sophisticated logic and continuous improvement.
Continuous Improvement Process:
Weekly performance review meetings
Monthly logic optimization sessions
Quarterly comprehensive strategy review
Annual workshop for major logic overhaul
Testing Framework for Fraud Rules
Test Design Structure:
Control vs. Treatment Setup
Control Group (50% of volume):
• Current/baseline fraud logic
• Existing manual review processes
• Standard operational procedures
Treatment Group (50% of volume):
• New fraud detection logic
• Enhanced automation rules
• Modified review workflows
Key Performance Indicators
Fraud Detection Metrics:
• True Positive Rate (fraud correctly identified)
• False Positive Rate (legitimate submissions flagged)
• False Negative Rate (fraud missed)
Overall accuracy percentage Operational Metrics:
• Manual review volume
• Processing time per submission
• Team productivity measures
User experience impact Business Metrics:
• Fraud-related losses prevented
• Operational cost per submission
• Customer satisfaction scores
• Compliance adherence rates
Statistical Significance Requirements
Minimum sample size: 1,000 submissions per group
Test duration: 2-4 weeks for statistical validity
Confidence level: 95% for production decisions
Effect size: Minimum 10% improvement to justify change
Common Pitfalls and Best Practices
Over-Engineering from the Start
Problem: Attempting to handle every possible edge case in the initial design
Symptoms:
Decision trees with >20 decision points
Rules that require extensive documentation to understand
Logic that accounts for <1% probability scenarios
Solution:
Start with 80/20 rule: Handle 80% of cases with simple logic
Use iterative approach: Add complexity only when data justifies it
Focus on high-impact fraud patterns first
Best Practice Example:
Phase 1: Handle obvious fraud (AI-generated, clear tampering)
Phase 2: Add context rules (vendor categories, amounts)
Phase 3: Implement sophisticated pattern recognition
NOT: Try to handle everything perfectly from day one
Insufficient Stakeholder Involvement
Problem: Technical teams designing fraud logic without business input
Symptoms:
Rules that make technical sense but miss business context
High false positive rates due to misunderstanding legitimate patterns
Stakeholder rejection during review phase
Solution:
Include fraud analysts, operations, and compliance from day one
Get business sign-off at each design phase
Analysis Paralysis
Problem: Endless debate over perfect threshold values and edge cases
Symptoms:
Weeks spent debating 0.8 vs 0.85 similarity thresholds
Requirements that change every meeting
No progress toward implementation
Solution:
Set "good enough" initial thresholds based on best available data
Plan for rapid iteration and adjustment
Time-box decision-making sessions
Use A/B testing to resolve threshold debates with data
Implementation Phase Pitfalls
Big Bang Deployment
Problem: Deploying all new fraud logic simultaneously
Symptoms:
Sudden spike in false positives overwhelming operations
Inability to identify which rules are causing problems
Emergency rollbacks affecting fraud protection
Solution:
Gradual deployment: One rule category at a time
Feature flags: Enable/disable rules without code deployment
Lack of Monitoring Infrastructure
Problem: Deploying rules without adequate performance tracking
Symptoms:
No visibility into rule performance
Inability to identify underperforming rules
Manual effort required to assess fraud detection effectiveness
Solution:
Build monitoring into rule design from the start
Automated alerting for performance degradation
Real-time dashboards for operational teams
In Q3 2025 Veryfi will launch custom reports functionality that will enable custom dashboards in web portal
Operational Phase Pitfalls
Set-and-Forget Mentality
Problem: Deploying fraud logic without ongoing optimization
Symptoms:
Performance degradation over time as fraud patterns evolve
Accumulation of edge cases not handled by original logic
Stakeholder dissatisfaction with fraud detection effectiveness
Solution:
Scheduled monthly rules reviews
Quarterly logic optimization sessions
Feedback loops from manual review to rule improvement
Threshold Drift
Problem: Gradual degradation of fraud detection thresholds
Symptoms:
Slow increase in false positive rates
Unnoticed changes in fraud detection sensitivity
Rules that become less effective over time
Solution:
Automated threshold monitoring and alerting
Regular recalibration against fresh fraud samples
Version control for all threshold changes
Best Practices Summary
Design Best Practices
Start Simple: Basic protection first, sophistication later
Include Stakeholders: Business context is crucial for effective fraud detection
Visual First: Use decision trees and flowcharts before writing code/adding rules
Document Decisions: Why each rule exists and what it's meant to catch
Plan for Change: Design rules that can be easily modified
Implementation Best Practices
Gradual Rollout: Deploy incrementally to identify issues early
Monitor Everything: Build performance tracking into every rule
Test Thoroughly: Use historical data, A/B testing, and shadow mode
Train Teams: Ensure operational teams understand and can execute new logic
Version Control: Track all changes for audit and rollback purposes
Veryfi-Specific Resources:
Professional Support:
Account manager consultation for complex implementations
Technical support for Business Rules development on SLA Gold and Platinum Plans
The journey from fraud detection concept to operational success requires structured thinking, collaborative design, and iterative improvement. Use this guide as your roadmap, adapt the methodologies to your specific context, and remember that the best fraud detection system is one that evolves with your business needs and the fraud landscape.