# Governance Standards

## Overview

FraudSimulator-AI implements enterprise-grade governance standards for fraud detection in regulated insurance markets. All decisions are auditable, explainable, and compliant with GCC regulatory requirements.

## Core Governance Principles

### 1. Decision Traceability

Every fraud decision must be fully traceable:

**Audit Log Requirements:**
- Unique audit ID for each decision
- UTC timestamp
- Claim ID and claimant information
- Input data snapshot
- Model version used
- Decision output (investigate | allow)
- Fraud score and risk band
- Evidence list
- Confidence score

**Retention Policy:**
- Audit logs retained for minimum 7 years
- Immutable storage (append-only)
- Encrypted at rest and in transit
- Access controlled via role-based permissions

### 2. Explainability (XAI)

All decisions must be explainable to:
- Claims adjusters
- Fraud investigators
- Regulators
- Claimants (upon request)

**Explainability Requirements:**
- List of activated fraud indicators
- Indicator weights and contributions
- Human-readable descriptions
- Confidence score with interpretation
- Model version and decision threshold

### 3. Human-in-the-Loop (HITL)

AI recommends, humans decide:

**Override Capability:**
- All AI decisions can be overridden by authorized personnel
- Override reason must be documented
- Override logged in audit trail
- Override patterns monitored for model improvement

**Escalation Rules:**
- High-risk decisions (fraud_score ≥ 0.7) → Fraud investigation team
- Medium-risk decisions (0.4-0.7) → Senior claims adjuster
- Low-confidence decisions (confidence < 0.6) → Manual review
- Borderline cases (fraud_score 0.6-0.7) → Dual review

**Human Review SLA:**
- High-risk: Review within 4 hours
- Medium-risk: Review within 24 hours
- Low-risk: Review within 72 hours

### 4. Bias & Fairness Monitoring

**Protected Attributes:**
The system must NOT use:
- Gender
- Age (except for actuarial validity)
- Nationality
- Religion
- Ethnicity
- Disability status

**Bias Detection:**
- Monthly analysis of decision patterns across demographics
- Statistical parity testing
- Disparate impact analysis
- Equal opportunity metrics

**Bias Mitigation:**
- Feature importance analysis
- Fairness constraints in model training
- Regular bias audits by independent third party
- Corrective action plan for detected bias

### 5. Model Drift Monitoring

**Drift Detection:**
- **Data Drift**: Monitor input feature distributions
- **Concept Drift**: Monitor fraud_score distribution over time
- **Performance Drift**: Track precision, recall, F1 score

**Monitoring Frequency:**
- Real-time: Decision latency, error rates
- Daily: Fraud score distribution, decision volume
- Weekly: Precision, recall, false positive rate
- Monthly: Comprehensive model performance review

**Drift Thresholds:**
- **Warning**: 10% deviation from baseline
- **Alert**: 20% deviation from baseline
- **Critical**: 30% deviation → Model retraining required

**Retraining Triggers:**
- Performance degradation > 15%
- Significant data drift detected
- New fraud patterns identified
- Regulatory requirement changes
- Quarterly scheduled retraining

### 6. PII & Data Protection

**Data Classification:**
- **PII**: Name, ID number, contact information
- **Sensitive**: Financial data, health information
- **Public**: Claim type, general statistics

**Protection Measures:**
- PII encrypted at rest (AES-256)
- PII encrypted in transit (TLS 1.3)
- PII access logged and monitored
- PII retention limited to regulatory minimum
- Right to erasure (GDPR-compliant)

**Data Minimization:**
- Collect only necessary data for fraud detection
- Anonymize data for model training
- Pseudonymize data for analytics
- Delete PII after retention period

### 7. Regulatory Compliance

**IFRS 17 Compliance:**
- Fraud detection impacts loss reserves
- Decisions must be actuarially sound
- Audit trail supports financial reporting
- Model assumptions documented

**AML Compliance:**
- Detect money laundering via insurance fraud
- Flag suspicious patterns for AML team
- Integrate with AML transaction monitoring
- Report suspicious activity per regulations

**GCC Insurance Regulations:**
- Comply with local insurance authority requirements
- Support Takaful-specific fraud patterns
- Align with Sharia compliance where applicable
- Meet local data residency requirements

**Audit Readiness:**
- Documentation of model development
- Validation reports
- Performance monitoring reports
- Bias and fairness audits
- Incident response logs

### 8. Security Standards

**Access Control:**
- Role-based access control (RBAC)
- Principle of least privilege
- Multi-factor authentication (MFA) required
- Access reviews quarterly

**Roles:**
- **Fraud Analyst**: View decisions, evidence, audit logs
- **Claims Adjuster**: View decisions, submit overrides
- **Data Scientist**: Model training, performance monitoring
- **Compliance Officer**: Full audit access, bias reports
- **System Admin**: Infrastructure management

**Security Monitoring:**
- Failed login attempts
- Unauthorized access attempts
- Data export activities
- Model prediction anomalies
- System performance anomalies

### 9. Incident Response

**Incident Types:**
- Model performance degradation
- Bias detection
- Security breach
- Data quality issues
- System outage

**Response Protocol:**
1. **Detection**: Automated monitoring alerts
2. **Assessment**: Severity classification (P1-P4)
3. **Containment**: Isolate affected systems
4. **Investigation**: Root cause analysis
5. **Remediation**: Fix and validate
6. **Documentation**: Incident report
7. **Review**: Post-mortem and lessons learned

**Escalation:**
- P1 (Critical): Immediate escalation to CTO
- P2 (High): Escalation within 1 hour
- P3 (Medium): Escalation within 4 hours
- P4 (Low): Escalation within 24 hours

### 10. Model Versioning & Rollback

**Version Control:**
- Semantic versioning (MAJOR.MINOR.PATCH)
- Git-based model registry
- Tagged releases with documentation
- Changelog for each version

**Deployment Process:**
1. Model training and validation
2. Bias and fairness testing
3. Performance benchmarking
4. Staging deployment
5. A/B testing (10% traffic)
6. Gradual rollout (25% → 50% → 100%)
7. Production monitoring

**Rollback Criteria:**
- Performance degradation > 10%
- Bias detected
- System errors > 1%
- Stakeholder escalation

**Rollback Process:**
- Immediate revert to previous version
- Incident investigation
- Root cause analysis
- Fix and revalidate
- Controlled re-deployment

## Governance Metrics

**Tracked Metrics:**
- Decision volume (daily, weekly, monthly)
- Fraud detection rate
- False positive rate
- False negative rate
- Override rate
- Average confidence score
- Decision latency
- Audit log completeness
- Bias metrics (demographic parity, equal opportunity)
- Model drift indicators

**Reporting:**
- **Daily**: Operations dashboard
- **Weekly**: Performance summary
- **Monthly**: Executive report
- **Quarterly**: Regulatory compliance report
- **Annual**: Comprehensive governance audit

## Continuous Improvement

Governance standards are reviewed and updated:
- Quarterly governance committee meetings
- Annual third-party audit
- Regulatory requirement changes
- Industry best practice updates
- Stakeholder feedback integration

## Accountability

**Roles & Responsibilities:**
- **Chief Risk Officer**: Overall governance accountability
- **Head of Fraud**: Fraud detection effectiveness
- **Chief Data Officer**: Data quality and protection
- **Compliance Officer**: Regulatory compliance
- **Data Science Lead**: Model performance and fairness

## Contact

For governance inquiries:
- Email: governance@bdr-ai.com
- Escalation: compliance@bdr-ai.com