docs/GOVERNANCE.md

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