Safety Framework for Autonomous Financial Agents

As we enable AI agents to participate in financial transactions, ensuring safety isn't optional—it's essential. Today, we're publishing our comprehensive safety framework for autonomous financial agents.

Core Principles

Our safety framework is built on four foundational principles:

Spending Limits

Our platform implements hierarchical spending controls:

Dynamic Limit Adjustment

Limits can be adjusted based on trust signals:

interface LimitAdjustment {
  // Factors that can increase limits
  positiveSignals: {
    successfulTransactions: number;
    accountAge: Duration;
    verificationLevel: 'basic' | 'enhanced' | 'enterprise';
  };
  
  // Factors that decrease limits
  riskSignals: {
    unusualActivity: boolean;
    failedVerifications: number;
    disputeRate: number;
  };
}

Human-in-the-Loop

Not all transactions should be fully autonomous. Our framework defines when human approval is required:

Approval Workflows

The platform supports multiple approval patterns:

1. Synchronous Approval - Agent waits for explicit user confirmation
2. Asynchronous Approval - User has a window to reject before execution
3. Escalation Chains - Multi-level approval for high-value transactions
4. Policy-based Auto-approval - Pre-defined rules for routine transactions

Fraud Detection

Our ML-based fraud detection system monitors for:

Detection Pipeline

class FraudDetector:
    def analyze_transaction(self, txn: Transaction) -> RiskScore:
        scores = [
            self.velocity_check(txn),
            self.pattern_match(txn),
            self.behavioral_analysis(txn),
            self.merchant_risk(txn),
            self.geographic_risk(txn)
        ]
        
        # Weighted ensemble of risk signals
        final_score = self.ensemble_score(scores)
        
        if final_score > BLOCK_THRESHOLD:
            return RiskScore.BLOCK
        elif final_score > REVIEW_THRESHOLD:
            return RiskScore.REQUIRE_REVIEW
        else:
            return RiskScore.APPROVE

Audit & Compliance

Every agent action is logged with:

• Timestamp - Precise timing with microsecond resolution
• Context - What the agent was trying to accomplish
• Authorization Chain - How the action was authorized
• Outcome - Result of the action
• Rollback Info - Steps to reverse if needed

Incident Response

When issues are detected, our response protocol activates:

1. Immediate - Suspicious transactions are paused
2. Assessment - Automated triage determines severity
3. Notification - Users are alerted through multiple channels
4. Investigation - Detailed analysis of the incident
5. Resolution - Remediation and prevention measures
6. Disclosure - Transparent communication about what happened

Continuous Improvement

Safety is not a destination—it's an ongoing process. We commit to:

• Regular Audits - Third-party security assessments quarterly
• Red Team Exercises - Proactive testing of defenses
• Incident Learning - Publishing anonymized learnings from incidents
• Community Input - Incorporating feedback from researchers and users

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Read the full safety documentation or contact our safety team to discuss specific requirements for your use case.