Anti-Fraud Policy

1. Purpose and Commitment

This Anti-Fraud Policy establishes the principles, controls, and enforcement mechanisms used to prevent, detect, and respond to fraudulent activity across the platform.

Fraud undermines system integrity, user trust, and regulatory compliance. Accordingly, we maintain a zero-tolerance approach to fraudulent behavior and implement layered controls designed to mitigate financial crime, abuse, and misrepresentation at every stage of platform interaction.

2. Scope of Application

This policy applies to:

• All users, including individuals and institutions

• All transactions, accounts, and integrations

• All access methods, including APIs and third-party connections

The policy operates in conjunction with applicable laws, regulatory requirements, and our internal risk management frameworks.

3. Definition of Fraud

For the purposes of this policy, fraud includes any intentional or reckless act designed to deceive, mislead, or unlawfully benefit from the platform or its users.

Fraudulent activities may include, but are not limited to:

• Identity misrepresentation or impersonation

• Submission of false, misleading, or forged information

• Unauthorized use of accounts or credentials

• Transaction manipulation, replay attacks, or routing abuse

• Circumvention of controls, limits, or monitoring systems

• Collusion or coordinated abuse involving multiple accounts

Attempts, facilitation, or conspiracy to commit fraud are treated as violations of this policy.

4. Fraud Prevention Framework

We implement a multi-layered fraud prevention framework designed to operate in real time and across system boundaries.

Key elements include:

• Identity verification and risk-based onboarding

• Transaction monitoring and behavioral analysis

• Velocity, anomaly, and pattern-based detection

• Device, network, and session integrity controls

• Segmentation of duties and access privileges

Controls are calibrated to balance fraud mitigation, user experience, and regulatory requirements.

5. Detection and Monitoring

Fraud detection is continuous and adaptive.

Monitoring mechanisms may include:

• Automated rules and machine-learning-assisted models

• Manual review by trained risk and compliance personnel

• Cross-reference checks against internal and external datasets

Monitoring activities are conducted in accordance with applicable privacy and data protection laws.