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Latest coverage for Fraud

Stay informed on the latest in information security with updates on fraud prevention, detection techniques, and cyber fraud trends.

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Fraud is intentional deception used to obtain money, access, information, or another unfair benefit. In information security, the term commonly covers digitally enabled schemes such as phishing, account takeover, payment fraud, business email compromise, and misuse of stolen identities or credentials. The defining feature is deceptive use of systems, accounts, or data—not merely a technical failure.

Security teams should treat fraud as both an identity and transaction-risk problem. Relevant controls include phishing-resistant authentication, least-privilege access, payment and account-change verification, and monitoring for unusual login or transaction patterns. Personal and financial data require appropriate privacy protections because exposed data can support impersonation even when passwords are not compromised. Investigation must preserve authentication, email, endpoint, and transaction records so organizations can contain unauthorized access, reverse or block fraudulent activity where possible, notify affected parties, and improve controls based on the attack path.

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Bank Info Security 1 year, 3 months ago

Shifting the Mindset from Cost Center to Revenue Generator

How Strong AML and Fraud Prevention Controls Can Differentiate Your Fintech and Help You ScaleAs fintechs and financial institutions navigate an increasingly complex payments ecosystem, robust AML and fraud prevention controls are no longer just a compliance necessity—they’re a strategic advantage. Discover how risk-based frameworks, intelligent detection systems, and stronger partnerships can enhance trust, streamline operations, and safeguard against evolving financial crime threats.

Bank Info Security 1 year, 3 months ago

Fighting Financial Fraud With Adversarial AI Defenses

Experts Weigh the Advantages and Risks of Generative Adversarial NetworksWith traditional rule-based fraud detection systems and even conventional machine learning models struggling to identify these highly deceptive fraud patterns, financial institutions are exploring generative adversarial networks to enhance fraud detection.