Anti-fraud system
Anti-fraud system definition
An anti-fraud system is a system to find, prevent, and deal with fraud. It uses machine learning, AI, and data analytics to detect suspicious transactions, identity theft, and cyber scams. The system is typically used by businesses, especially those in the financial sector.
See also: salami fraud, scam, machine learning
Anti-fraud system examples:
Fraud Detection Algorithms: Algorithms analyze transaction patterns to identify suspicious behavior. They use techniques like machine learning, data mining, and statistical analysis.
Identity Verification: Procedures that confirm the identity of users — multi-factor authentication, biometrics, and document verification.
Risk Scoring: Evaluating each transaction based on factors like transaction amount, location, and patterns of user behavior. Transactions with high-risk scores are flagged for review.
Behavioral Analytics: Monitoring users to identify unusual behavior patterns. For example, a sudden change in the location of transactions, a rapid increase in transaction volume, or weird times of activity.
Data Encryption: To prevent data breaches and the theft of sensitive data, anti-fraud systems ensure that all data is encrypted so it can be transmitted securely.
Auditing: Auditing and reporting helps organizations track and analyze their fraud prevention efforts. They may provide reports on detected fraud, the effectiveness of preventive measures, and areas that need improvement.