What AI application is primarily used for fraud detection in finance?

The application most commonly associated with fraud detection in finance is predictive trading algorithms. These algorithms analyze historical and current trading data to identify unusual patterns and anomalies that may indicate fraudulent activity. By leveraging machine learning models and statistical techniques, predictive trading algorithms can assess transactions in real-time and flag suspicious activities that deviate from established behavioral norms.

While other choices address important aspects of finance, such as supply-chain logistics, investment strategies, and personalized financial advice, they do not directly focus on fraud detection. Supply-chain optimization pertains more to enhancing efficiency in logistics rather than identifying fraudulent transactions. Investment portfolio optimization deals with maximizing returns on investments, which is also not centered on detecting fraud. Personalized financial advice focuses on tailoring offerings to individual clients and does not typically employ AI for monitoring fraud within transactions. Hence, predictive trading algorithms stand out as the primary AI application designed specifically for detecting fraud in the financial sector.