Real-Time Data Analytics for Fraud Detection in Investment Banking Using AI and Machine Learning: Techniques and Case Studies
Keywords:
Investment Banking Fraud, Real-Time Data Analytics, Machine LearningAbstract
Complex financial products and digital investment banking encourage fraud. Rule-based fraud detection systems are effective but can't keep up with criminals' tactics. This research examines AI and ML-driven real-time data analytics for investment banking fraud detection.
The article begins with investment banking fraud. Including account manipulation, unlawful trading, fraudulent account setup, and tech-enabled crimes. Rules-based systems fail to identify new fraud.
This article then examines how AI and ML-powered real-time data analytics may change. These methods are crucial for abnormality detection in massive financial transaction, consumer behavior, and network activity datasets. Investment banking fraud detection using supervised and unsupervised learning is compared.
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