Advanced Machine Learning Algorithms for Real-Time Fraud Detection in Investment Banking: A Comprehensive Framework
Keywords:
Investment Banking, Fraud DetectionAbstract
Financial institutions, especially investment banks, confront sophisticated fraud. Financial losses, reputational harm, and market stability are fraud risks. The complexity and speed of current financial transactions render static rules and human inspection insufficient for fraud detection. This study examines advanced ML algorithms for real-time investment banking fraud detection. Anomaly identification, risk assessment, and mitigation are essential.
The article begins with investment banking fraud. Domain scams include account takeover, payment manipulation, market manipulation, and insider trading. Each fraud type's methods and financial and reputational effects are described. Traditional rule-based fraud detection has flaws. Failure to adapt to changing fraud trends, high false positive rates that cause operational inefficiencies, and human review delays are major shortcomings.
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