Detecting Misconduct and Malfeasance within Financial InstitutionsSpeaker: Panos Ipeirotis – New York, NY, United States
Topic(s): Information Systems, Search, Information Retrieval, Database Systems, Data Mining, Data Science
AbstractMisbehavior in the online world manifests itself in several forms, and often depends on the domain at hand. In the financial domain, firms have the regulatory obligation to self-monitor the activities of their employees (e.g., emails, chats, phone calls), in order to detect any form of misconduct. Some forms of misconduct are illegal activities (e.g., insider trading, bribery) while others are various forms of policy violations (e.g., following improper security practices, or inappropriate language use). Traditionally, and due to ease of understanding and implementation, firms deployed relatively archaic, rule-based systems for employee surveillance. Such rule-based systems generate a large number of false positive alerts and are hard to adapt in changing environments. More recent techniques aimed at solving the problem by simply transitioning from simple rule-based techniques to statistical machine learning approaches, trying to treat the problem of misconduct detection as a single-document classification problem. We discuss why approaches that try to identify misconduct within single documents are destined to fail, and we present a set of approaches that focus on actors, connections among actors, and on cases of misconduct. Furthermore, we highlight the importance of having a ``human in the loop'' approach, where humans are both guided and guide the system at the same time, in order to detect malfeasance faster, and also adapt to changing environments; we also show how humans can play an important role in detecting shortcomings of existing machine-learning-based malfeasance-detection systems, and how humans can be incentivized to detect such shortcomings. Our multifaceted approach has been used in real environments within both big, multinational and smaller financial institutions; we discuss the practical constraints and lessons learned by operating in such non-tech, highly regulated environments.
About this LectureNumber of Slides: 50
Duration: 45 minutes
Languages Available: English
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