Past Meetup

Detecting Misconduct and Malfeasance within Financial Institutions

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Dataiku, ACM ( and NYC Data Science Academy are hosting two talks focused on human influence in data science.

Tentative Schedule:
6:00pm: Pizza + Beer networking
6:15pm: Dr. Panos Ipeirotis, Professor and George A. Kellner
Faculty Fellow at NYU
7:15pm: Alexander Wolf, Data Scientist at Dataiku

Talk Abstracts:
Detecting Misconduct and Malfeasance within Financial Institutions by Dr. Ipeirotis:
Misbehavior 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 for 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 practical constraints and lessons learned by
operating in such non-tech, highly regulated environments.

Attention-based Models and their Revolution of NLP by Alexander Wolf:
In order to build robust NLP models that can reason from language well, architectures should function more similarily to how our human brains work over pure pattern recognition. Attention is an interpretable type of neural network layer that is loosely based on attention in humans, and it has recently enabled a powerful alternative to RNNs. Attention-based models have produced new techniques and state of the art performances for many language modeling tasks. In this presentation, an introduction to Attention layers will be given along with why and how they have been utilized to revolutionize NLP.

Panos Ipeirotis is a Professor and George A. Kellner
Faculty Fellow at the Department of Information, Operations, and
Management Sciences at Leonard N. Stern School of Business of New York University. He received his Ph.D. degree in Computer Science from
Columbia University in 2004. He has received nine “Best Paper” awards
and nominations, a CAREER award from the National Science Foundation,
and is the recipient of the 2015 Lagrange Prize in Complex Systems,
for his contributions in the field of social media, user-generated
content, and crowdsourcing.

Alex is a Data Scientist at Dataiku, working with clients around the world to organize their data infrastructures and deploy data-driven products into production. Prior, he worked on software and business development in the tech industry and studied Computer Science and Statistics at Dartmouth College. He's passionate about the latest developments in Deep Learning/Tech and enriches Dataiku's NLP features.