Papers can be downloaded from the "Paper Downloads" section

07:55 - 08:00 AM - Opening Remarks Senthil Kumar (Capital One)

08:00 - 08:15 AM - Invited Talk: The Cost of Finding Anomalies - Adekunle Adediji (Visa)

08:15 - 08:30 AM - Invited Talk: How Reuters gained speed and scale by automated detection, verification and publication of breaking news from Twitter - Armineh Nourbakhsh (S&P Global Ratings)

08:30 - 08:45 AM - Invited Talk: Fake News: A Path to the Truth - Clay Eltzroth (Bloomberg L.P.)

08:45 - 09:00 AM - Invited Talk: Higher-order Networks for Anomaly Detection - Jian Xu (Citadel) and Mandana Saebi (University of Notre Dame)

09:00 - 09:30 AM - Lightning Oral Paper Presentations

  • [09:00 - 09:05 AM] Detection of Accounting Anomalies in the Latent Space using Adversarial Autoencoder Neural Networks - Marco Schreyer (University of St. Gallen); Timur Sattarov (Deutsche Bundesbank); Christian Schulze (University of St. Gallen); Bernd Reimer (PricewaterhouseCoopers GmbH WPG); Damian Borth (University of St. Gallen)
  • [09:05 - 09:10 AM] Risk Management via Anomaly Circumvent: Mnemonic Deep Learning for Midterm Stock Prediction - Xinyi Li (Columbia University); Yinchuan Li (Columbia University); Xiao-Yang Liu (Columbia University); Christina Dan Wang (New York University)
  • [09:10 - 09:15 AM] Case-Based Reasoning for Assisting Domain Experts in Processing Fraud Alerts of Black-Box Machine Learning Models - Hilde J.P. Weerts (Eindhoven University of Technology); Mykola Pechenizkiy (TU Eindhoven); Werner van Ipenburg (Cooperatieve Rabobank U.A.)
  • [09:15 - 09:20 AM] A framework for anomaly detection using language modeling, and its applications to finance - Armineh Nourbakhsh (S&P Global Ratings); Grace Bang (S&P Global)
  • [09:20 - 09:25 AM] Detecting Unusual Expense Groups for Customer Advice Apps - Axel Brando (BBVA Data & Analytics); Jose Rodriguez-Serrano (BBVA Analytics SL); Jordi Vitria (Universitat de Barcelona)
  • [09:25 - 09:30 AM] Empirical Study on Detecting Controversy in Social Media Zhiqiang Ma (S&P Global); Xiaomo Liu (S&P Global); Azadeh Nematzadeh (S&P Global); Grace Bang (S&P Global)

09:30 - 10:00 AM - Coffee Break + Socializing

10:00 - 10:15 AM - Invited Talk: Applied Anomaly Detection: Money Laundering and Terrorist Financing - Kathryn O'Donnell (Clovis Technologies)

10:15 - 10:30 AM - Invited Talk: Understanding sample likelihood computation in GANs - Soheil Feizi (University of Maryland)

10:30 - 10:45 AM - Tutorial: Experiments in Graph Deep Learning for Cryptocurrency Forensics - Mark Weber (MIT-IBM Watson AI Lab); Giacomo Domeniconi (IBM T.J. Watson Research Center); Moreno Bonaventura (Elliptic Enterprises Ltd.); Claudio Bellei (Elliptic Enterprises Ltd.); Daniel Karl (IBM Research AI); Jie Chen (IBM Research)

10:45 AM - 12:00 PM - Poster Presentations

  • Automating Data Monitoring: Detecting Structural Breaks in Time Series Data Using Bayesian Minimum Description Length - Yingbo Li (Capital One)
  • Infusing domain knowledge in AI-based "black box" models for better explainability with application in bankruptcy prediction - Sheikh Rabiul Islam (Tennessee Technological University); William Eberle (Tennessee Tech. University); Sid Bundy (Tennessee Technological University); Sheikh Khaled Ghafoor (Tennessee Technological University)
  • Automatic Model Monitoring for Data Streams - Fábio Pinto (Feedzai); Marco O P Sampaio (Feedzai); Pedro Bizarro (Feedzai)
  • Online NEAT for Credit Evaluation - a Dynamic Problem with Sequential Data - Yue Liu (The Southern University of Science and Technology ); Adam Ghandar (Southern University of Science and Technology); Georgios Theodoropoulos (Southern University of Science and Technology)
  • Detecting Anomalies in Sequential Data with Higher-Order Networks - Mandana Saebi (University of Notre Dame)
  • Calibration for Anomaly Detection - Adrian Benton (Bloomberg L.P.)
  • Textual Outlier Detection and Anomalies in Financial Reporting - Leslie Barrett (Bloomberg L.P.)
  • Detecting anomalous doctors by measuring behavioral volatility using temporal clustering - Daniel A Lasaga (Deloitte)
  • Multi-Modal and Multi-Level Machine Learning for Fake Rideshare Trip Detection - Chengliang Yang (Uber Techologies)
  • Systematic detection of fraudulent account registration - Yun Zhang (Uber technologies, Inc)
  • Dynamic Linear Regression for Variable Level Monitoring - Thomas J Caputo (Capital One)