posted by user: lemanakoglu || 1492 views || tracked by 6 users: [display]

ADF 2019 : 2nd KDD Workshop on Anomaly Detection in Finance


When Aug 5, 2019 - Aug 5, 2019
Where Anchorage, Alaska, USA
Submission Deadline May 12, 2019
Notification Due Jun 1, 2019
Final Version Due Jun 15, 2019
Categories    anomaly detection   financial analytics   outlier mining   machine learning

Call For Papers

CFP: 2nd ACM SIGKDD Workshop on Anomaly Detection in Finance

Anomaly Detection in Finance @ KDD 2019

August 5, 2019 in Anchorage, Alaska, USA

Detecting anomalies and novel events is vital to the financial industry. These events are often indicative of illegal activities such as credit card fraud, identity theft, network intrusion and money laundering. Left unchecked, these activities can cause poor customer experiences and billions of dollars in losses. In addition to these activities, a new threat is emerging in the form of fake news in financial media outlets that can lead to distortions in trading strategies and investment decisions. A number of new techniques are emerging to tackle these problems including semi-supervised learning methods, deep learning algorithms, network/graph based solutions as well as linguistic approaches. These methods must often be able to work in real-time and be able to handle large volumes of data. The purpose of this workshop is to bring together researchers and practitioners to discuss both the problems faced by the financial industry and potential solutions.

Key dates
Submission deadline: May 5, 2019 11:59 PM Pacific Time at
Author notification: June 1, 2019
Workshop: August 5, 2019

Call for Papers
We invite papers on anomaly and novelty detection with applications for the financial industry. Topics of interest include, but are not limited to, the following:

Business problems:
● Financial Crimes:
Anti-money laundering
Fraudulent transactions
Identity theft and fake account registration
Promotion credit abuse
Account takeover
Insurance fraud

● Risk Modeling
Enhanced risk modeling
Regulation-aware feature engineering

● Other applications
Fake news
Social media mining
Early detection of emerging phenomena
Technical problems:

● Semi-supervised anomaly detection (aka Novelty Detection):
Data available for training does not contain any anomalies and represents expected operation of the system.
The algorithm classifies everything that does not fit in description of the previously seen data as a “novelty”.

● Unsupervised anomaly detection (aka Outlier Detection):
Data available for training may contain anomalies, which are assumed to be rare.
Anomalies detected by the algorithm are considered to be “outliers” relative to the majority of available data.

● Explainable models for anomaly detection
Models that can explain their decisions in interpretable ways
Post-hoc methods that can be used to explain outputs of other detection algorithms

● Human-in-the-loop anomaly detection
Interactive ranking techniques
Methods that can handle exploration-exploitation trade-off
Novel human feedback gathering strategies beyond labeling

● Adversarially-robust detection
Methods that are provably robust to evasion and camouflage
Evasion-cost aware fraud and intrusion detection
Analysis of evasion schemes and camouflage mechanisms

We also invite tutorials and introductory papers to bridge the gap between academia and the financial industry:

Overview of Industry Challenges

Short papers from financial industry practitioners that introduce domain specific problems and challenges to academic researchers. These papers should describe problems that can inspire new research directions in academia, and should serve to bridge the information gap between academia and the financial industry.
Algorithmic Tutorials

Short tutorials from academic researchers that explain current solutions to challenges related to anomaly detection, not necessarily limited to the financial domain. These tutorials will serve as an introduction and enable financial industry practitioners to employ/adapt latest academic research to their use-cases.

Submission Guidelines:

All submissions must be PDFs formatted in the Standard ACM Conference Proceedings Template. Submissions are limited to 8 content pages or less, including all figures and tables but excluding references. All accepted papers will be presented as posters; some may be selected for oral presentations, depending on schedule constraints. Accepted papers will be posted on the workshop website or, at the authors’ request, may be linked to an external repository such as arXiv.

Authors should clearly indicate in their abstracts the kinds of submissions that the papers belong to, to help reviewers better understand their contributions.

Papers should be submitted on CMT3 by May 12, 2019 11:59 PM Pacific Time
at the link below:


Leman Akoglu, Carnegie Mellon University
Nitesh Chawla, University of Notre Dame
Senthil Kumar, Capital One
Prabhanjan (Anju) Kambadur, Capital One
Tanveer Faruquie, Capital One
Saurabh Nagrecha, Capital One

Related Resources

ICMLA 2019   18th IEEE International Conference on Machine Learning and Applications
IEEE ICDM 2019   ICDM 2019: The 19th IEEE International Conference on Data Mining
RADIMCC 2019   Special Issue: Recent Advances in Detection, Investigation and Mitigation of Cyber Crimes (J.UCS)
NeurIPS 2019   Thirty-third Conference on Neural Information Processing Systems
TrueFact 2019   KDD 2019 Workshop on Truth Discovery and Fact Checking: Theory and Practice
FAIML 2019   2019 International Conference on Frontiers of Artificial Intelligence and Machine Learning (FAIML 2019)
ICEFS 2019   【CPCI / Web of Science】2019 3rd International Conference on Economics, Finance and Statistics
NIPS 2019   Thirty-third Conference on Neural Information Processing Systems
DIMVA 2019   16th International Conference on Detection of Intrusions and Malware & Vulnerability Assessment