Connectionists: KDD Workshop on Anomaly Detection in Finance

[Senthil Kumar]

2nd KDD Workshop on Anomaly Detection in Finance

Summary

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.


Organizers:

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



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:
<https://sites.google.com/view/kdd-adf-2019/home#h.p_Z85HVqqZhkjS>
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
         - Monitoring

● *Other applications*

   - Fake news
         - Social media mining
         - Early detection of emerging phenomena
         - Manipulation in cryptocurrency markets

<https://sites.google.com/view/kdd-adf-2019/home#h.p_J0dt5ziYAVTA>
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


<https://sites.google.com/view/kdd-adf-2019/home#h.p_6c2U76vAxNH6>
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.


<https://sites.google.com/view/kdd-adf-2019/home#h.p_jFgFSEZlxtNh>
Submission Guidelines:

All submissions must be PDFs formatted in the Standard ACM Conference
Proceedings Template
<https://www.google.com/url?q=https%3A%2F%2Fwww.overleaf.com%2Flatex%2Ftemplates%2Facm-conference-proceedings-new-master-template%2Fpnrfvrrdbfwt&sa=D&sntz=1&usg=AFQjCNHGnGiYlKRooAVXx8x0rFzhVzcPJA>.
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*

*https://cmt3.research.microsoft.com/ADF2019*
<https://www.google.com/url?q=https%3A%2F%2Fcmt3.research.microsoft.com%2FADF2019&sa=D&sntz=1&usg=AFQjCNFEHpQQXYfmpNUBzAp8rOM5ASK3uw>


<https://sites.google.com/view/kdd-adf-2019/home#h.p_WZBJ-s7nxP4a>
Key dates

   - *Submission deadline: May 12, 2019 11:59 PM Pacific Time* *at
   https://cmt3.research.microsoft.com/ADF2019
   <https://www.google.com/url?q=https%3A%2F%2Fcmt3.research.microsoft.com%2FADF2019&sa=D&sntz=1&usg=AFQjCNFEHpQQXYfmpNUBzAp8rOM5ASK3uw>
   *
   - Author notification: June 1*, *2019
   - Workshop: August 5, 2019
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