KDD Workshop on Machine Learning in Finance

Virtual Workshop

August 15, 2021


The finance industry is constantly faced with an ever evolving set of challenges including credit card fraud, identity theft, network intrusion, money laundering, human trafficking, and illegal sales of firearms. There is also the newly emerging threat of fake news in financial media that can lead to distortions in trading strategies and investment decisions. In addition, traditional problems such as customer analytics, forecasting, and recommendations take on a unique flavor when applied to financial data. A number of new ideas are emerging to tackle all 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 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. This will be the fourth in a sequence of finance related workshops we have organized at KDD. The first workshop was held at KDD 2017, the second workshop at KDD 2019 and the third at KDD2020.

Call for Papers

We invite papers on machine learning and AI with applications to the financial industry. Topics of interest include, but are not limited to, the following:

Application areas:

    • Analysis of financial graphs

    • Early detection of emerging phenomena

    • Fraud, anti money laundering and identity theft

    • Fake news in financial outlets

    • Fairness in lending

    • Enhanced risk modeling

    • Monitoring

    • Recommendations

    • Text analytics of financial reports, forecasts and documents

    • Forecasting

    • Social media mining

    • Manipulation in cryptocurrency markets

    • Forecasting

    • Trustworthy AI

    • The interplay between social media and stock markets

Technical areas:

Analysis of financial graphs:

      • Node embeddings for downstream classification and prediction

      • Link prediction for forecasting

      • Node classification on partial graphs

      • Analysis of heterogeneous graphs

Semi-supervised anomaly detection (aka Novelty Detection):

      • Data available for training does not contain any anomalies and represents expected operation of the system.

      • Data with “positive only” labels.

Explainable models:

      • 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 techniques for anti money laundering and other financial crimes:

      • 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

Time Series:

      • Predictions in temporal graphs

      • Forecasting

Text Analytics and NLP:

      • Transfer learning for financial text data

      • Detection of emerging trends

      • Linguistic approaches for fake news detection

      • Analysis of unstructured text data within transactional financial data.

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 the technical areas mentioned above, 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 and some would be selected for oral presentations, depending on schedule constraints. Accepted papers will be posted on the workshop website.

Following the KDD conference submission policy, reviews are double-blind, and author names and affiliations should NOT be listed.

Papers should be submitted on CMT3 by May 20, 2021 11:59 PM Pacific Time


Key dates

  • Submission deadline: May 20, 2021 11:59 PM Pacific Time at https://cmt3.research.microsoft.com/KDDMLF2021.

  • Author notification: June 10, 2021 June 22, 2021 (notification date changed due to a large number of submissions)

  • Camera ready papers due: Aug 5, 2021

  • Workshop: August 15, 2021