TY - GEN
T1 - Machine Learning in Finance
AU - Kumar, Senthil
AU - Akoglu, Leman
AU - Chawla, Nitesh
AU - Rodriguez-Serrano, Jose Antonio
AU - Faruquie, Tanveer
AU - Nagrecha, Saurabh
N1 - Publisher Copyright:
© 2021 Owner/Author.
PY - 2021/8/14
Y1 - 2021/8/14
N2 - 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 are also newly emerging threats such as 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. We have invited regular papers, positional papers and extended abstracts of work in progress. We have also encouraged short papers from financial industry practitioners that introduce domain specific problems and challenges to academic researchers. This event is the fourth in a sequence of finance related workshops we have organized at KDD since 2017.
AB - 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 are also newly emerging threats such as 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. We have invited regular papers, positional papers and extended abstracts of work in progress. We have also encouraged short papers from financial industry practitioners that introduce domain specific problems and challenges to academic researchers. This event is the fourth in a sequence of finance related workshops we have organized at KDD since 2017.
KW - early detection of emerging phenomenon
KW - fairness in lending
KW - finance
KW - financial graphs
KW - forecasting
KW - fraud
UR - http://www.scopus.com/inward/record.url?scp=85114948911&partnerID=8YFLogxK
U2 - 10.1145/3447548.3469456
DO - 10.1145/3447548.3469456
M3 - Conference contribution
AN - SCOPUS:85114948911
T3 - Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
SP - 4139
EP - 4140
BT - KDD 2021 - Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
PB - Association for Computing Machinery
T2 - 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2021
Y2 - 14 August 2021 through 18 August 2021
ER -
