Machine Learning in Finance
Senthil Kumar,Leman Akoglu,Nitesh Chawla,Jose A. Rodriguez-Serrano,Tanveer Faruquie,Saurabh Nagrecha
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.
