Machine Learning in Finance

Leman Akoglu, Nitesh Chawla, Josep Domingo-Ferrer, Eren Kurshan, Senthil Kumar, Vidyut Naware, Jose A. Rodriguez-Serrano, Isha Chaturvedi, Saurabh Nagrecha, Mahashweta Das, Tanveer Faruquie

Research output: Book chapterConference contributionpeer-review

Abstract

This workshop aims to explore the intersection of Generative AI with the rich tapestry of financial data types, seeking to uncover new methodologies and techniques that can enhance predictive analytics, fraud detection, and customer insights across the sector. By harnessing these advancements in AI, we can pave the way to not only understand customer behavior but also anticipate their needs more effectively, leading to superior customer outcomes and more personalized services. Our objective is to shed light on the challenges and opportunities presented by the diverse data formats in finance. We aim to bridge the gap between the dominance of traditional models for tabular data analysis and the emerging potential of Generative AI to revolutionize the treatment of time series, click streams, and other unstructured data forms.

Original languageEnglish
Title of host publicationKDD 2024 - Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
PublisherAssociation for Computing Machinery
Pages6703
Number of pages1
ISBN (Electronic)9798400704901
DOIs
Publication statusPublished - 25 Aug 2024
Event30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2024 - Barcelona, Spain
Duration: 25 Aug 202429 Aug 2024

Publication series

NameProceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
ISSN (Print)2154-817X

Conference

Conference30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2024
Country/TerritorySpain
CityBarcelona
Period25/08/2429/08/24

Keywords

  • ai
  • finance
  • genai
  • machine learning

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