TY - GEN
T1 - Machine Learning in Finance
AU - Akoglu, Leman
AU - Chawla, Nitesh
AU - Domingo-Ferrer, Josep
AU - Kurshan, Eren
AU - Kumar, Senthil
AU - Naware, Vidyut
AU - Rodriguez-Serrano, Jose Antonio
AU - Chaturvedi, Isha
AU - Nagrecha, Saurabh
AU - Das, Mahashweta
AU - Faruquie, Tanveer
N1 - Publisher Copyright:
© 2024 Copyright held by the owner/author(s).
PY - 2024/8/25
Y1 - 2024/8/25
N2 - 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.
AB - 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.
KW - ai
KW - finance
KW - genai
KW - machine learning
UR - http://www.scopus.com/inward/record.url?scp=85203720831&partnerID=8YFLogxK
U2 - 10.1145/3637528.3671488
DO - 10.1145/3637528.3671488
M3 - Conference contribution
AN - SCOPUS:85203720831
T3 - Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
SP - 6703
BT - KDD 2024 - Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
PB - Association for Computing Machinery
T2 - 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2024
Y2 - 25 August 2024 through 29 August 2024
ER -