August 9-13, 2026 [Exact Workshop Date yet to be announced by KDD]
Full Day [Exact start and end times yet to be announced by KDD]
The financial sector is a complex ecosystem where data flows in myriad forms, from structured tabular datasets to intricate time series and dynamic click streams. These diverse data formats capture everything from customer transactions and behaviors to market trends and risk factors. With the advent of multimodal machine learning techniques, the fusion of signals from conventional tabular datasets, time series, free-text articles, earnings reports, images, and networks, has de-siloed decision-making to an unprecedented degree. This deluge of actionable information, combined with easily available high-performance commodity computing resources, has significantly lowered the landscape in the industry. 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 to enhance predictive analytics, fraud detection, and customer insights across the sector. 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. The workshop will continue to serve as a platform for discussing how AI can be leveraged to provide comprehensive insights into customer behavior, market dynamics, and risk assessment, transcending the limitations of current models. We plan to invite regular papers, positional papers, and extended abstracts of work in progress. We will also encourage short papers from financial industry practitioners that introduce domain-specific problems and challenges to academic researchers. This event will be the ninth in a sequence of finance-related workshops we have organized at KDD.
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:
Understanding and predicting customer behavior
Recommendations
Detecting anomalies at large in unsupervised/semi-supervised settings
Active learning strategies in noisy and uncertain environments. Reinforcement learning strategies and their applications to gather ground truth
Model calibration, stability, and adaptiveness trade-offs
Insider trading
Fraud and abuse
Cyber threats
Money Laundering
Compliance violations
Agentic AI for Personalized Experiences and Intelligent Decision-Making
Mining for signals in financial data
Vetting and sourcing data for high-stakes decision-making
Tabular transformer techniques
Transfer learning, Time Series, Recommendation Systems, Reinforcement Learning, Network Science, Image Processing etc
Patterns and anti-patterns in early-detection
Multi-modal machine learning in practice
Sensor fusion approaches in the use of alternative data
Use cases like marketing, anomaly detection, churn prevention, etc
Model Safety, Explainability & Governance
Defense against GenAI attacks and abuse
Role of explainability in several verticals, markets
Deployed and vetted applications with explainability and reasoning
Guard-rails for GenAI
Fairness
Fairness in the context of finance - lending and beyond!
Privacy preservation
Reassessing credit in the conventional sense
Role of DeFi in fairness
Market Manipulation
Analysis of limit order book feeds
Fake news and other noisy social signal ingestion challenges
Robustness to adversarial actors
Crypto and DeFi
Specific challenges and analysis of high-risk domains
Best practices to thwart bad actors and stay compliant with an in-flux regulatory landscape
Synthetic Data
Evaluation
Privacy protection
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.
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, tables, but excluding references and appendices. 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 the submission portal by Apr 30, 2026
<CMT3 PORTAL LINK>
Paper Submissions Due: Apr 30, 2026
Author Notifications: May 31, 2026
Workshop: August 9-13, 2026 (exact date yet to be announced by KDD)