Date: 2026-08-09 to 2026-08-13
Location: Jeju, South Korea
Type: Workshop (co-located with KDD 2026)
Website: wikicfp.com
What Is the ACM SIGKDD Workshop on Machine Learning in Finance 2026?
The ACM SIGKDD Workshop on Machine Learning in Finance (KDD-MLF 2026) is a specialized workshop co-located with the annual ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2026). Scheduled for August 9–13, 2026, in Jeju, South Korea, this workshop brings together researchers, practitioners, and industry leaders to explore the intersection of machine learning and financial services. Organized under the auspices of the Association for Computing Machinery’s Special Interest Group on Knowledge Discovery and Data Mining (ACM SIGKDD), the workshop provides a focused forum for discussing cutting-edge ML applications in finance.
The KDD-MLF 2026 workshop addresses four core domains: algorithmic trading, risk management, fraud detection, and financial forecasting. Each of these areas has seen rapid transformation in recent years as financial institutions increasingly adopt advanced machine learning techniques to gain competitive advantages, improve operational efficiency, and manage complex regulatory requirements. By situating the workshop within the broader KDD conference, attendees benefit from cross-pollination with the latest advances in data mining and knowledge discovery across multiple industries.
This workshop matters because the financial sector generates massive, high-frequency datasets that present unique challenges for machine learning—including non-stationarity, low signal-to-noise ratios, and strict regulatory constraints. KDD-MLF 2026 serves as a critical venue for sharing methodologies that address these specific challenges, bridging the gap between academic research and real-world financial applications.
Why It Matters for AI Professionals
For AI professionals working in or adjacent to the financial industry, KDD-MLF 2026 offers direct exposure to state-of-the-art techniques being deployed in production environments. The workshop’s focus on algorithmic trading, risk management, fraud detection, and financial forecasting means attendees will encounter practical solutions to problems that cost financial institutions billions annually. Whether you are developing trading algorithms, building credit risk models, or designing anti-fraud systems, the insights shared at this workshop can directly inform your work.
Moreover, the workshop’s co-location with KDD 2026 provides a unique opportunity to network with leading researchers and practitioners from both the ML and finance communities. Attendees can expect to gain a deeper understanding of how techniques such as deep learning, reinforcement learning, and graph neural networks are being adapted for financial time series, anomaly detection, and portfolio optimization.
What to Expect
KDD-MLF 2026 will feature a program centered on the workshop’s four key themes. While the full agenda and speaker list are pending announcement, the workshop traditionally includes:
- Paper presentations covering novel ML methodologies applied to financial problems, with peer-reviewed research from academic and industry contributors.
- Panel discussions on topics such as model interpretability in regulated environments, data privacy in financial ML, and the challenges of deploying models in live trading systems.
- Networking sessions designed to foster collaboration between researchers and financial technology practitioners.
- Keynote talks from invited experts in quantitative finance and machine learning (specific speakers to be announced).
The workshop will also provide a platform for discussing emerging trends, including the use of large language models for financial document analysis, adversarial robustness in fraud detection, and the integration of alternative data sources into forecasting models.
Who Should Attend
KDD-MLF 2026 is designed for a diverse audience including:
- Machine learning researchers interested in applying their work to financial domains or seeking challenging real-world datasets.
- Quantitative analysts and data scientists working at hedge funds, investment banks, fintech companies, and insurance firms.
- Risk management professionals looking to incorporate advanced ML techniques into credit scoring, market risk modeling, and operational risk frameworks.
- Fraud detection specialists seeking the latest methods for identifying financial crime and money laundering patterns.
- AI product managers and engineering leaders evaluating ML solutions for financial applications.
- Graduate students and academics pursuing research at the intersection of machine learning and finance.
How to Register
Registration for KDD-MLF 2026 is handled through the main KDD 2026 conference portal. Since the workshop is co-located with KDD, attendees typically need to register for the main conference to access the workshop, though day-pass options may be available. Pricing details, early-bird deadlines, and registration links will be posted on the official KDD 2026 website as the event approaches. For the most current information, visit the workshop’s dedicated page on WikiCFP or the main KDD conference site. Details to be announced regarding specific registration fees and deadlines.
