MIDASMIDAS
The Seventh Workshop on MIning DAta for financial applicationS
September
23, 2022 - Grenoble,
France - Hybrid Event
in
conjunction with
ECML-PKDD 2022
European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases
September 19-23, 2022 - Grenoble, France - Hybrid Event
We invite submissions to the 7th MIDAS Workshop on MIning DAta for financial applicationS, to be held in conjunction with ECML-PKDD 2022 - European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases.
Like the famous King Midas, popularly remembered in Greek
mythology for his ability to turn everything he touched with his
hand into gold, we believe that the wealth of data generated by
modern technologies, with widespread presence of computers,
users and media connected by Internet, is a goldmine for
tackling a variety of problems in the financial domain.
Nowadays, people's interactions with technological systems provide us with gargantuan amounts of data documenting collective behaviour in a previously unimaginable fashion. Recent research has shown that by properly modeling and analyzing these massive datasets, for instance representing them as network structures it is possible to gain useful insights into the evolution of the systems considered (i.e., trading, disease spreading, political elections).
Investigating the impact of data arising from today's application domains on financial decisions may be of paramount importance. Knowledge extracted from data can help gather critical information for trading decisions, reveal early signs of impactful events (such as stock market moves), or anticipate catastrophic events (e.g., financial crises) that result from a combination of actions, and affect humans worldwide.
The importance of data-mining tasks in the financial domain has been long recognized. Core application scenarios include correlating Web-search data with financial decisions, forecasting stock market, predicting bank bankruptcies, understanding and managing financial risk, trading futures, credit rating, loan management, bank customer profiling.
The MIDAS workshop is aimed at discussing challenges, potentialities, and applications of leveraging data-mining tasks to tackle problems in the financial domain. The workshop provides a premier forum for sharing findings, knowledge, insights, experience and lessons learned from mining data generated in various application domains. The intrinsic interdisciplinary nature of the workshop constitutes an invaluable opportunity to promote interaction between computer scientists, physicists, mathematicians, economists and financial analysts, thus paving the way for an exciting and stimulating environment involving researchers and practitioners from different areas.
PAST EDITIONS
We encourage submission of papers on the area of data mining for financial applications. Topics of interest include, but are not limited to:
WORKSHOP FORMAT
The ECML-PKDD 2022 conference and all its satellite events -- including MIDAS 2022 -- will take place according to a hybrid modality.
INVITED SPEAKER
Speaker: Jose A Rodriguez-Serrano, Esade Business School
Title: Modernizing Banking and Finance with Machine Learning: techniques, successes and challenges
Abstract:
Machine learning (ML) adoption in the banking and fintech industry is accelerating and recently demonstrating success beyond the old-school applications of e.g. risk and fraud. This leads to a modernization of the industry with interesting advances such as more interactive apps, proactive advice, or shift in customer relation models. But adoption still feels slow-paced, opportunistic, and unevenly distributed, not systemic; and the sector is still undergoing a learning process.
This talk will review how ML has the potential to become an horizontal enabling layer in several non-traditional domains of banking and finance (such personalization, personal and corporate customer relations, or process optimization). This will include a review of relevant ML techniques (e.g. forcasting with uncertainty, or graph-based methods), real examples of known success cases, and also discussions of relevant challenges and cultural shifts that need to happen in this industry. The talk will also draw from the speaker’s personal experience in research and applied data science in an AI banking lab during the last 7 years.
PROGRAM