Third International Workshop on Modelling Uncertainty in the Financial World (MUFin'23)
MUFin'23@AAAI2023
13th February, 2023, Washington, DC, USA
MUFin'23@AAAI2023
13th February, 2023, Washington, DC, USA
[10:00 AM to 10:15 AM] Welcome and Opening
[10:15 AM to 11:15 AM] Invited Talk – 1: "Modeling Uncertainty in Finance at J.P. Morgan AI Research", Dr. Tucker Balch, J.P. Morgan AI Research
[11:15 AM to 12:30 PM] Session-1 Paper Presentations
[11:15 – 11:30] Detection of Online Auction Fraud using Deep Learning Framework
[11:30 – 11:45] Flagging Payments for Fraud Detection: A Strategic Agent-Based Model
[11:45 – 12:00] Practical Bias Mitigation through Proxy Sensitive Attribute Label Generation
[12:00 – 12:15] Look Around! A Neighbor Relation Graph Learning Framework for Real Estate Appraisal
[12:15 – 12:30] Short-Term Volatility Prediction Using Deep CNNs Trained on Order Flow
[12:30 PM to 2:30 PM] Break
[2:30 PM to 3:30 PM] Session-2 Paper Presentations
[2:30 – 2:45] A Comparative Study of Elicitation Granularity in Prediction Markets Under Trader Misinformation
[2:45 – 3:00] Early Warning Systems? Building Time Consistent Perception Indicators for Economic Uncertainty and Inflation Using Efficient Dynamic Modeling
[3:00 – 3:15] On the Robustness of Stock Market Regressors
[3:15 – 3:30] A data mining framework for predicting Natural Gas prices in uncertain times
[3:30 PM to 4:30 PM] Invited Talk – 2: "Mitigating Uncertainty in Mission-Critical AI Systems", Dr. Anita Raj, Professor of Computer Science, Hunter College, The Graduate Center of The City University of New York
[4:30 PM to 5:00 PM] Open Challenges and Closing
Please refer to AAAI's website for the registration details: https://aaai.org/Conferences/AAAI-23/registration/ Early registration closes on 5th January!
Detection of Online Auction Fraud using Deep Learning Framework; Satyajit Panigrahi and Sharmila Subudhi (Siksha 'O' Anusandhan Deemed to be University, India; Maharaja Sriram Chandra Bhanja Deo University, Baripada, India)
Flagging Payments for Fraud Detection: A Strategic Agent-Based Model; Katherine Mayo, Shaily Fozdar, and Michael P. Wellman (University of Michigan, USA)
Practical Bias Mitigation through Proxy Sensitive Attribute Label Generation; Bhushan Chaudhari, Anubha Pandey, Deepak Bhatt, Darshika Tiwari (AI Garage, Mastercard, India)
Look Around! A Neighbor Relation Graph Learning Framework for Real Estate Appraisal; Chih-Chia Li, Wei-Yao Wang, Wei-Wei Du, and Wen-Chih Peng (National Yang Ming Chiao Tung University, Taiwan)
Short-Term Volatility Prediction Using Deep CNNs Trained on Order Flow; Mingyu Hao, and Artem Lenskiy (Australian National University, Australia)
A Comparative Study of Elicitation Granularity in Prediction Markets Under Trader Misinformation; Noah Lincke, Mithun Chakraborty, and Sindhu Kutty (Kodiak Robotics and University of Michigan, USA)
Early Warning Systems? Building Time Consistent Perception Indicators for Economic Uncertainty and Inflation Using Efficient Dynamic Modeling; Jonas Rieger, Nico Hornig, Tobias Schmidt, and Henrik Müller (Department of Statistics, TU Dortmund University, Germany and Institute of Journalism, TU Dortmund University, Germany)
Are You Investing in the Correct Stock? Benchmarking Robustness of Stock Market Regressors; Akshay Agarwal and Nalini Ratha (Department of Data Science and Engineering, IISER Bhopal, India and Department of Computer Science and Engineering, University at Buffalo, USA)
A data mining framework for predicting Natural Gas prices in uncertain times; Dadabada Pradeep Kumar (Information Systems and Analytics, Indian Institute of Management, Shillong, India)
Of many things, Covid-19 has provided a stark proof that uncertainty is real, and it is here to stay. Perhaps nothing is more sensitive to uncertainty than the Financial World. To couple with it, while Artificial Intelligence techniques are used to predict the future state of events, their performance is significantly impacted by disruptions not captured in the past. Unforeseen scenarios such as economy changes, variations in the customer behaviour, pandemics, recessions, and fraudulent transactions often result in unexpected behaviour of financial models, thus associating a level of uncertainty with them. It is thus imperative for the research community to explore, identify, analyze, and address such uncertainties in order to develop robust models applicable in real-world scenarios. To this effect, the goal of this workshop is to bring academics and industry experts together to discuss on this important, timely and yet-unsolved area of modelling uncertainties in the financial world.
We invite full papers (that have not been published before and nor are currently under consideration at some other venue) focused on modelling data uncertainty for financial applications. Topics of interest include, but are not limited to the following:
Application Topics:
- Evaluating financial risk
- Forecasting stock market
- Modelling seasonality in market trends
- Fraud prediction
- Modelling temporal social media activity
- Recommendation systems
Technical Topics:
- Temporal/Sequential data modelling – clustering, classification
- Modelling uncertainty in financial data
- Temporal graphs
- Time Series Forecasting
- Text analytics of financial reports, forecasts, and documents
- Explainable/interpretable sequential modelling
- Exploring fairness and robustness towards bias in financial models
- Representation learning from temporal/sequential data
- Modelling financial data as temporal point processes
The paper formatting must be consistent with that of AAAI'23 main conference research track. The link to the submission site is as follows: https://easychair.org/conferences/?conf=mufin23
Paper Submission: 30th November 2022
Paper Decisions: 18th December 2022
The best paper will be awarded a Best Paper Award worth $500! (sponsored by Mastercard)