Fourth International Workshop on Modelling Uncertainty in the Financial World (MUFin'24)

Call For Papers

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