Machine Learning in Business Workshop

Summary

Come learn the latest on the use of machine learning in business!  This free workshop will feature discussions of early research by academic experts in the field and a lively panel discussion with industry experts. The goal is to generate synergies, produce collaborations, benefit HDR students and attract scholars and practitioners from within the School, other faculties and universities.

Hosted by:
Business Financing and Banking Research Group
Productivity, Efficiency and Measurement Analytics Research Group
Time Series and Forecasting Research Group

Program

8:30-9:00 Coffee

9-10:30 Session 1 - ML in time series and forecasting | Chair: Boris Choy

Andrew Patton, Duke: Bespoke Realized Volatility: Tailored Measures of Risk for Volatility Prediction
Chao Wang, USyd: Realized recurrent conditional heteroskedasticity model for volatility modelling
Minh-Ngoc Tran, USyd: Machine Learning for financial volatility modelling: some existing results and future directions

10:30-11:00 Coffee

11:00-12:30 Panel Discussion, moderated by Marcel Scharth

Tariq Scherer, Executive Manager – Data Science | Analytics, Data & Decision Science | BB Everyday Business Banking | CBA – TBC
Tiberio Caetano, Chief Scientist | Gradient Institute – confirmed
Dimitri Semenovich, Head of Analytics | Cover Genius – TBC
Barrett Hasseldine, Data Science Leader | Machine Learning & Analytics | Ilion AU&NZ - TBC

12:30-1:30 lunch

1:30-3:00 Session 2 - ML for productivity and efficiency | Chair: Peter Radchenko

Artem Prokhorov, USyd: Does technical inefficiency exist: a machine learning perspective
Robert James, USyd & NGS Super: Improving estimates of technical inefficiency of production using local linear forests
Andrew Grant, USyd: Machine Learning in Customer Lending

3:00-3:30 Coffee

3:30-5:00 Session 3 - ML for business financing and banking | Chair: Eliza Wu

Talis Putnins, UTS: Market efficiency in the age of machine learning
Adrian Gao, USyd: Anomalous lending and bank risk
Yunying Huang, USyd: A GNN-based bank risk measure.

5:00-7:00 Reception