Open Access. Powered by Scholars. Published by Universities.®
- Discipline
-
- Analysis (1)
- Applied Statistics (1)
- Artificial Intelligence and Robotics (1)
- Business Analytics (1)
- Computer Engineering (1)
-
- Computer Sciences (1)
- Data Storage Systems (1)
- Databases and Information Systems (1)
- Engineering (1)
- Finance and Financial Management (1)
- Management Sciences and Quantitative Methods (1)
- Mathematics (1)
- Numerical Analysis and Scientific Computing (1)
- Physical Sciences and Mathematics (1)
- Portfolio and Security Analysis (1)
- Programming Languages and Compilers (1)
- Statistical Models (1)
- Statistics and Probability (1)
- Technology and Innovation (1)
- Theory and Algorithms (1)
Articles 1 - 2 of 2
Full-Text Articles in Insurance
Modeling And Application Of Neural Networks For Automotive Damage Appraisals, Fred Poon, Yang Zhang, Jonathon Roach, David Josephs, John Santerre
Modeling And Application Of Neural Networks For Automotive Damage Appraisals, Fred Poon, Yang Zhang, Jonathon Roach, David Josephs, John Santerre
SMU Data Science Review
The automotive damage appraisal process is one of the areas in property and casualty insurance that can benefit from applying deep learning technology and computer vision. It is commercially beneficial to introduce a fast and efficient claim process that can shorten the entire process. Technologies adopted include advanced neural network algorithm and Mask R-CNN to solve tasks such as image classification, object detection, and segmentation in combination with statistical analysis and model construction of the appraisal metadata to approximate final claim cost. With a database of over 3 million records as the data source, a workflow is constructed via a …
Improving Vix Futures Forecasts Using Machine Learning Methods, James Hosker, Slobodan Djurdjevic, Hieu Nguyen, Robert Slater
Improving Vix Futures Forecasts Using Machine Learning Methods, James Hosker, Slobodan Djurdjevic, Hieu Nguyen, Robert Slater
SMU Data Science Review
The problem of forecasting market volatility is a difficult task for most fund managers. Volatility forecasts are used for risk management, alpha (risk) trading, and the reduction of trading friction. Improving the forecasts of future market volatility assists fund managers in adding or reducing risk in their portfolios as well as in increasing hedges to protect their portfolios in anticipation of a market sell-off event. Our analysis compares three existing financial models that forecast future market volatility using the Chicago Board Options Exchange Volatility Index (VIX) to six machine/deep learning supervised regression methods. This analysis determines which models provide best …