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Day 1: MATLAB for Artificial Intelligence in Computational Finance


Artificial Intelligence holds a lot of potential for the Finance Industry. However, while interest is high, many questions remain around explainability and governance. In Day 1 of the MATLAB for Artificial Intelligence in Computational Finance series, we tackle questions around using natural language processing (NLP) and Neural Networks in computational finance.

In the first session, we will provide a specific case study around how a tier 1 bank was able to successfully build high impact NLP-based AI models while balancing the challenges of transparency, scalability and governance. NLP and AI represent an opportunity to impact the bottom line of financial institutions when implemented with governance in mind.  Some common tasks that stand to gain the most from such advances in modeling include automation of traditional business workflows such as identifying new investment opportunities, risk managing portfolios, predicting fraudulent activities, enhancing the customer experience, and even more robust forecasting of markets.

The second presentation is a live demonstration and code walk-through to get started in using neural networks for developing credit risk models.  Deep learning is rapidly growing in popularity in computational finance and MathWorks have developed a framework to simplify the adoption of deep learning by finance professionals.  This tech deep dive walks through the process of creating, testing and evaluating a model for both use and explainability.

Time (AEDT) Topic
12:00 pm Overview 
12:05 pm Why top financial institutions fail at Artificial Intelligence
12:45 pm Using Neural Networks for developing credit risk models 
1:20 pm Q&A

About the Presenter

Dr Peter Brady is a Senior Application Engineer with a background in numerical simulation, analysis and high-performance computing.  Peter covers these areas of the MathWorks products as well as machine learning, deep learning and deployment.  He covers the computational finance industry and has worked with numerous customers in areas from data access, to algorithm development and deployment. Peter has a PhD in Mechanical Engineering and a Bachelors in Civil Engineering both from UTS and is a Chartered Practicing Engineer with Engineers Australia (CPEng NER).

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