Title: Multimodal AI/ML in FinTech

Speaker:    Sanjiv Das
Date and Time: Friday, Oct. 28 from 11 a.m. – 12:30 p.m.
Location:  Room BB 3.03.02

Sanjiv Das is the William and Janice Terry Professor of Finance and Data Science at Santa Clara University’s Leavey School of Business, and an Amazon Scholar at AWS. He previously held faculty appointments at Harvard Business School (Assistant and Associate Professor) and UC Berkeley (Visiting Associate Professor). He holds post-graduate degrees in Finance (M.Phil and Ph.D. from New York University) and Computer Science (M.S. from UC Berkeley).

 Abstract: This seminar will describe the evolving landscape of multimodal machine learning and its role in financial data science, with examples from recent research, placed within a framework for industrialized AI/ML.

Speaker Notes and Resources

The talk will be about a collection of work I have done at Amazon, demonstrating how AI/ML is improving on econometric models, specifically through the use of multimodal data. There are many papers I will cover and also if possible, I will demo some tools.

1. Context, Language Modeling, and Multimodal Data in Finance: https://jfds.pm-research.com/content/3/3/52
2. Multimodal Machine Learning for Credit Modeling: https://www.amazon.science/publications/multimodal-machine-learning-for-credit-modeling
3. On the Lack of Robust Interpretability of Neural Text Classifiers: https://www.amazon.science/publications/on-the-lack-of-robust-interpretability-of-neural-text-classifiers
4. FinLex: An Effective Use of Word Embeddings for Financial Lexicon Generation: https://www.sciencedirect.com/science/article/pii/S2405918821000131
5. Credit Modeling with Graph Machine Learning: https://aws.amazon.com/blogs/machine-learning/build-a-corporate-credit-ratings-classifier-using-graph-machine-learning-in-amazon-sagemaker-jumpstart/

The content of these papers has been deployed for use, this may be of interest to faculty and PhD students who want to get into multimodal ML in finance, resources are here:

1. Smjsindustry SDK: https://pypi.org/project/smjsindustry/
   2. ReadTheDocs: https://sagemaker-jumpstart-industry-pack.readthedocs.io/en/latest/notebooks/index.html
   3. Github Repo: https://github.com/aws/sagemaker-jumpstart-industry-pack/
   4. Official SageMaker doc: https://docs.aws.amazon.com/sagemaker/latest/dg/studio-jumpstart-industry.htmlGet started by going to JumpStart using SageMaker Studio: https://aws.amazon.com/sagemaker/getting-started/

Blogs related to this work:

1. Use SEC text for ratings classification using multimodal ML in Amazon SageMaker JumpStart : https://aws.amazon.com/blogs/machine-learning/use-sec-text-for-ratings-classification-using-multimodal-ml-in-amazon-sagemaker-jumpstart/
2. Use pre-trained financial language models for transfer learning in Amazon SageMaker JumpStart : https://aws.amazon.com/blogs/machine-learning/use-pre-trained-financial-language-models-for-transfer-learning-in-amazon-sagemaker-jumpstart/
3. Create a dashboard with SEC text for financial NLP in Amazon SageMaker JumpStart: https://aws.amazon.com/blogs/machine-learning/create-a-dashboard-with-sec-text-for-financial-nlp-in-amazon-sagemaker-jumpstart/
4. Graph Based Credit Scoring: https://aws.amazon.com/blogs/machine-learning/build-a-corporate-credit-ratings-classifier-using-graph-machine-learning-in-amazon-sagemaker-jumpstart/

I will also reference some other papers that apply AI/ML to financial applications in market prediction, venture capital, etc. Folks can visit my research at:

1. https://srdas.github.io/research.htm
2. https://www.amazon.science/author/sanjiv-das