Machine Learning for Financial Services
This course will explore the conceptual aspects of applying machine learning to problems in the financial services industry and discuss case studies of machine learning used in financial services.
What you'll learn
Analytical and statistical models are already an integral part of the finance industry and the use of machine learning builds on a strong foundation in this industry. The financial services industry is uniquely positioned to leverage machine learning because of the vast quantities of high-quality data already available.
In this course, Machine Learning for Financial Services, you will explore machine learning techniques currently applied in the financial services industry. First, you will look at some examples and cases of where ML is already being used in financial services - for investment predictions, loan automation, process automation, and fraud detection. Then, you will develop an intuitive understanding of how recurrent neural networks
Next, you will explore two ML case studies from research papers - the first focusing on assessing and quantifying the return on investment and the second exploring how classification and clustering models can help detect money laundering.
Finally, you will get hands-on coding and see how you can use a classification model for fraud detection on a synthetically generated dataset.
When you are finished with this course, you will have the awareness of how machine learning can be applied in the financial services industry and hands-on experience working with financial data.
Table of contents
- Version Check 0m
- Prerequisites and Course Outline 2m
- Data and Analytics Trends in Finance 6m
- Use Cases of ML in Finance: Investment Predictions 5m
- Use Cases of ML in Finance: Loan Automation 3m
- Use Cases of ML in Finance: Process Automation 4m
- Use Cases of ML in Finance: Robo Advisors 3m
- Use Cases of ML in Finance: Fraud Detection 3m
- Recurrent Neural Networks for Financial Data 7m
- Challenges of ML in Finance 6m
- Classification Use Cases 2m
- Accuracy, Precision, and Recall 5m
- Demo: Fraud Detection - Data Exploration and Preparation Part I 5m
- Demo: Fraud Detection - Data Exploration and Preparation Part II 6m
- Demo: Fraud Detection - Classification Models 5m
- Demo: Fraud Detection - ROC Curves and AUC 4m
- Summary, Resources Used, and Further Study 1m
About the author
Janani has a Masters degree from Stanford and worked for 7+ years at Google. She was one of the original engineers on Google Docs and holds 4 patents for its real-time collaborative editing framework. After spending years working in tech in the Bay Area, New York, and Singapore at companies such as Microsoft, Google, and Flipkart, Janani finally decided to combine her love for technology with her passion for teaching. She is now the co-founder of Loonycorn, a content studio focused on providing ... more