AI has been revolutionizing finance in many areas such as credit risk assessment, fraud detection, trading, process automation and financial advisory services.

In this case study, we will assume that you work as data scientists at a bank and you have been tasked to predict the probability that customers would default on their credit card payments

This dataset contains extensive information about the customers such as default payments, demographic factors, credit data, history of payment, and credit card bill statements.

In this project, we are going to use AWS sagemaker studio which is a brand-new tool by AWS that will truly impress you and makes your life 10x easier.

SageMaker Studio offers a web-based interface where all machine learning development stages such as model building, training, and deployment could be performed in one place resulting in an enhanced productivity.

SageMaker studio allows developers to manage "experiments" by automatically keeping track of model parameters, inputs and model artifacts in one place.

In this project, we will learn the following:
• Understand how to leverage the power of AI and ML in the finance industry by predicting the probability of credit card default.
• Learn how to use SageMaker Studio AutoML tool to build, train and deploy AI/Ml models which requires almost zero coding experience.
• Build, train and deploy XGBoost-based algorithm to perform regression tasks.
• Develop and manage experiments using AWS sageMaker Studio.
• Generate and view candidate generation notebook and data exploration notebooks.

I hope you will enjoy this case study and let’s apply AI/Ml to Finance!

Thanks

#sagemaker #awssagemaker #autopilot

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