Credit Risk Analytics with Leverage AWS Machine Learning Services
TABLE OF CONTENT
1. Introduction to Credit Risk Analogy 2. Methodology3. Machine Learning Modelling4. Analytical Dashboards5. Conclusion6. CloudThat 7. FAQs1. Introduction to Credit Risk Analogy
Financial institutions are concerned about minimizing credit risk. Credit risk is mainly associated with the possibility that an individual fails to fulfill contractual obligations, such as mortgage or credit card debts. Venture capital uses technology to predict and understand client behavior and classify them as defaulters or payers. Credit risk analytics can be used to analyze a client’s behavior using income, purchases, and timely payments.
One of the two methods below can be used to model credit risk and do analytics.
Machine Learning Modeling
3. Machine Learning Modeling
Financial credit companies can use Machine Learning models to improve their risk analysis efficiency. Models provide a scientific method to predict potential debtors/churners ahead of time.
Amazon Sagemaker is a machine learning platform.
Amazon Sagemaker is a fully-fledged service that allows Data Analysts and Developers to quickly train and deploy machine-learning models. It comes with a variety of modules that can be used together or individually to build machine-learning models.
Amazon Sagemaker Studio offers many tools, including Jupyter notebooks and pre-built open-source models. This allows you to build, scale, and test mathematical models faster. It is easy to combine the EDA and training of a model process into one unit that can be deployed in just a few clicks. These models perform best in churn prediction. They depend on the features of the dataset.
Below is an architecture diagram showing the flow of data between S3 (Simple Storage Area) and Sagemaker.
4. Analytical Dashboards
The Credit Risk Dashboard assists financial loan/credit organizations in managing their bank’s credit risk profile. The Analytical dashboard helps in visualizing potential defaulters and churners.
Analytical Dashboards allow you to drill down and analyze factors where payers have similarity. They also help segment customers based upon the generalized view charts. A customer with high credit utilization and high income has a lower default rate than a customer with low credit utilization and high income.
Amazon offers Quicksight, a platform that allows you to visualize data and plot maps and graphs. This helps in segregating customers as well as generalizing spend areas. This would give you a better view of which customers become defaulters.
QuickSight, a cloud-based service for business intelligence (BI), is available. It provides easy-to-understand insights based on a variety data sources. Amazon QuickSight connects with your data stored in S3 and similar cloud storage.
You can upload a CSV file to the s3 bucket and grant Quicksight permissions to access it. The Amazon Redshift manifest file (a text file in JSON format that Amazon Redshift uses for connecting to the host) will handle the connections and help you get going.
The following diagram shows how Quicksight displays data from import to display.
AWS Sagemaker allows us to perform predictive analysis on data using a suitable Machine Learning Algorithm, and then evaluate the model. This would be a great way to predict defaulters based upon past purchasing data.
AWS Quicksight is a great tool for plotting graphs using the dataset. This would allow you to visualize patterns and features that are more important for the prediction. The ability to plot graphs could help you identify patterns and attributes that could be more useful in your prediction.