Minimizing credit risk is one of the major concerns for financial institutions, credit risk is mostly associated with the possibility of an individual failing to meet contractual obligations such as debts on credit cards on mortgages. Due to these financial institutions, venture capital relies on technology to understand the behavior or predict the client’s purchase history and classify them as a defaulter or a payer. Credit risk analytics can be considered as a methodology to understand a client’s behavior based on the purchases, timely payments, income, etc.
Credit risk modeling and analytics can be based on either of the methods listed below
Machine Learning Modeling
3. Machine Learning Modeling
Machine Learning models help financial credit companies to improve the efficiency of their risk analysis, Models help provide a scientific solution to predict potential debtors/churners in advance.
Machine learning models using Amazon Sagemaker.
Amazon Sagemaker provides a full-fledged service that helps Data Analysts and developers quickly train and deploy machine learning models. It includes a number of modules that can be used individually or together to build machine learning models.
Amazon Sagemaker Studio provides a lot many tools from Jupyter notebooks to Pre-built open-source models that help in building, scaling, and testing mathematical models at a faster pace. The process from EDA to training a model can be combined in a single unit and deployed easily within clicks. Some models that perform the best on churn prediction are as stated below(They depend on the dataset features).
The Credit Risk Dashboard helps financial loan /credit organizations to manage the bank’s credit risk profile. The Analytical dashboard help in visualizing potential churners and defaulters.
Analytical Dashboards help drill down to analyze factors where the payers show similarity and segment customers based on the generalized view charts. For example, a customer having high income and low credit utilization shows a lower default probability as compared to a person with low income and high credit utilization.
Amazon provides a Quicksight as a platform to visualize datasets and plot maps and graphs which help in segregating customers and generalizing the areas of spend which would give a broader view of what segment of customers turn into defaulters.
QuickSight is a cloud-based business intelligence (BI) service. It helps to deliver easy-to-understand insights from a variety of datasets. Amazon QuickSight connects to your data in S3 or similar other cloud storage.
For a quick start, one can use the uploaded CSV file in the s3 bucked and grant permissions to read it to Quicksight. A manifest file (text file in JSON format that Amazon Redshift uses to connect to the host) will take care of the connections and help you get started.
The following figure demonstrates the architecture of importing data to displaying data in Quicksight please follow the below diagram:
With the help of AWS Sagemaker, we could perform Predictive analysis on data by using an appropriate Machine Learning Algorithm and Evaluating the model. This would help significantly in predicting defaulters based on past historic purchasing data.
AWS Quicksight helps considerably in plotting graphs with the dataset. This would help visualize patterns and features that are of higher importance for the prediction. Plotting graphs could discover patterns and attributes which could be more helpful in segmenting customers based on spending.
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The maximum level of risk that an individual can take based on their financial circumstances define their risk capacity. This is a quantification of the client’s total ability to absorb a loss, whether the loss is small, moderate, or large. The second part is risk requirement, where the client can discuss their investment objective with the advisor to determine the risk that can be taken to meet the investment return objective.
What is a stress test?
Stress testing techniques include generating scenarios, translating them into environment parameters, assessing the impact on the market, and executing to mitigate risks and leverage newer opportunities.
Bineet Singh Kushwah works as a Sr. Research Associate at CloudThat. His work revolves around data engineering, analytics, and machine learning projects. He is passionate about providing analytical solutions for business problems and deriving insights to enhance productivity. In a quest to learn and work with recent technologies, he spends the most time on upcoming data science trends and services in cloud platforms and keeps up with the advancements.