Credit Risk Segmentation

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Website/App Usage: How frequently do they log in and what features do they use?

Credit Limit Utilization: How much of their available credit do they use?
Cash Advance Activity: Indicates potential liquidity issues.
Card Features Used: Rewards redemption, insurance benefits, lounge access.


Credit Score: (e.g., Prime, Subprime, Near country email list Prime) – A fundamental indicator of creditworthiness.
Debt-to-Income Ratio: Measures a customer's ability to manage additional debt.
Number of Active Credit Accounts: Can indicate financial health or over-leveraging.
Length of Credit History: Generally, a longer history is more favorable.
The Segmentation Process: From Data to Actionable Insights
Implementing effective credit card customer segmentation involves several key steps:

Define Your Objectives: What do you hope to achieve with segmentation? (e.g., reduce churn, increase cross-selling, improve fraud detection).
Data Collection & Integration: Gather comprehensive data from various sources: transaction data, credit bureau data, application forms, customer service interactions, website analytics. Ensure data quality and consistency.
Data Analysis & Modeling: This is where the magic happens.
Descriptive Analytics: Understand current customer demographics and behaviors.
Clustering Algorithms: (e.g., K-Means, Hierarchical Clustering) are powerful tools to identify natural groupings within your data based on chosen variables.
Predictive Analytics: Build models to predict future behavior (e.g., likelihood of default, propensity to respond to an offer).
Segment Profiling: Once clusters are identified, give them names and create detailed profiles. What are their defining characteristics? What are their needs, pain points, and aspirations?