Segmentation is a critical aspect in the development of credit risk models. This process involves identifying and justifying the number of scorecards to effectively manage different population segments in credit risk assessment. Here are the key points explaining why segmentation is essential:
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Business and Statistical Drivers for Segmentation:
- Business Drivers: These include marketing strategies like product offerings, demographic-based customer treatments, and data availability which differs across marketing channels or customer groups.
- Statistical Drivers: This involves ensuring a sufficient number of observations, including both 'good' and 'bad' accounts in each segment, and recognizing the existence of interaction effects where predictive patterns vary across segments.
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Segmentation Process:
- Initial Steps: The process starts with a pre-assessment during the business insights analysis to identify heterogeneous population segments.
- Segmentation Methods: It involves both supervised segmentation (using decision trees or ensemble model residuals) and unsupervised segmentation (like clustering). The goal is to identify different segments and capture interaction effects.
- Model Building: Separate models are developed for each segment.
- Assessment: The effectiveness of segmented models is assessed based on their predictive patterns and the lift in predictive power they offer compared to a single model.
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Iterative Nature of Segmentation:
- Segmentation is iterative, requiring constant judgment to decide between single or multiple segments. It's noted that segmentation often does not result in significant improvement, leading to a preference for a single scorecard.
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Alternatives to Segmentation:
- Instead of separate scorecards, alternatives like adding additional variables in logistic regression or identifying the most predictive variables per segment for a combined single model can be used.
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Parent/Child Model Approach:
- In cases of unreliable model factors, a parent/child model approach can be adopted. Here, a parent model focuses on common characteristics and feeds its output as a predictor into child models which cater to unique characteristics across segments.
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Purpose of Multiple Scorecards:
- The primary aim is to improve the quality of risk assessment. Segmented scorecards should be used only if they offer significant business value that justifies the higher development and implementation costs, increased complexity in decision management, additional scorecard management, and greater IT resource utilization.
In summary, segmentation in credit risk model development is essential for tailoring risk assessment to diverse customer groups and ensuring the models are sensitive to varying predictive patterns across these groups. However, the approach is balanced by considerations of practicality, cost, and effectiveness, often leading to a preference for single scorecard models when possible.