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About Model Building in Creditcard and Loan Approval V
This document discusses credit risk model building. It outlines the typical 4 steps: 1) studying historical customer data to identify causes of default or charge-offs, 2) identifying the most impacting customer attributes, 3) using logistic regression to quantify the impact of each attribute, and 4) using the model to predict probabilities of default for new customers based on their attribute values. An example model is described that uses attributes like income, number of loans, payment history, credit utilization, age, and debt ratio to predict default probability. Other applications of model building discussed include fraud detection, employee attrition prediction, and marketing response modeling.
Detailed Information
| Author: | ['mike110*'] |
|---|---|
| Publication Year: | 2019 |
| Pages: | 24 |
| Language: | English |
| Format: | |
| Price: | FREE |
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