Cracking the Code Understanding and Overcoming Bias in Credit Scoring Algorithms
| AUTHOR | Kovilan |
| PUBLISHER | Self (12/07/2023) |
| PRODUCT TYPE | Paperback (Paperback) |
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Technical Features and Mathematical Foundations of Credit Scoring Models
Credit scoring models are the foundation of modern financial services. They help lenders assess a person's ability to repay a loan and make lending decisions. These models are built on mathematical analysis, and they play a critical role in financial markets.
Technical Features
Credit scoring models typically use a variety of data points to assess a borrower's creditworthiness. These data points can include:
Demographics: Age, gender, marital status, education level, employment status, etc.
Financial history: Credit history, debt-to-income ratio, payment history, etc.
Behavioral characteristics: Risk tolerance, willingness to pay, etc.
The models then use these data points to calculate a credit score. This score is a numerical representation of the borrower's creditworthiness. A higher credit score indicates a lower risk of default.
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Technical Features and Mathematical Foundations of Credit Scoring Models
Credit scoring models are the foundation of modern financial services. They help lenders assess a person's ability to repay a loan and make lending decisions. These models are built on mathematical analysis, and they play a critical role in financial markets.
Technical Features
Credit scoring models typically use a variety of data points to assess a borrower's creditworthiness. These data points can include:
Demographics: Age, gender, marital status, education level, employment status, etc.
Financial history: Credit history, debt-to-income ratio, payment history, etc.
Behavioral characteristics: Risk tolerance, willingness to pay, etc.
The models then use these data points to calculate a credit score. This score is a numerical representation of the borrower's creditworthiness. A higher credit score indicates a lower risk of default.