Credit scoring is perhaps one of the most "classic" applications for predictive modeling, to predict whether or not credit extended to an applicant will likely result in profit or losses for the lending institution. There are many variations and complexities regarding how exactly credit is extended to individuals, businesses, and other organizations for various purposes (purchasing equipment, real estate, consumer items, and so on), and using various methods of credit (credit card, loan, delayed payment plan). But in all cases, a the lender provides money to an individual or institution and expects to be paid back in time with interest commensurate with the risk of default.
BUSINESS OBJECTIVES:
- Marketing Aspect
- Application Aspect
- Performance Aspect
- Bad Debt Management
To address the business objectives related to Marketing, Application, Performance, and Bad Debt Management, I chose the following study questions. Here’s why each question was selected and how it relates to the respective business aspects:
- Marketing Aspect: Question 3: “What is the distribution of customers by age group and their default rates?”
Understanding the distribution of customers by age group and their default rates is crucial for tailoring marketing strategies. Different age groups have distinct financial behaviors and needs. By identifying which age groups have higher default rates, marketing efforts can be directed towards educating and supporting these groups, developing targeted financial products, and designing campaigns that resonate with their specific circumstances and challenges.
- Application Aspect:
Question 1: “What is the average income of customers who have defaulted compared to those who have not?”
Question 5: “What is the average credit-to-debt ratio for different income brackets?”
Income level and credit-to-debt ratios are key factors in the application process for credit products. By examining the average income of defaulters versus non-defaulters and the credit-to-debt ratio across different income brackets, lenders can refine their application criteria to better assess the risk associated with applicants. This helps in creating more accurate and fair credit scoring models and ensures that the credit products offered align with the financial capacity of different income segments.
- Performance Aspect:
Question 2: “How does the debt-to-income ratio vary across different education levels?”
The debt-to-income ratio is a significant indicator of a customer’s ability to manage debt. By analyzing how this ratio varies across education levels, we can gain insights into the financial performance and stability of different segments. This understanding allows for the development of performance metrics that take into account the educational background, leading to more personalized and effective financial advice and products that enhance overall financial health and performance.
- Bad Debt Management:
Question 4: “Is there a significant difference in work experience between customers who default and those who don’t?”
Work experience is often correlated with job stability and income level, both of which impact a person’s ability to manage debt. By investigating the difference in work experience between defaulters and non-defaulters, lenders can identify potential risk factors associated with limited work experience. This information can be used to develop support programs for customers with less work experience, implement more stringent evaluation criteria for high-risk profiles, and create strategies to mitigate the risk of bad debt.
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