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To conduct a thorough exploratory data analysis (EDA) and deep analysis of a comprehensive dataset containing basic customer details and extensive credit-related information. The aim is to create new, informative features, calculate a hypothetical credit score, and uncover meaningful patterns, anomalies, and insights within the data.
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This casestudy expects a deep dive into bank details and credit data, creating valuable features, a hypothetical credit score, and uncovering hidden patterns. This involves thorough EDA, strategic feature engineering, model-driven score calculation, and insightful analysis that reveals factors influencing creditworthiness and guides potential risk mitigation strategies.
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Remember, your analysis isn't just about dissecting data but uncovering actionable insights. Create a credit score strategy that you think would be the best and mention your justifications for criteria, weightage for the features
Data Dictionary:
| Column Name | Description |
|---|---|
| ID | Represents a unique identification of an entry |
| Customer_ID | Represents a unique identification of a person |
| Month | Represents the month of the year |
| Name | Represents the name of a person |
| Age | Represents the age of the person |
| SSN | Represents the social security number of a person |
| Occupation | Represents the occupation of the person |
| Annual_Income | Represents the annual income of the person |
| Monthly_Inhand_Salary | Represents the monthly base salary of a person |
| Num_Bank_Accounts | Represents the number of bank accounts a person holds |
| Num_Credit_Card | Represents the number of other credit cards held by a person |
| Interest_Rate | Represents the interest rate on credit card |
| Num_of_Loan | Represents the number of loans taken from the bank |
| Type_of_Loan | Represents the types of loan taken by a person |
| Delay_from_due_date | Represents the average number of days delayed from the payment date |
| Num_of_Delayed_Payment | Represents the average number of payments delayed by a person |
| Changed_Credit_Limit | Represents the percentage change in credit card limit |
| Num_Credit_Inquiries | Represents the number of credit card inquiries |
| Credit_Mix | Represents the classification of the mix of credits |
| Outstanding_Debt | Represents the remaining debt to be paid (in USD) |
| Credit_Utilization_Ratio | Represents the utilization ratio of credit card |
| Credit_History_Age | Represents the age of credit history of the person |
| Payment_of_Min_Amount | Represents whether only the minimum amount was paid by the person |
| Total_EMI_per_month | Represents the monthly EMI payments (in USD) |
| Amount_invested_monthly | Represents the monthly amount invested by the customer (in USD) |
| Payment_Behaviour | Represents the payment behavior of the customer (in USD) |
| Monthly_Balance | Represents the monthly balance amount of the customer (in USD) |
Exploratory Data Analysis (EDA):
- Performing a comprehensive EDA to understand the data's structure, characteristics, distributions, and relationships.
- Identified and addressed any missing values, mismatch data types, inconsistencies, or outliers.
- Utilized appropriate visualizations (e.g., histograms, scatter plots, box plots, correlation matrices) to uncover patterns and insights.
Feature Engineering:
- Created new features that can be leveraged for the calculation of credit scores based on domain knowledge and insights from EDA
Hypothetical Credit Score Calculation:
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Developed a methodology to calculate a hypothetical credit score(kinda CIBIL/FICO ranges from 300-850,900) using relevant features (using a minimum of 5 maximum of 10 features) and justified it.
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Explored various weighting schemes to assign scores.
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Provided a score for each individual customer
References
- https://thedocs.worldbank.org/en/doc/935891585869698451-0130022020/original/CREDITSCORINGAPPROACHESGUIDELINESFINALWEB.pdf
- https://www.iifl.com/blogs/cibil-score/different-types-of-credit-score
- https://www.etmoney.com/learn/loans/difference-between-cibil-score-and-credit-score/
Also thought about:
- Can credit score and aggregated features be calculated at different time frames like the last 3 months/last 6 months (recency based metrics)
Analysis and Insights
- Added valuable insights from EDA and credit score calculation