EDA using python, to explore the driving factors behind the delayed payments of loan, on credit data of Gold Atlantis Bank.

1. Banking Credit Risk:
Exploratory Data Analysis
Rishika Khanna

2. Objective
To analyze the credit data of Gold Atlantis, to explore the driving factors behind the
delayed payments of loan, i.e. the variables which are strong indicators of default.
Further, the company can utilize this knowledge for its portfolio and risk assessment with
respect to loan sanctions.

3. Criteria for Analysis- Direct
A] Direct Factors
• The borrower’s capacity to repay the loan -income relative to loan amount.
• Borrower’s assets.
• History of loan repayment.

4. Criteria for Analysis- Indirect
B] Indirect Factors
• Gender.
• Level of education.
• Occupation of the client.
• Family status of the client.
• Family members/children.
• Employment history.

5. What % of clients have difficulty with on-time payments?

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7. Analysis: On-Time payments

8.  Clients who are Married/Widowed are more likely to make On-time payments.
In terms of Family Status, which clients are more likely to make On-Time payments?

9.  Commercial assoc., Pensioners, State servants are more likely to make on-time
payments.
In terms of Income Type, which clients are less likely to make On-Time payments?

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11.  CNT_CHILDREN =0 there are more clients with on-time payments.
CNT_CHILDREN=1/2, the count of on-time payments reduces, but remains higher than
delayed payments.
In terms of Children_Count, which clients are more likely to make On-Time payments?

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13.  Female clients in almost all occupations, are more likely to make payments On-Time.
In terms of occupation, which clients are more likely to make On-Time payments?

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15.  Female clients who are working, are more likely to make payments On-Time.
In terms of Income Type, which clients are more likely to make On-Time payments?

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17.  Female clients who are educated are more likely to make On-Time payments
In terms of education, which clients are more likely to make On-Time payments?

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19. In terms of number of employment years, which clients are more likely to make On-Time
payments?
 Clients who are employed for > 15 years.

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21. In terms of AMT_GOODS_PRICE v/s AMT_INCOME_TOTAL, which clients are more likely to
make On-Time payments?
 Area with AMT_INCOME_TOTAL > 2,50,000 and AMT_GOODS_PRICE > 5,00,000, is more dense in On time
analysis chart. And it is sparse in with delay analysis chart.
 So, we can say that clients AMT_INCOME_TOTAL > 2,50,000 and AMT_GOODS_PRICE > 5,00,000 are more
likely to make On-Time payments.

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23. Analysis: delayed payments

24.  Default rate is higher in general, for Cash loans for both males and females.
In terms of gender & Loan type, which clients are more likely to make delayed payments?

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26.  Male clients, are likely to make delayed payments.
In terms of gender, which clients are more likely to make delayed payments?

27.  Unemployed, Working class clients, are likely to make delayed payments
In terms of income-type, which clients are more likely to make delayed payments?

28.  Assets: Clients who do not own a house/car, are more likely to make delayed payments.
In terms of Assets, which clients are more likely to make delayed payments?

29.  Clients who are Labourers, Salesman, Drivers are more likely to make delayed payments.
In terms of Occupation, which clients are more likely to make delayed payments?

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31.  Medium & Low income clients are more likely to default.
In terms of income, which clients are more likely to make delayed payments?

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33.  AMT_CREDIT is highest among clients with Academic degree & higher ed., esp. Females.
Academic degree clients don't show up on the Defaulter's graph, they must be making payments on-
time.
What effect does Education with respect to AMT_CREDIT & Gender, have on business,
in case of defaults?

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35.  AMT_CREDIT is highest in VeryHigh income groups.
And lowest among VeryLow income groups.
Among all category AMT_CREDIT is higher in Females.
What effect does Income with respect to AMT_CREDIT & Gender, have on business,
in case of defaults?

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37.  Loans in Very High income group has high amount credit & Amt_Goods_price.
This could lead to loss for business, in case of default.
Amt_credit not correspondingly equivalent to Amt_Goods_price – is a concern
What effect does relationship of IncomeGroup with AMT_CREDIT & AMT_GOODS_PRICE have on
business, in case of defaults?

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39. Conclusion

40. With respect to On-Time payments, following client categories can be preferred:
• Clients who are employed for >= 20 years.
• Female clients who are working.
• Female applicants should be given extra weightage as defaults are lesser.
• Clients who are Married/Widowed.
• Female & Male clients with Academic degree.
• Commercial associates, Pensioners, State servants are likely to repay On-Time.

41. With respect to Delayed payments , following client categories can be avoided:
• Medium & Low income, tend to make delayed payments.
• Male in general, tend to make delayed payments.
• Unemployed clients.
• Labourer, Salesman, Drivers, tend to make delayed payments.
• Clients who don’t own House/Car, tend to make delayed payments.
• Loans in Very High income group have high credit amount &
Amt_Goods_price. This could lead to loss for business, in case of default.
• Amt_credit not correspondingly equivalent to Amt_Goods_Price, can be a
concern for business, in case of default.

42. Thankyou.