EDA (Bank Case Study)

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University of Melbourne *

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90009

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Oct 30, 2023

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Bank Loan EDA Case Study BY ANISH CHAUHAN AND HIMANSHU SHARMA
Problem Statement This contextual analysis expects to recognize patterns which show if a customer experiences issues paying their portions which might be utilized for taking actions such as denying the loan, reducing the amount of loan, lending (to risky applicants) at a higher interest rate, etc, and so forth This will guarantee that the consumer is capable of repaying the loan are not rejected. Identification of such applicants using EDA is the aim of this case study.
Analysis Structure Analysis done on multiple metrics of applications database with respect to target variable. • Target 1 : Customer with payment difficulties in past (Defaulters) • Target 0 : Customers who paid on time (Non - defaulters) Types of analysis: • Univariate on applications data • Bivariate on applications data • Univariate on current + previous applications data • Bivariate on current + previous applications data
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Univariate Analysis - Current Applications Data
Gender Both genders are nearly equally like to default
Income Type Due to imbalance in income type distribution between defaulters and non-defaulters, defaulters are more likely to be working than non defaulters
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Goods Price Applicants with goods price of Rs 4-5 lacs are most likely to default *1e6
Age Applicant above the age of 68 are least likely to default.
Correlation of numerical variables Feature 1 Feature 2 AMT_GOODS_PRICE AMT_CREDIT AMT_ANNUITY AMT_GOODS_PRICE AMT_CREDIT AMT_ANNUITY AMT_INCOME_TOTAL AMT_ANNUITY AMT_INCOME_TOTAL AMT_GOODS_PRICE AMT_INCOME_TOTA AMT_CREDIT REGION_POPULATION_RELATIVE EXT_SOURCE_2 AMT_INCOME_TOTAL EXT_SOURCE_2 DAYS_REGISTRATION DAYS_EMPLOYED Majority of features for both defaulters and non defaulters were the same
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Bivariate Analysis Current Application Data
Credit Vs Annuity Amount • Higher correlation between credit amount and annuity amount for defaulters with most values being concentrated for credit amount less than Rs. 20 lacs while limit is 25 lacs for non-defaulters. • Therefore, people with high annuity for credit amount < 20lacs are more likely to default on payments
Age Vs Income Applicants with income < Rs. 3lac are more likely to default on payments across all age groups
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Income Vs Education Type Applicants holding an academic degree with income over Rs. 3 lacs are highly likely to default on payments.
Income Vs Family Status Single applicants with income over 3 lacs and other applicants with income over 3.4 lacs are less likely to default on payments.
Income Type vs Mean Region - city rating • Applicants on maternity leave living in regions with rating >= 2 are likely to default payments • Businessmen, pensioners and students are unlikely to default on loan
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Education Type vs Ratio staying away from base city Applicants with an academic degree staying in a city different from the city of their permanent address are more likely to default on payments
Analysis Current merged with Previous Application Data
Goods Category 35% of previous loan applications for current applicants were to purchase mobiles
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Previous Contract Status 63% of previous applications of current applicants were approved
Product Combination vs default rate Applicants with following product combinations are most likely to default on payments: • Card Street • Cash Street : high • Cash Street : middle • Cash X-Sell : high
Goods category vs default rate • Applicants seeking loans for heavier investments like insurance, jewellery and vehicles are most likely to default on payments • Applicants seeking loan to purchase animals, tourism and fitness products are least likely to default
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Reject Reason vs default rate Applicants rejected for reason code SCOFR are most likely to default on their payments while code SYSTEM are least likely
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Reject Reason + Goods Category vs default rate • The following reason code + goods category pairs have shown 100% default rate so they should be avoided while giving loans: • HC – Insurance • SYSTEM – Consumer Electronics • SCO – Additional Service • SCOFR - Homewares
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Final Recommendations and Inference
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Collect data on category of goods that the applicant intends to get a loan on Do not provide loan to applicants previously rejected for following reason – goods category pair: • HC – Insurance • SYSTEM – Consumer Electronics • SCO – Additional Service • SCOFR – Homewares Provide loan with higher interest rate to following cases: • Holding an academic degree and working away from their base city • On maternity leave living in cities with rating >= 2 • Seeking to buy heavy investments like insurance, jewellery and vehicles Accept loan application with low interest rate for following cases: • Seeking to buy animals, tourism and fitness products • Businessmen, students and pensioners
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THE END
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