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Loan default predictions

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Problem Statement - I

Introduction: This case study aims to give you an idea of applying EDA in a real business scenario. In this case study, apart from applying the techniques that you have learnt in the EDA module, you will also develop a basic understanding of risk analytics in banking and financial services and understand how data is used to minimise the risk of losing money while lending to customers.

Business Understanding: The loan providing companies find it hard to give loans to the people due to their insufficient or non-existent credit history. Because of that, some consumers use it as their advantage by becoming a defaulter. Suppose you work for a consumer finance company which specialises in lending various types of loans to urban customers. You have to use EDA to analyse the patterns present in the data. This will ensure that the applicants are capable of repaying the loan are not rejected.

When the company receives a loan application, the company has to decide for loan approval based on the applicant’s profile. Two types of risks are associated with the bank’s decision:

If the applicant is likely to repay the loan, then not approving the loan results in a loss of business to the company

If the applicant is not likely to repay the loan, i.e. he/she is likely to default, then approving the loan may lead to a financial loss for the company.

The data given below contains the information about the loan application at the time of applying for the loan. It contains two types of scenarios:

The client with payment difficulties: he/she had late payment more than X days on at least one of the first Y instalments of the loan in our sample,

All other cases: All other cases when the payment is paid on time.

When a client applies for a loan, there are four types of decisions that could be taken by the client/company):

Approved: The Company has approved loan Application

Cancelled: The client cancelled the application sometime during approval. Either the client changed her/his mind about the loan or in some cases due to a higher risk of the client he received worse pricing which he did not want.

Refused: The company had rejected the loan (because the client does not meet their requirements etc.).

Unused offer: Loan has been cancelled by the client but on different stages of the process.

In this case study, you will use EDA to understand how consumer attributes and loan attributes influence the tendency of default.

Business Objectives: This case study aims to identify patterns which indicate if a client has difficulty paying their installments which may be used for taking actions such as denying the loan, reducing the amount of loan, lending (to risky applicants) at a higher interest rate, etc. This will ensure that the consumers capable of repaying the loan are not rejected. Identification of such applicants using EDA is the aim of this case study.

In other words, the company wants to understand the driving factors (or driver variables) behind loan default, i.e. the variables which are strong indicators of default. The company can utilise this knowledge for its portfolio and risk assessment.

To develop your understanding of the domain, you are advised to independently research a little about risk analytics - understanding the types of variables and their significance should be enough).

• Jupyter Notebook 100.0%

Emerging Technologies in Data Mining and Information Security pp 563–574 Cite as

Credit Risk Analysis Using EDA

• Prakriti Arora 14 ,
• Siddharth Gautam 15 ,
• Anushka Kalra 16 ,
• Ashish Negi 14 &
• Nitin Tyagi 14  
• Conference paper
• First Online: 16 September 2022

488 Accesses

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 490))

Organizations or banks are providing funds to support people monetarily, keeping assets in return till the amount is repaid to the company with interest encounter loss at many instances when the borrower or the client fails to repay the loan appearing to be a defaulter. Also, when the firm disapproves the loan of an applicant who is likely to repay the sum, the loss is again withstood by the firm. Therefore, to avoid this loss, this research is performed deeply analyzing the factors using exploratory data analysis, affecting the trend of defaulters as well as non-defaulters, helping the firm recognize the defaulters, and disapproving their request to borrow. The exploratory data analysis is performed by visually performing univariate, bivariate, and multivariate analysis on almost all the aspects of the two credit history datasets. The patterns and learnings were noted based on the visual as well as statistical analysis to determine creditworthiness of a client.

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Prakriti Arora, Ashish Negi & Nitin Tyagi

NSUT, Delhi, India

Siddharth Gautam

Mahavir Swami Institute of Technology, Sonipat, India

Anushka Kalra

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Paramartha Dutta

Department of Computer Science and Engineering, Institute of Engineering and Management, Kolkata, West Bengal, India

Satyajit Chakrabarti

Department of Computer Application and Science, Institute of Engineering and Management, Kolkata, West Bengal, India

Abhishek Bhattacharya

Soumi Dutta

Department of Electrical and Electronic Engineering, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh

Celia Shahnaz

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Arora, P., Gautam, S., Kalra, A., Negi, A., Tyagi, N. (2023). Credit Risk Analysis Using EDA. In: Dutta, P., Chakrabarti, S., Bhattacharya, A., Dutta, S., Shahnaz, C. (eds) Emerging Technologies in Data Mining and Information Security. Lecture Notes in Networks and Systems, vol 490. Springer, Singapore. https://doi.org/10.1007/978-981-19-4052-1_56

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DOI : https://doi.org/10.1007/978-981-19-4052-1_56

Published : 16 September 2022

Publisher Name : Springer, Singapore

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Table of Contents

Credit eda case study, problem statement.

This case study aims to identify patterns which indicate if a client has difficulty paying their installments which may be used for taking actions such as denying the loan, reducing the amount of loan, lending (to risky applicants) at a higher interest rate, etc. This will ensure that the consumers capable of repaying the loan are not rejected. Identification of such applicants using EDA is the aim of this case study. In other words, the company wants to understand the driving factors (or driver variables) behind loan default, i.e. the variables which are strong indicators of default.

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