Modelling Credit Default in Microfinance—An Indian Case Study

2017 ◽  
Vol 16 (3) ◽  
pp. 246-258 ◽  
Author(s):  
P. K. Viswanathan ◽  
S. K. Shanthi
Keyword(s):  
Credit Card ◽  
Credit Scoring ◽  
Default Probability ◽  
Relative Importance ◽  
Microfinance Institutions ◽  
Scoring Method ◽  
Credit Score ◽  
Credit Default ◽  
Key Drivers

Credit score models have been successfully applied in a traditional credit card industry and by mortgage firms to determine defaulting customer from the non-defaulting customer. In the light of growing competition in the microfinance industry, over-indebtedness and other factors, the industry has come under increased regulatory supervision. Our study provides evidence from a large microfinance institutions (MFI) in India, and we have applied both the credit scoring method and neural network (NN) method and compared the results. In this article, we demonstrate the capability of credit scoring models for an Indian-based microfinance firm in terms of predicting default probability as well the relative importance of each of its associated drivers. A logistic regression model and NN have been used as the predictive analytic tools for sifting the key drivers of default.

Data Mining for Credit Scoring

2008 ◽  
pp. 2449-2463
Author(s):  
Indranil Bose ◽  
Cheng Pui Kan ◽  
Chi King Tsz ◽  
Lau Wai Ki ◽  
Wong Cho Hung
Keyword(s):  
Data Mining ◽  
Credit Card ◽  
Credit Scoring ◽  
Default Probability ◽  
Probability Estimation ◽  
Financial Industry ◽  
Future Developments ◽  
History Of ◽  
The Relationship ◽  
Major Data

Credit scoring is one of the most popular uses of data mining in the financial industry. Credit scoring can be defined as a technique that helps creditors decide whether to grant credit to customers. With the use of credit scoring decisions about granting of loans can be made in an automated and faster way in order to assist the creditors in managing credit risk. This chapter begins with an explanation of the need for credit scoring followed by the history of credit scoring. Then it discusses the relationship between credit scoring and data mining. The major applications of credit scoring in three areas, which include credit card, mortgage and small business lending, are introduced. This is followed by a discussion of the models used for credit scoring and evaluation of seven major data mining techniques for credit scoring. A study of default probability estimation is also presented. Finally the chapter investigates the benefits and limitations of credit scoring as well as the future developments in this area.

Data Mining for Credit Scoring

2008 ◽  
pp. 309-323 ◽  
Author(s):  
Indranil Bose ◽  
Cheng Pui Kan ◽  
Chi King Tsz ◽  
Lau Wai Ki ◽  
Wong Cho Hung
Keyword(s):  
Data Mining ◽  
Credit Card ◽  
Credit Scoring ◽  
Default Probability ◽  
Probability Estimation ◽  
Financial Industry ◽  
Future Developments ◽  
History Of ◽  
The Relationship ◽  
Major Data

Credit scoring is one of the most popular uses of data mining in the financial industry. Credit scoring can be defined as a technique that helps creditors decide whether to grant credit to customers. With the use of credit scoring decisions about granting of loans can be made in an automated and faster way in order to assist the creditors in managing credit risk. This chapter begins with an explanation of the need for credit scoring followed by the history of credit scoring. Then it discusses the relationship between credit scoring and data mining. The major applications of credit scoring in three areas, which include credit card, mortgage and small business lending, are introduced. This is followed by a discussion of the models used for credit scoring and evaluation of seven major data mining techniques for credit scoring. A study of default probability estimation is also presented. Finally the chapter investigates the benefits and limitations of credit scoring as well as the future developments in this area.

scholarly journals Identification of Default Payments of Credit Card Clients using Boosting Techniques

2020 ◽  
Vol 8 (6) ◽  
pp. 4990-4994
Keyword(s):  
Credit Card ◽  
Credit Rating ◽  
Learning Algorithm ◽  
Credit Scoring ◽  
Screening Method ◽  
Main Idea ◽  
Customer Data ◽  
Huge Data ◽  
Credit Default ◽  
History Of

Understanding the history of clients will act as a valuable screening method for banks by providing information that can categorize clients as defaulters on a loan. Customer credit rating is a grade process where the consumer is categorized by the grade. Credit scoring model used to ascertain credit risk from new and existing customer. Credit rating is an assessment used to measure the creditworthiness of the customer. For the huge customers related dataset we can use various classification techniques used in the field of data mining. The main idea is by analyzing the customer data and by combining machine-learning algorithm to identify the default credit card user. Default is a keyword, used for predicting the customer who cant repay the amount on time. Predicting future credit default accounts in advance is highly tedious task. Modern statistical techniques are usually unable to manage huge data. The proposed work focus mainly on ensemble learning and other artificial intelligence technique.

scholarly journals Use of Machine Learning Techniques to Create a Credit Score Model for Airtime Loans

2020 ◽  
Vol 13 (8) ◽  
pp. 180 ◽  
Author(s):  
Bernard Dushimimana ◽  
Yvonne Wambui ◽  
Timothy Lubega ◽  
Patrick E. McSharry
Keyword(s):  
Machine Learning ◽  
Credit Scoring ◽  
Machine Learning Techniques ◽  
Microfinance Institutions ◽  
Credit Score ◽  
Learning Techniques ◽  
Machine Learning Model ◽  
Default Rates ◽  
Default Rate ◽  
Score Model

Airtime lending default rates are typically lower than those experienced by banks and microfinance institutions (MFIs) but are likely to grow as the service is offered more widely. In this paper, credit scoring techniques are reviewed, and that knowledge is built upon to create an appropriate machine learning model for airtime lending. Over three million loans belonging to more than 41 thousand customers with a repayment period of three months are analysed. Logistic Regression, Decision Trees and Random Forest are evaluated for their ability to classify defaulters using several cross-validation approaches and the latter model performed best. When the default rate is below 2%, it is better to offer everyone a loan. For higher default rates, the model substantially enhances profitability. The model quadruples the tolerable level of default rate for breaking even from 8% to 32%. Nonlinear classification models offer considerable potential for credit scoring, coping with higher levels of default and therefore allowing for larger volumes of customers.

An Ensemble Learning Approach for Credit Scoring Problem: A Case Study of Taiwan Default Credit Card Dataset

2021 ◽  
pp. 283-292
Author(s):  
Duc Quynh Tran ◽  
Doan Dong Nguyen ◽  
Huu Hai Nguyen ◽  
Quang Thuan Nguyen
Keyword(s):  
Ensemble Learning ◽  
Credit Card ◽  
Credit Scoring ◽  
Learning Approach

The Role of Mobile Money in Moderating Financial Exclusion: A Tanzanian Experience

2020 ◽  
Vol 2 ◽  
pp. 1-24 ◽  
Author(s):  
Deogratius Joseph Mhella
Keyword(s):  
Developing Countries ◽  
Financial Institutions ◽  
Banking Sector ◽  
Research Question ◽  
Microfinance Institutions ◽  
Mobile Money ◽  
Access To Credit ◽  
Financial Exclusion

Prior to the advent of mobile money, the banking sector in most of the developing countries excluded certain segments of the population. The excluded populations were deemed as a risk to the banking sector. The banking sector did not work with cash stripped and the financially disenfranchised people. Financial exclusion persisted to incredibly higher levels. Those excluded did not have: bank accounts, savings in financial institutions, access to credit, loan and insurance services. The advent of mobile money moderated the very factors of financial exclusion that the banks failed to resolve. This paper explains how mobile money moderates the factors of financial exclusion that the banks and microfinance institutions have always failed to moderate. The paper seeks to answer the following research question: 'How has mobile money moderated the factors of financial exclusion that other financial institutions failed to resolve between 1960 and 2008? Tanzania has been chosen as a case study to show how mobile has succeeded in moderating financial exclusion in the period after 2008.

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