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Predicting microfinance loan default

Microfinance lending institutions can use the following predictors to avoid bad loans : Marital status (single individuals are more prone to defaults). Time period of loan (longer loans are prone to higher default rate). Interest rate (very high interest rates are likely to resul in loan default).

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Bibliographic Details
Main Authors: Kumar, Senthil, Aslam, Mohammad
Format: Conference or Workshop Item
Language:English
Published: 2021
Subjects:
HD28 Management. Industrial Management
T Technology (General)
TA Engineering (General). Civil engineering (General)
Online Access:http://umpir.ump.edu.my/id/eprint/34530/1/Predicting%20microfinance%20loan%20default.CITREX2021..pdf
http://umpir.ump.edu.my/id/eprint/34530/
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spelling my.ump.umpir.345302022-06-28T07:01:45Z http://umpir.ump.edu.my/id/eprint/34530/ Predicting microfinance loan default Kumar, Senthil Aslam, Mohammad HD28 Management. Industrial Management T Technology (General) TA Engineering (General). Civil engineering (General) Microfinance lending institutions can use the following predictors to avoid bad loans : Marital status (single individuals are more prone to defaults). Time period of loan (longer loans are prone to higher default rate). Interest rate (very high interest rates are likely to resul in loan default). 2021 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/34530/1/Predicting%20microfinance%20loan%20default.CITREX2021..pdf Kumar, Senthil and Aslam, Mohammad (2021) Predicting microfinance loan default. In: Creation, Innovation, Technology & Research Exposition (CITREX) 2021, 2021 , Virtually hosted by Universiti Malaysia Pahang. p. 1..
institution Universiti Malaysia Pahang
building UMP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang
content_source UMP Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
topic HD28 Management. Industrial Management
T Technology (General)
TA Engineering (General). Civil engineering (General)
spellingShingle HD28 Management. Industrial Management
T Technology (General)
TA Engineering (General). Civil engineering (General)
Kumar, Senthil
Aslam, Mohammad
Predicting microfinance loan default
description Microfinance lending institutions can use the following predictors to avoid bad loans : Marital status (single individuals are more prone to defaults). Time period of loan (longer loans are prone to higher default rate). Interest rate (very high interest rates are likely to resul in loan default).
format Conference or Workshop Item
author Kumar, Senthil
Aslam, Mohammad
author_facet Kumar, Senthil
Aslam, Mohammad
author_sort Kumar, Senthil
title Predicting microfinance loan default
title_short Predicting microfinance loan default
title_full Predicting microfinance loan default
title_fullStr Predicting microfinance loan default
title_full_unstemmed Predicting microfinance loan default
title_sort predicting microfinance loan default
publishDate 2021
url http://umpir.ump.edu.my/id/eprint/34530/1/Predicting%20microfinance%20loan%20default.CITREX2021..pdf
http://umpir.ump.edu.my/id/eprint/34530/
_version_ 1738510514371166208
score 13.160551

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