Credit Risk Management in Banking Industry: Case Study Atwiman Kwanwoma Rural Bank
| dc.contributor.author | Paddy, Jonathan | |
| dc.date.accessioned | 2013-12-16T14:59:11Z | |
| dc.date.accessioned | 2023-04-20T01:35:03Z | |
| dc.date.available | 2013-12-16T14:59:11Z | |
| dc.date.available | 2023-04-20T01:35:03Z | |
| dc.date.issued | 2012 | |
| dc.description | A Thesis Submitted to the Department of Mathematics,Kwame Nkrumah University of Science and Technology, Kumasi In partial fulfillment of the requirement for the degree of Master of Science in Industrial Mathematics | en_US |
| dc.description.abstract | The Business of lending is gradually becoming a major target for many banks, as a result there is high competition among the financial institutions in Ghana leading to default of most loans. In order to raise the quality of giving loans and reduce the risk involve in giving loans, credit scoring models have been developed by banks and researchers to improve the process of assessing credit worthiness during the credit evaluation process. This study uses historical data on payments, demographic characteristics and statistical techniques to construct logistic regression model (credit scoring models) and to identify the important demographic characteristics related to credit risk. The logistic regression model was used to design a logistic regression model calculator which was used to calculate the probability of default. Customers’ age, sex, occupation, number of dependent, marital Status and amount of loan collected were used. The results showed that default rate is higher in males than in females, 30—39 year olds have the highest rate of default. Married customers defaulted more than the customers who are not married (single) and the higher the number of dependents, the higher the default rate. The self employed clients defaulted more than salary earners. It was found out that the higher the amount of loan collected, the higher the probability of default. The predicting power of the model is 70%.The model has 70.5% accuracy rate of distinqiushing defaulters from non-defaulters. If one was identified as defaulter, he/she had 84% chance of actually defaulting and if a customer was identified as non-defaulter, he/she had 54% chance of actually not defaulting. | en_US |
| dc.description.sponsorship | KNUST | en_US |
| dc.identifier.uri | https://ir.knust.edu.gh/handle/123456789/5424 | |
| dc.language.iso | en | en_US |
| dc.title | Credit Risk Management in Banking Industry: Case Study Atwiman Kwanwoma Rural Bank | en_US |
| dc.type | Thesis | en_US |
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