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The artificial neural network technique with back-propagation algorithm was applied to develop a model that supports the banks in the credit granting decision-making. The model was trained to categorize applicants as either good (credit granted) or bad (credit denied) based on the credit record. The model was able to predict whether a particular applicant is likely or unlikely to repay the credit. The training of neural network model and validation testing was done using data obtained from the bank. The results show a greater performance, classification and prediction accuracy.<\/jats:p>","DOI":"10.4018\/ijtd.2017100104","type":"journal-article","created":{"date-parts":[[2017,8,23]],"date-time":"2017-08-23T09:05:05Z","timestamp":1503479105000},"page":"47-65","source":"Crossref","is-referenced-by-count":4,"title":["A Model Augmenting Credit Risk Management in the Banking Industry"],"prefix":"10.4018","volume":"8","author":[{"given":"Okuthe Paul","family":"Kogeda","sequence":"first","affiliation":[{"name":"Tshwane University of Technology, Computer Science Department, Pretoria, South Africa"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nicknolt N.","family":"Vumane","sequence":"additional","affiliation":[{"name":"Tshwane University of Technology, Computer Science Department, Pretoria, South Africa"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"2432","reference":[{"key":"IJTD.2017100104-0","unstructured":"Adam. (2004, April 23). 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