Credit evaluation model using naïve bayes classifier a case of a Kenyan Commercial Bank
With the increasing demand for credit facilities for the purpose of development, more and more financial institutions are being established to cater for the need. Acquiring these facilities from the institutions sometimes prove slow and inefficient due to the model adopted for credit evaluation. Reliance on traditional methods for instance, a checklist of bank rules, conventional statistical methods and personal judgment in evaluating credit worthiness makes the process slow and such judgments could be biased. Effective models are required to help mitigate these day-to-day challenges. This study examines the relevance of Naïve Bayes Classifier as an enabling tool in credit decision that can automatically evaluate credit applications based on customer‟s biographic, demographic and behavioural characteristics. Data used is obtained from one of the commercial banks in Kenya. Feature selection is performed on the data in order to eliminate redundant and less relevant variables. A model using Naïve Bayes Classifier algorithm is developed and its classification performance evaluated. Results show that Naïve Bayes Classifier can be used as a credit decision tool that can speed up and improve efficiency of the process. It also shows that using significant variables improves the model‟s classification performance. The classification accuracy obtained indicates that the classifier has ability to correctly classify credit applications thereby identifying “bad” credit applications at an early stage hence reducing loss of revenue. Implementation of such model in Kenyan Commercial banks can be helpful for the decision making process.