Abstract:
A number of manual loan application evaluation models that use traditional judgmental
methods have been used and continue to be used by commercial banks in Kenya. These
systems however have shortcomings like more time taken to process loan applications
and inconsistency in decision making by bank officials due to a variation of information
provided by different customers. Some customers who are dissatisfied by this mode of
processing loan applications subsequently moving to other banks or seek other financing
modes. This poses a potential loss of business to a competitor commercial bank. The aim
of this study was to analyze the current loan evaluation system at KCB with a view to
design and develop an ANN architecture-based expert system for evaluating loans at the
bank. The objectives of the study were: to find out the types of loans KCB offers to its
clients; to examine the current systems used by KCB to evaluate loan applications; to
determine the challenges faced when evaluating loan applications; to recommend suitable
systems for improving the evaluation of loan applications; to design and develop an
intelligent system that will improve loan applications evaluation process. This study was
based on the expert system theory and the neural network architecture. Data were
collected from information rich sources at KCB which involved sixteen respondents. The
expert system was developed based on ANN architecture and modeled using the
evolutionary development model. The neural network system was built using the back
propagation algorithm. The developed expert system helps to fairly and uniformly
evaluate loan applications efficiently and is considered an improvement to current loan
evaluation processing. This system is recommended for use in Kenya Commercial Bank.
