The research aims to improve the effectiveness of financial lending business decision-making by developing dynamic models involved in the money-lending business. The objectives of this study are to identify preference factors that affect a customer’s decision of choosing a particular financial institution, to determine the important approval factors that providers need to take into consideration while approving loans and to identify any relationship between and among the factors. The data are taken from a case study of a lending company in northern Thailand. The first model is the preference model, comprising 68 inputs factors, which are used to determine the reasons why a customer chooses service providers, which can be either commercial or non-commercial banks. The model is developed using a neural network (NN) with a history data of 2973 records and comprising four sub-models. The model is improved by varying the NN structure and EPOC. The best model provides an accuracy rate of 100%. The second model is the approval model, comprising 55 input factors for predicting the result of loan requests, which can determine if the loan should be approved with the full amount of the request, approved with a lesser amount or another outcome. The model is developed using a neural network with history data of 787 records. This model is composed of three sub-models; the best model of which gives an accuracy rate of 55%. The third model is the hybrid decision model, linking preference factors and approval factors with external factors. The model is constructed using system dynamics factors, approval factors, financial institutions and system dynamic modeling and the model can simulate the result if the input is changed.

In Thailand, the numbers of commercial and non commercial banks have increased which include leasing companies dramatically. Competition in the banking markets is severe. In this paper we concentrate only in Chinagmai province. So the purpose of this paper is to explore which factors are important for customer preference to use service.In this paper, 64 factors are categorized by market mixed (7P) Questionnaires are developed for survey. Neural network, a data mining tool, is used to develop framework for preference. For developing model, C# Programming is used. K fold validation is used to validation this model (90:10). The result of training model is 95%. The first model is called a preference model. It is used to predict if customer selects commercial banks, non commercial banks or leasing companies. When option is made, model would give information on the reasons. The result of prediction is 81%. The benefit of this model is to address the strategy to any provider satisfy customer preference.

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