With the advances in the Information Technology (IT) field, banks can eva-luate the credit requests of the customers via effective analytical methods and risk analysis. The software products, named Credit Scoring Systems, consist of collecting customer data based on pre-determined credit factors, processing the data with various statistical or machine learning methods, and conducting credit risk analysis to make the final credit decision. Throughout the evaluation process of the credit applications, various scoring models are commonly used. These models utilize the previous transactions on the bank accounts of the customers to make a decision on the credit applications. In the proposed work, the information about the customer related to several as-pects and processed with machine learning techniques, and finally a credit score will be determined for each customer. Classification problem using Grey Wolf Optimization method was focused in this work. This information will later be used to decide whether the credit application of a customer can be approved or not. In this study, intentions can be summarized as, providing useful tools to manage the increasing number of customers who apply for consume credits, establishing a structure for crediting the right customers at the right time with the right amount and payment plan, increasing the effici-ency of collecting credit payments, thus contributing to the national economy by using the resources more effectively, creating optimal strategies for maximizing the profit by minimizing the risk, reducing the effect of an expert for credit scoring and evaluation, and reducing the costs.