Toward a new classification model for analysing financial datasets

Nowadays, financial data analysis is becoming increasingly urgent in the business market. As companies collect more and more data from daily operations, they expect to extract useful knowledge from existing collected data to help make suitable decisions for new customer requests, e.g. user credit category, confidence of expected return, etc. Banking and financial institutions have applied various data mining techniques to improve their decision-making processes. However, naive approaches of data mining techniques could raise performance issues in analysing very large and complex financial data. In this paper, we present a classification model for analysing efficiently these financial data. We also evaluate the performance of our model with different real-world data from transaction to stock and credit rating, etc., and we show that it is efficient, robust, and well suited for these data.