Maximum Entropy Principle Based on Bank Customer Account Validation Using the Spark Method

Abstract

Bank customer validation is carried out with the aim of providing a series of services to users of a bank and financial institutions. It is necessary to perform various analytical methods for user’s accounts due to the high volume of banking data. This research works in the field of money laundering detection from real bank data. Banking data analysis is a complex process that involves information gathered from various sources, mainly in terms of personality, such as bills or bank account transactions which have qualitative characteristics such as the testimony of eyewitnesses. Operational or research activities can be greatly improved if supported by proprietary techniques and tools, due to the vast nature of this information. The application of data mining operations with the aim of discovering new knowledge of banking data with an intelligent approach is considered in this research. The approach of this research is to use the spiking neural network (SNN) with a group of sparks to detect money laundering, but due to the weakness in accurately identifying the characteristics of money laundering, the maximum entropy principle (MEP) method is also used. This approach will have a mapping from clustering and feature extraction to classification for accurate detection. Based on the analysis and simulation, it is observed that the proposed approach SNN-MFP has 87% accuracy and is 84.71% more functional than the classical method of using only the SNN. In this analysis, it is observed that in real banking data from Mellat Bank, Iran, in its third and fourth data, with a comprehensive analysis and reaching different outputs, there have been two money laundering cases.