Bank Transaction Analyze for Recognize of Money Laundering Using Decision Tree Algorithm (DTA)

Abstract

Abstract Bank Transaction Analyze for Recognize of Money Laundering Using DTA One of the important problems of the banking systems is illegal transactions based on fraud and money laundering that can destroy the economy and financial foundation of a country. Fraud and money laundering are used to escape from tax payment or inject dirty money to the economy cycle. The offenders use lots of transactions to show their illegal funds rightful. One important problem of fraud and money laundering recognize in banking system is high complexity of it. We need to use knowledge discover methods like learning machine and data mining. We can mostly recognize the hidden pattern of illegal transactions by using various learning machines or data mining like Bayesian Network, DT, Support Vector Machine or Artificial Neural Network. To recognize the pattern and classify the banking transactions, ML methods need to classify in two categories Normal and Abnormal to find suitable features for increase their accuracy. Actually, the feature selection is too important in bank fraud recognition and it is a kind of optimization too because the suitable feature selection causes recognize error go down by techniques like DT. In this research we use DTA to select important features related with bank fraud to decide and knowledge discovery which has a good speed in discover of bank fraud. When the features of bank fraud select in right way and with good accuracy then the accuracy of DT will be increased and we must select those features that are more important to get better accuracy in recognition of fraud and money laundering.