In today’s world, money laundering is a major issue, so there is a need to fight against money laundering. “Cleaning stolen money, making the source of money no longer exist, called Money Laundering” Financial transactions are huge so it is a challenging task to detect money laundering. As earlier AMLS ( Anti-Money Laundering Suite) was introduced to detect suspicious activities but it is applicable only on individual transactions not for a credit card transaction and other bank transactions. To Overcome these issues of AMLS we propose a Machine Learning method to identify common attributes and behavior with other bank account transactions and also identify suspicious transactions through credit cards. Detection of Money Laundering transaction from a large volume dataset is difficult, so we reduce the input dataset and then calculating the annual turnover of their credit card transfer set a max value which is to be transferred.  Also, find a pair of the transaction with another bank account with common attributes and behavior.