Today, financial sector is increasing at a rapid rate. Data associated with these financial sectors is also increasing. The banking sector typically, contains huge amount of data. Where there is more data, there are risks and frauds. As Banking sector is the biggest financial institution, majority of frauds take place in this sector. Money laundering is a common fraud related to banks. Knowingly or unknowingly if a bank is involved in money laundering then it has to face serious consequences.
According to the definition of money laundering by Investopedia “Money laundering is the process of making large amounts of money generated by a criminal activity, such as drug trafficking or terrorist funding, appear to have come from a legitimate source. The money from the criminal activity is considered dirty, and the process “launders” it to make it look clean. “
Money laundering basically has three stages. In the first stage, dirty money is broken down into small installments and placed into different bank accounts belonging to different people. After this the account holders are then asked to transfer this money into different bank accounts. These bank accounts are controlled by the money launderer. Once the money is deposited in these accounts, the money launderer uses this money as white money to buy assets.
Money laundering is a major issue and those associated with it face severe consequences. In order to combat money laundering, banks are trying to make use of machine learning to combat money laundering. However, this is an intricate task. In machine learning, the system learns from the existing data base which contains similar patterns. The system then applies this learnt knowledge to predict future outcomes. If there are any irregularities in the data base then the system cannot provide accurate results. This is the problem in bank. The data bases do not have fixed patterns. there is not much regularity in the database. Therefore, it is very difficult to actually implement machine learning in anti-money laundering.
Anti-money laundering is a serious offence. Application of machine learning in efforts to curb money laundering can be fruitful. However, more research needs to be done in customizing the machines according to the patterns in the database to provide nearing to 100% accuracy.