Regulatory technology domain is changing faster with the advent of new age solutions. There have been lot of activity around application of the machine learning technologies and tools to combat the peril of money laundering.
According to the definition of money laundering by Indiaforensic “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.”
Money laundering basically has three stages. In the first stage, dirty money is broken down into small installments. Later it finds a place into different bank accounts belonging to different people. After this the account holders transfer this money into different bank accounts. Typically these bank accounts are in the name of money launderers. Once the money is deposited in these accounts, the money launderer uses this money as white money to buy assets.
Existing AML Framework
Financial institutions have to deal with the criminals on one hand but on the other they also face the regulators who are imposing increasingly severe sanctions for offenses.
In order to implement a risk-based approach to customer knowledge (KYC), financial institutions are increasingly seeking to understand the customer’s professional, institutional, political and social context by analyzing large amounts of external data, including information and media, public archives, social networks, and other open-source data sets.
Existing AML systems mostly use the rules-based approach. These rules based systems were developed over several decades. Financial institutions, software publishers and regulators have made significant investments in developing these systems. This has resulted in the creation of state of the art systems required by regulators for the development, refinement, and maintenance of business rules.
Rules based systems throw a significant amount of false positives. Investigation of false positives is time-consuming process. Banks spend billions of dollars every year to investigate false positives. Hence, financial institutions are seeking for more efficient approaches to combat financial crime and fraud detection.
In this context, understanding machine learning can prove to be valuable for the financial institutions and software publishers.
Machine learning models
However, machine learning models are now replacing the rule based approach. Primarily there are two different machine learning models used to detect the suspicious activities. First model is called unsupervised learning and second is supervised learning.
Under the unsupervised learning, machine tries to identify the patterns in the data without knowing the true positives. Whereas in the supervised learning the machine understands the difference between true and false positives.
However, one should understand that the machine learning models do not detect the money laundering attempts, they are useful in anomaly detection. Today, many financial institutions are using the deep learning methods to combat the financial crimes. They have the datasets of historical transactions.But supervised machine learning is still not a viable approach. The trained models applied to the financial data sets reveal the massive imbalance between the number of “good” and “bad” transactions.
Certification program
Regtechtimes offers a certification course for the software publishers and financial institutions on the topic of machine learning to combat money laundering. This course will walk you through different aspects of anti money laundering AML compliance. You will understand the basics of the money laundering in addition to the methods of applying the advanced technologies in combating the peril of money laundering.
Know more about the possible used cases of Robotics Process Automation, Artificial Intelligence and Natural Language Processing in enhancing the value of the compliance function by innovating methods to improve KYC process, customer screening and due diligence etc.
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.
