In the present digital era, preventing and reducing money laundering activities isn’t easy with the use of traditional methods. Hence, financial organizations are equipping and adopting advanced analytical tools and technologies such as machine learning to fight against money laundering.
SwissCognitive Guest Blogger: Dronacharya Dave, Marketing Data Analyst
Money laundering is among the biggest threats to the financial world. It is one of the most trending methods used to convert black money into white without letting others know about it. However, different banking and financial institutions have certain rules and act formed to reduce money laundering activity. However, in the present digital era, preventing and reducing such money laundering activities isn’t easy with the use of traditional methods.
Hence, financial organizations are equipping and adopting advanced analytical tools and technologies such as machine learning to fight against money laundering.
Let us start with a quick discussion on anti-money laundering and then the use of machine learning for anti-money laundering activities.
Anti-Money Laundering- A Brief Introduction
Anti-money laundering refers to the procedures, regulations, and laws followed by financial institutions and banking to prevent and control money laundering activities.
Money laundering includes three primary steps as follows:
- Placement: This is the first stage where money received from illegal sources is placed into financial organizations for the first time.
- Layering: In the second step, money launders create different layers by separating money into numerous bank accounts to obscure the machine learning (ML) algorithms and banking analysts to save the actual origin of laundering from getting identified.
- Integration: This is the last step for transmitting this layered money to the money launder’s account.
Role of Machine Learning (ML) in Anti-Money Laundering (AML)
Machine learning technology plays a vital role in controlling money laundering activities in financial sectors. To control money laundering, it utilizes an advanced machine learning procedure in which the ML system is developed with different types of trends or data to catch the suspicious transactions flagged by the inner banking system.
These machine learning systems help to catch these suspicious transactions, sender, financial records, and transaction patterns using transaction history. Moreover, ML algorithms also help in reducing human error in the AML to a great extent with the use of techniques such as natural language processing (NPL).
NPL technique of ML helps machines understand human language and recognize alerts, process mortgage loans, negative news screening, payments screening, etc. Further, these machine learning technologies assist in monitoring different suspicious transactions and activities and transaction.
ML teaches banking systems to identify and detect the transaction behavior, patterns related to suspicious users/accounts, and classification of alertness based on the risk types such as minimal risk, medium risk, and elevated risk.
Further, the ML systems check alerts and also automatically clear a few alerts and make accounts completely operational based on the account behavior and needed documents.
Due to the increasing adoption of advanced technologies, the demand for money laundering software is constantly increasing.
According to the BIS Research report, the global market anti-money laundering software is expected to reach $4.09 billion by the year 2025, with a growth rate of 14.12% during the forecast period, 2020-2025.
Reason To Use Machine Learning in Anti-Money Laundering
Machine learning is universally used in the finance and banking industry, and AML is the most suited example for using machine learning.
The following are reasons that show how machine learning plays a crucial role.
Detecting Change in Customer Behavior
Machine learning teaches computers to install in the banking and finance industry to detect the changing customer behavior with the help of ML algorithms and NPL.
These machines understand the old customer data first and then examine the customer’s present transaction industry to detect the changes.
As per the changing transaction patterns and behavior, these machines catch suspicious activities and pass the warning to the finance systems.
Machine learning has completely taken over traditional approaches due to the accuracy and quick work procedures.
Deduction of Faulty Positive Alerts in the AML Process
ML helps in detecting and identifying 98% of the faulty positives in the AML process, while adherence teams count only 1% to 2% of AML warns.
In the AML procedure, some warnings get wrongly created to influence the customer’s account by setting some restrictions.
ML helps to lower the rate of faulty positives by utilizing statistical analysis and semantic analysis to detect the risk elements that result in true positive warnings.
Analysis of External and Unstructured Data
Finance and banking institutions examine consumer data such as screening, know your customer (KYC), residence country, politically exposed person (PEP) status, professions, and social status to check their transaction behavior.
Norkom is the most used software to find matches or search customer names with the use of external data and let banking and financial organization systems know if the customer is having any reaction to fraud/suspicious activity.
However, using the traditional approach for gathering such information about the customers are much time taking and has a high chance of human errors.
Hence, the adoption of advanced technology such as machine learning will help to understand the extra data and unrestricted data in many significant manners compared to the traditional method with a better accuracy level.
Anti-money laundering is one of the broad areas in the financial and banking industry and one of the key factors in controlling the illegal flow of money.
Machine learning plays a crucial role in anti-money laundering procedures to get more useful results with more significant effectiveness and efficiency.
However, many financial organizations also choose automation, such as robotics process automation (RPA), in their business process, but the benefits provided by technology such as artificial intelligence and machine learning are much more compared to all other options.
About the Author:
Dronacharya Dave is a data enthusiast currently working as a digital content analyst with a research firm. He largely writes on the role of emerging technologies across different industries.