Anti-Money Laundering Using Artificial Intelligence

Today, money laundering is a major problem, in part because financial institutions have gone digital. To fulfill client requests and offer them online services, the fintech sector is rapidly growing. However, the possibility of cybercrime has grown due to increasing demand.

 

Online banking is praised everywhere for making life simpler, but criminals have seized this chance to support terrorists more quickly and efficiently by laundering illegal payments via it. Additionally, it is hard to identify fraud using manual procedures because of the vast quantity of data that is available worldwide, especially in the financial sector.

 

To combat financial crime, traditional anti-money laundering (AML) methods are no longer effective, and many detection systems provide false positives because of out-of-date criteria or thresholds.

 

So, what exactly is money laundering? How can AI help in its prevention? Let us answer a few of these questions in this article. 

 

Money Laundering

 

Money laundering is the practice of giving significant sums of cash obtained via illegal activity the appearance of coming from a legitimate source. Many cyber criminals use this fraud to make money illegally.

 

Money laundering involves three basic steps:

 

• Placement is the process of transferring illegally obtained funds into the legitimate stream of commerce, sometimes through a "front" company that accepts only cash, such as laundry or bar.

 

• Layering is the practice of moving money constantly, in smaller amounts, through several legal transactions to obscure its source.

 

• Integration is the act of eventually giving the owner their money back so they may spend it without raising suspicions from the law.

 

Each of these phases establishes legal business dealings to make it more challenging for an investigator to determine the money's true origin. Financial institutions must deal with the difficult subject of anti-money laundering, to save their companies from fraud.

 

Also Read | 15 Informative Money Laundering Statistics

 

Importance of Technology in Anti-Money Laundering

 

Financial institutions have developed technologically sophisticated weapons in the fight against money laundering and the conflict between the various financial sectors. Thus, these organizations seek to successfully combat various financial crimes by using advanced analytical technologies like machine learning.

 

Financial firms also have to deal with criminals and authorities who are applying harsher penalties for violations. Branches are more committed than ever in their fight against money laundering and the financing of terrorism (AML / CFT), as they fear supervisor sanctions and harm to their brand.

 

Also Read | Understanding Anti-money Laundering

 

Using Artificial Intelligence for AML/CFT

 

Machine learning and artificial intelligence are two dynamic branches of research that intelligently respond to market changes and identify potential dangers. They are easily and swiftly integrated into already-in-place compliance systems. Early adopters are helping their institutions fulfill increasing regulatory requirements while also realizing considerable savings.

 

AML compliance is required, and organizations that don't comply face penalties including fines. For big businesses and institutions, AML compliance procedures have proven expensive and complicated. Time is lost, and expenses rise as a result of the volume, complexity, and AML requirements. Despite the measures made to prevent them, financial crimes are on the rise.

 

Before putting any solution into action, it is essential to recognize the hazards. The risks include using money mules, who are hired to transport money in a money-laundering plan either consciously or unknowingly or using Smurfing, which involves transferring huge amounts of illegal money through a succession of smaller transactions.

 

AI technologies may identify money laundering in many ways by using specialized algorithms. These algorithms essentially examine massive quantities of data and raise a warning sign whenever anything suspect is found, including atypical transactions or account activity.

 

How does AI detect Suspicious Behavior?

 

Here are a few techniques AI can use to spot questionable activity.

 

• To accomplish tasks like customer due diligence and know your client more quickly and fully, AI may also help.

 

• AI may give financial institutions access to a larger variety of client data for AML purposes, which can be utilized in risk analyses, suspicious activity reports, and investigations.

 

• Algorithms may be used to pre-fill reports with pertinent information and standardize language and terminology when dealing with potentially suspicious behavior, saving AML staff members considerable time.

 

• AI can help financial organizations handle their enormous volumes of unstructured data. To be AML compliant, banks must be able to evaluate their unstructured data for transaction monitoring, sanctions screening, and other tasks.

 

Using Machine Learning for AML/CFT

 

The effectiveness of machine learning is crucial to lowering the number of false positives generated by traditional AML devices. Machine learning also gains from lowering the false positives generated by Transaction Monitoring Software.

 

Banks may gain most quickly and significantly from transaction monitoring, especially when integrating machine learning with other cutting-edge algorithms, in their anti-money laundering initiatives.

 

These new technologies are anticipated to cover AML/CFT sectors. As a result, emerging technologies like machine learning and artificial intelligence have the potential to be very important in the realm of AML/CFT. 

 

For this reason, financial institutions must finish their transition with new technologies to compete with their rivals. This includes enhancing their AML / CFT devices with various artificial intelligence (AI) technologies.

 

Where should banks use ML?

 

To begin with, banks must comprehend the situations in which they can apply ML effectively and those in which they cannot. When there is sufficient access to high-quality data and a great degree of freedom in selecting data qualities, machine learning is undoubtedly useful (for example, in scenarios where there is a rapid movement of funds and a large number of attributes can be considered). When it becomes challenging to understand the dynamics and connections between risk variables, ML is also a good choice.

 

When there is insufficient historical data to develop predictive intelligence, ML is useless. In certain circumstances, a conventional strategy (such as tools based on rules and scenarios, for example) could be more efficient.

 

AI/ML to Drive Efficiencies in Operational Hotspots

 

Modern AI approaches can successfully replace manual research by sorting through enormous volumes of data gathered across many bank divisions. This makes it possible for the contemporary bank to quickly and thoroughly examine each transaction that takes place, especially in these areas.

 

AI technologies can automate these compliance procedures, such as:

 

1. Identity Verification:

 

To be sure that your customers are who they say they are, verification checks and KYC procedures are crucial. Banks need to be able to contextualize relationships between people and businesses to identify AML threats with high accuracy and at the earliest opportunity.

 

As part of digital identity verification, this might also incorporate scans of official papers and biometric information. Because some fraudulent documents seem extremely convincing, additional safeguards, such as a video KYC check, can be used.

 

Banks should think about the advantages of delaying onboarding to lower the possibility of hurried and erroneous approvals. By including layers in the early scoping phase, banks will be one step ahead of the competition.

 

2. Activity Monitoring:

 

Digital banks must properly and effectively check customers against international sanctions and watch lists. Additionally, they must be able to identify and follow changes in the status of politically exposed individuals (PEIs), as well as that of their relatives and close friends.

 

3. Transaction Monitoring:

 

To make sure that consumer transactional behavior is consistent with expectations, it is crucial to monitor and comprehend it. Constant monitoring is required in the presence of suspicious behavior, such as transactions carried out in high-risk jurisdictions or at unusually high frequencies or volumes.

 

Applications of AI Technologies for AML/CFT Compliance:

 

Here are some applications of the following AI technologies in improving and supporting the new generation AML tools:

 

Machine learning:

 

1. False positives in the AML Process are reduced using ML:

 

According to the compliance teams, between 1% and 2% of AML alerts result in a Declaration of Suspicion (DS). The most radical changes will be brought about by machine learning and AI, which will aid in identifying and deactivating the 98% of false positive instances.

 

We give the following examples of AI strategies that can lower the rate of false positives:

 

• To find correspondences caused by duplicate data using semantic analysis.
• Identification of high-risk entities using statistical analysis of client information files to determine truly positive results.

 

2. Changes in Customer Behavior can be detected using ML:

 

By examining client transactions, machine learning models may be created to assist in identifying changes in consumer behavior. This method may be used to improve current devices that rely on rule engines for monitoring.

 

This will allow for the detection of clients who have questionable activity at a stage of inquiry. Because money launderers are often one step ahead, what is absent from classic behavioral analysis devices are the novel patterns that might appear.

 

3. Unstructured data and External Data can be analyzed using ML:

 

Financial institutions are looking more and more to understand the professional, institutional, political, and social context of their customers to implement a risk-based approach to customer knowledge (KYC). To do this, they are analyzing vast amounts of external data, including information and media, public archives, social networks, and other open-source data sources.

 

Traditional name searches can discover matches in extraneous information. However, it is unable to give the name's context, identify connections to politically exposed individuals (PEP) or high-risk companies, or evaluate other risk indicators from these sources. Thus, to evaluate unstructured data and create these linkages, artificial intelligence, and natural language processing techniques are required.

 

Robotic Process Automation (RPA):

 

To enable intelligent automation of "Know Your Customer" duties, RPA can be integrated with AI approaches. We specifically mention the following examples of its applications:

 

1. By combining internal customer data from various back-end systems and data deduplication, customer views across all accounts may be created.

 

2. Assembling and compiling data from pertinent outside data sources to develop consumer profiles.

 

3. Leveraging internal and external data to enhance alerts.

 

4. Creation of files comprising recognized KYC controls, notifications, profile information, and all enrichment data, such as geolocation data, in the case of management devices.

 

5. Beneficial owners are analyzed and presented using information from outside databases.

 

Also Read | What is Robotic Process Automation?

 

 

Natural Language Generation:

 

A critical link in the AML/CFT value chain, efficient regulatory reporting may also be improved by advanced technology. The RPA may be used to archive reports electronically and to populate regulatory reporting formats with current data. The standard reporting modules of Know Your Customer and Anti-Money Laundering systems have long included the automatic preparation of regulatory reports.

 

The narrative of the suspicious activity report created by analysts is a weakness of these systems. Natural language generation (NLG) advancements now make it possible to compile pertinent information about recognized instances into a cohesive narrative that can be sent to the analyst for review or amendment, facilitating aided report preparation.

 

On the other hand, to track AML/CFT activities, regulators, financial intelligence units, and the board of directors or general management internally require several reports at various intervals. Nothing is more comprehensible than reporting in everyday language.

 

Challenges of AML/CFT

 

Traditional financial institutions have had time to build their anti-money laundering (AML) processes over the years, gradually adjusting to shifting regulatory requirements. Fintechs are catching up and making an effort to grow their technology and resources to fulfill the demand for their services while continuing to adhere to legal requirements. Here are a few difficulties financial organizations have while integrating AI.

 

Digital banks confront several significant issues in guaranteeing AML compliance yet urgently need actionable data to create and enhance their anti-money laundering (AML) and counter-terrorist financing (CFT) regimes. As evidence that Fintechs are receiving increased scrutiny, the Financial Conduct Authority (FCA) revealed in 2021 that it was looking into Monzo for potential AML/CFT compliance violations.

 

They need risk assessment methods because, to start with, their dependence on internet banking leaves them susceptible when it comes to authorizing an account or transaction. Then there is the enormous volume of data that has to be processed fast, which consists of a range of data kinds, including IP and geolocation data as well as other personal information gleaned from applications and digital devices. Finding useful, pertinent, and timely insights from so much data may be challenging, especially since compliance procedures are frequently repetitive, data-intensive, and wasteful.