Fintechs: Machine Learning to Fight Money Laundering

Blog / Fintechs: Machine Learning to Fight Money Laundering

FinTechs are at the forefront of the fight against money laundering. The responsibility of the financial institutions' compliance departments is to comply with the European Anti-Money Laundering guidelines. The money houses try to track down the perpetrators by unmasking and removing their customer bases, the most widespread money laundering vehicles, so-called letterbox companies. With conventional compliance tools, however, the institutes quickly reach their limits. AML in fintech  offers innovative solutions to enhance the efficiency and effectiveness of anti-money laundering efforts in the rapidly evolving financial technology sector.

One of the key advantages of AML in fintech is its ability to leverage advanced data analytics and artificial intelligence to identify suspicious activities and patterns more accurately and swiftly. Fintech companies are developing cutting-edge algorithms that can analyze vast amounts of transaction data in real-time, helping financial institutions detect potential money laundering activities with greater precision.

Moreover, fintech firms are pioneering the use of blockchain technology for enhanced transparency and traceability in financial transactions. This decentralized ledger system has the potential to revolutionize AML efforts by creating an immutable record of all financial transactions, making it more challenging for money launderers to operate undetected.

Money Laundering: The Global Problem

Money laundering is a growing global problem that poses a significant threat to financial systems and economies worldwide. It is a criminal activity that involves disguising the proceeds of crime, such as drug trafficking, fraud, corruption, and other illicit activities, as legitimate funds. The aim of money laundering is to conceal the origin, ownership, or destination of the illicit funds, making it difficult to trace them back to their criminal origins.

The scale of the problem is enormous, with estimates suggesting that trillions of dollars are laundered each year. Criminal organizations and terrorist groups are among the biggest culprits, using the proceeds of their illegal activities to fund their operations and further their criminal endeavors. Money laundering is also a significant concern for financial institutions, as it can damage their reputation, increase their risk exposure, and result in hefty fines or even legal action.

Technology, specifically machine learning and artificial intelligence, is also playing an increasingly important role in the fight against money laundering. Financial institutions are using sophisticated machine learning algorithms to analyze large volumes of data and detect suspicious transactions that may indicate money laundering activities. By using machine learning, financial institutions can identify unusual patterns and behaviors in transactions, enabling them to take action quickly to prevent money laundering activities from occurring.

Detailed guidance to learn AML compliance in the Fintech Industry

The Role of Fintechs in Combating Money Laundering

Fintechs, or financial technology companies, are playing an increasingly important role in the fight against money laundering. Fintechs are disrupting the financial services industry by offering innovative solutions that enable individuals and businesses to access financial services more easily and at lower costs. These solutions often rely on cutting-edge technology, such as artificial intelligence, blockchain, and machine learning, which can be leveraged to combat money laundering and enhance AML in fintech.

One of the key advantages of fintechs is their ability to collect and analyze vast amounts of data quickly and accurately. This data can be used to identify patterns and behaviors that may indicate money laundering activities, such as unusual transaction volumes, frequent transfers to high-risk countries, or transactions that are not consistent with a customer's profile or business operations.

Fintechs are also leveraging artificial intelligence and machine learning algorithms to detect suspicious activities and prevent money laundering, further advancing the field of AML in fintech. These algorithms can analyze vast amounts of data and identify suspicious patterns or behaviors that would be difficult or impossible for humans to detect. By using machine learning, fintechs can continuously improve their detection capabilities and stay ahead of evolving money laundering techniques.

Another advantage of fintechs in combating money laundering is their agility and flexibility. Fintechs can quickly develop and deploy new solutions and technologies to respond to emerging threats or changing regulations. They can also collaborate with other fintechs, financial institutions, and regulatory bodies to share information and best practices, creating a more robust and coordinated response to money laundering.

Despite these advantages, fintechs also face several challenges in combating money laundering within the context of AML in fintech. One of the key challenges is ensuring compliance with AML regulations and laws. Fintechs must implement robust and effective AML compliance programs that can identify and prevent money laundering activities while maintaining customer privacy and data security.

Wolfsberg Group supports the use of artificial intelligence and machine learning in financial institutions' AML programs.

Rough Conclusions About the Legality

FinTechs are at the forefront when it comes to draining the structures behind letterbox companies. For a long time, it was possible to draw rough conclusions about the legality based on indications such as the amount and frequency of incoming and outgoing amounts of money, a minimal number of employees, and the company's location. Today, it has become much more difficult for fintech to identify mailbox companies and close loopholes that criminal organizations use to launder money.

Digitization has increased the complexity of money laundering cases. FinTech must be able to separate the wheat from the chaff here: which company is still operating legally, and who is actually making itself punishable? Because not every tax optimization is illegal, and not every subsidiary is prohibited in a tax haven. Intergovernmental cooperation solutions are not yet sufficiently developed to recognize potential mailbox companies across the board.

Collecting, categorizing, and analyzing data is, therefore, the best opportunity for fintech. However, the right processes are still missing in many institutes. The use of supportive technology is becoming increasingly important so that fintech can comply with all regulatory guidelines and meet compliance requirements.

Data Analysis as a Clarification Accelerator

The solution is Data Analytics and Machine Learning (ML). Practice shows that a significant number of suspected cases can be identified using these methods. Various solutions are conceivable, from guided to self-learning machine learning models, including self-building decision trees, neural networks, and integrated analytics platforms. Which of these makes the most sense depends on the data available. If the right technology is selected, it detects abnormalities from large amounts of data that would otherwise only have been reported externally via police authorities, correspondent fintech, or industry networks.

Through constant adaptation, technology can react independently to changes in transactions and customers. If it issues a hit, an employee checks it in the process and decides on a reaction, for example, whether the customer has to be reported to the authorities or not. The machine learning model aims to keep the rate of "wrong hits" as low as possible and raise the alarm only in justified cases. If the learning system reduces the false-positive rate, additional efforts can be cushioned directly. This saves project budgets and enables the solutions to be scaled faster. Ten-year plans are a thing of the past.

The key to success is to "know your data," and it must be part of the overall concept for preventing money laundering by fintech. Data and its analysis are an effective way to unmask mailbox companies. But first, the data has to be put in order and its quality checked. FinTech has numerous data points for its business and private customers, from payment behavior, the length of stay, and the amount of the amounts in the account to the transaction parties. However, if these data sets are distributed over many data silos, comprehensive data analysis becomes difficult. FinTech can control their amounts of data and their modeling themselves with a five-point plan:

Strategic Vision

The strategy comes first. Fintechs must be aware of the requirements that a machine learning model meets and what the result should look like. The model must also fit into a predefined target image to clarify how the products delivered are interpreted and which answers are sought.

Data Collection

Based on the strategic concept, the next step requires the exact description and procurement of required transaction and customer data and the establishment of technical databases and software. Financial institutions should also include external data such as company lists, adverse reports, and sanction lists in the calculation.

Data Analysis

Data Analytics helps to categorize company constructs, put them in relation, make frequencies and distributions visible, and unify them. Numerous analysis programs can be made available quickly and efficiently and are easy to use.

Understanding of Data

Analyzed data must be understandable. The more detailed the data, the more precise analyses machine learning programs can perform and recognize patterns or cross-connections. Using data from freely accessible sources can complete data sets and thus enable better results. Based on this logic, the prototype of a machine-learning model can be created.

Machine Learning

A machine learning model lives from its data and learns more when the distant data samples are repeated. This increases statistical accuracy, explains the dynamics of the model, and leaves room for further design. The model can be adapted and improved by subsequently making the results plausible.

How Sanction Scanner Helps

Sanction Scanner solutions are designed to automate and streamline the compliance process, making it easier for businesses to stay compliant and avoid potential regulatory fines, a critical aspect of AML in Fintech.

One of Sanction Scanner's key offerings is its screening engine, which uses artificial intelligence and machine learning algorithms to screen individuals and entities against global sanctions and watchlists. This enables businesses to quickly identify high-risk customers or transactions and take appropriate actions to mitigate the risk of money laundering and terrorism financing. In addition to screening engines, Sanction Scanner offers transaction monitoring solutions that use machine learning algorithms to analyze transaction data and identify patterns or behaviors that may indicate money laundering or terrorism financing activities.

Sanction Scanner offers compliance management tools to help businesses manage their AML compliance activities. These tools provide a centralized platform for managing compliance processes, tracking compliance activities, and generating reports for regulatory authorities.

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