Money Laundering Techniques - October 2023

?? Once a month, let's discover new money laundering, terrorist financing and fraud techniques as well as ancestral methods.?

Brought to you this month by Dotfile, the modern operating system for Compliance teams. Dotfile helps you verify your individual or business customers anywhere in the world in less than 10 seconds! ????

On today's menu we have:?

• The State of Fraud and AML in 2023 from Unit21???
• AI/Machine Learning & AML-Where are we now? by Robert L. Williams III, CAMS,CCI,CRFCC ??
• The infamous Jean-Bernard Lasnaud ????

Are you passionate about an AML related topic? ??

Would you like to write about it and reach over 18k compliance professionals? ??

If so, just send me a message to work out the details! ??

The State of Fraud and AML in 2023 from Unit21 ???

?? Unlock the secrets of fraud prevention! ??

?? Are you ready to enhance your skills and stay one step ahead of fraudsters?

I've got some exciting news for you!

After the excellent Fraud Fighters Manual, introducing the must-have guide for every fraud prevention expert.

This comprehensive resource gives all the top priorities for risk and compliance teams right now, and over the next 12 months.

And the best part??It's absolutely FREE!???

?? Let me give you a sneak peek into what this manual has in store for you:

?? Part 1: Top priorities for risk & compliance professionalsOne of them is adding automation to manual processes! Can you guess the other ones? ??

?? Part 2: The biggest challenges facing risk & compliance teamsVery interesting read, it shows how fraud and compliance teams concerns often aligns... but not always!

?? Part 3: How to build a successful risk & compliance programThis part gives great advices based on real use cases and will definitely help professionals like us in benchmarking their tool stack. ??

?? Part 4: Why software solutions are bought (or not)This is always a tough one and there is no one-size-fits-all solution, but this manual will give you a few good insights!

?? Part 5: What's coming in the future of fraud and AMLDefinitely my favorite part, I've been advocating for the power of shared data for a long time and you will find a great piece about it in there! ??

?? This manual is an invaluable resource, meticulously crafted to empower fraud prevention professionals like you.

To get your hands on it, simply click on ?? this link ??

Together, let's build a fraud-free future! ??

AI/Machine Learning & AML-Where are we now? by Robert L. Williams III, CAMS,CCI,CRFCC ??

To realize the full benefits of machine learning and advanced analytics in anti–money laundering, institutions need AML experts, strong data science talent, and reliable data sources in the fight against this type of financial crime.

Since the volume of money laundering?and other financial crimes is growing worldwide—and the techniques used to evade their detection are becoming ever more sophisticated, a vigorous response from banks has been brought forth, which, collectively, are investing billions each year to improve their defenses against financial crime.?

For example in 2020, institutions spent an estimated $214 billion on financial-crime compliance. ?And a more recent statistic from Celent predicts that estimates of spending on AML technology and operations by financial institutions worldwide will reach $50.8 billion in 2023 driven of course by new regulations and fines, increasing operational complexity, and the topic of this article, and how financial institutions are finding it imperative? to reign in costs through process automation, and AI/Machine Learning tools.?

Additionally, the resulting regulatory fines related to compliance are surging year over year as regulators impose tougher penalties. But banks’ traditional rule- and scenario-based approaches to fighting financial crimes has always seemed a step behind the bad guys, making the fight against money laundering an ongoing challenge for compliance, monitoring, and risk organizations.

Now, there is an opportunity for banks to get out in front. Recent enhancements in machine learning (ML) are helping banks to improve their anti-money-laundering (AML) programs significantly, including, and most immediately, the transaction monitoring element of these programs. Moreover, US regulators are strongly backing these efforts.

The best news in a long time for U.S. Banks is that included in the Anti-Money Laundering Act (AMLA) of 2020 and the subsequent National Illicit Finance Strategy, US agencies are reducing obstacles from existing regulations, guidance, and examination practices to encourage banks to test and adopt innovative approaches for fighting financial crimes.

So this is where the AI/Machine learning tool to fight money laundering comes in.?

Today, many financial institutions use rule- and scenario-based tools or basic statistical approaches for transaction monitoring. These rules and thresholds are driven primarily by industry red flags, basic statistical indicators, and expert judgment.?

But the rules often fail to capture the latest trends in money-laundering behavior. Machine learning models, on the other hand, leverage more granular, behavior-indicative data to build sophisticated algorithms.?

Points to consider for utilizing AL/ML/What additional data sources are needed?

When working with suspicious-activity reports, poor quality data inevitably leads to poor model performance. It is important, for example, not to be too dependent on suspicious-activity-report categories (for example, structuring, terrorist financing, money laundering, fraud), which are limited in today’s world. With this in mind, institutions are exploring a range of initiatives to improve data gathering for their ML models to provide enriched context for transaction monitoring.?

This may include modeling against individual transactions or cases, components of suspicious-activity-report filings or client relationships terminated for AML reasons, and data from historical subpoenas and other law enforcement requests for information.

These more complex ML models can incorporate a wide range of new elements and variables, such as the following:

• enhanced client data?(for example, nature of business, type of clients)
• more comprehensive?product data (for instance, granular product type and usage)
• more granular channel?data?(for example, channels for different products)
• risk indicators across risk type?(for instance, business geography)
• external data sources?(for example, bureau data, financial-crime registries)

Point to consider # 2- How should banks service the model?

ML models are less transparent than rule-based ones, and model risk management (MRM) teams and regulators are increasingly demanding better model “explainability”—that is, better methods of interpreting “black box” machine learning models, which develop and learn directly from the data with typically no human supervision or guidance—so they can assess the models.

At leading institutions, model development teams are working with AML investigators to help ensure that the teams understand the modeling data, create interpretable modeling features rather than a data dump, and integrate ML modules with existing rule- and scenario-based models and tools (that is, the transition process should leverage the existing platform, thus improving the status quo and not dismantling it entirely). Leading institutions are also starting to create AML-specific model guidelines.

?Some of the specific ways that banks are improving explainability and generating more high-quality alerts for downstream investigators include the following methods:

• Out-of-time sample:?Banks must reserve sufficient testing samples to conduct model testing.
• Model validation: Banks consider ML-specific risks, including feature engineering, hyper-parameter calibration, model bias against protected classes, model drift and interpretability, transparency, and explainability.
• Ongoing monitoring:?Banks conduct frequent, ongoing, below-the-line (BTL) testing to help monitor model performance.

They are also more flexible in quickly adjusting to new trends and continually improving over time. By replacing rule- and scenario-based tools with ML models, one leading financial institution improved suspicious activity identification by up to 40 percent and efficiency by up to 30 percent. Who out there wouldn’t wish to have the same result in their institution?

Recently the momentum in the fight against financial crimes is creating keen interest in ML among industry leaders. Earlier this year, McKinsey invited the heads of anti–money laundering and financial crime from 14 major North American banks to discuss adopting ML solutions in transaction monitoring.?

More than 80 percent of the participants had begun the process of adopting ML solutions, with most expecting to dedicate serious efforts to implementing ML solutions within their AML programs in the next two to three years. (I would? consider? that as good news folks)?

According to a McKinsey Global article I read recently, some CIB institutions are right now utilizing AI at scale and reaping enormous benefits. Apparently one of the issues holding banks back is bankers sometimes see areas across the bank such as the front, middle, or back offices as too complex to use machine learning.?

To me that just sounds like an excuse! Make it work (the old metaphor K.I.S.S. (Keep it Simple Stupid). I am a huge proponent in the AML and FCC realm that NOTHING is impossible, that you simply need the right minds and subject matter experts working on a solution!

Banks have had success in areas which include Relationship Managers (RM’s), support and advisory (Which I have most of my experience), (compliance and risk decisions), and finally client services on products like FX hedges or forward commodity agreements, where a computer algorithm would be a useful tool.?

And btw the AML Analyst/Investigators role is not in jeopardy just yet IMHO. I have been in the AML/Anti-Financial crime realm for over twenty years with probably the last decade reading everywhere that AI/ML will replace them, but it hasn’t happened as of yet??

In summary my feeling is if they haven’t done so already financial institutions must utilize what seems to me to be an extremely effective, efficient, and productive tool, to detect and fight money laundering.

I have included a few exhibits below that outline the tool clearer.

Exhibit 1

?

Exhibit # 2-above?

References utilized

https://www.mckinsey.com/capabilities/risk-and-resilience/our-insights/the-fight-against-money-laundering-machine-learning-is-a-game-changer

Jean-Bernard Lasnaud ????

?? The notorious international arms dealer has been involved in various money laundering operations throughout his career.

Known for his illicit activities, Lasnaud has managed to evade authorities for years while amassing vast amounts of wealth through illegal means.

His operations spanned across multiple countries, making it difficult for law enforcement agencies to track his activities.

?? One of the key aspects of Lasnaud's money laundering operations was his close relationship with intelligence agencies, including the CIA.

He claims to have worked with the CIA and the U.S. Customs in conducting covert operations and gathering information in various regions, particularly in South America.

He used a complex network of offshore accounts, shell companies and intermediaries to facilitate money laundering. ???

???? Lasnaud's illicit activities came to light in 2002 when he was arrested in Switzerland, following an extradition request from Argentina.

The Argentine justice system accused him of organizing the illegal export of military equipment during a war between Ecuador and Peru in 1995.

He admitted to playing a role as a technical inspector for the cargo, claiming to have acted on behalf of the Ecuadorian company Prodefensa.

However, the investigation revealed his involvement in a broader arms trafficking network, implicating high-ranking officials and former ministers in Argentina. ????

?? Lasnaud's operations were not without their share of suspicious deaths.

One notable case involved the "suicide" of Captain Horacio Estrada, whom Lasnaud considered a close friend.

Lasnaud believes that Estrada's death was not a suicide but rather a result of the high-stakes arms trafficking operation they were involved in.

?? To launder the proceeds from his illegal activities, Lasnaud employed various techniques.

He notably used offshore accounts in countries such as Switzerland, the Cayman Islands, and Mauritius.

They allowed him to create a complex web of transactions to obfuscate the origin of the funds. ???

?? Despite his attempts to conceal his wealth, Lasnaud still faced legal repercussions.

The Swiss authorities accepted Argentina's request for extradition, subjecting him to the Argentine justice system.

The exposure of his money laundering operations prompted significant reforms within the financial sector.

It led to stricter regulations and enhanced measures to combat illicit financial activities.

His case highlights the need for international cooperation and the exchange of information to dismantle complex networks involved in money laundering. ??

Are you passionate about an AML related topic? ??

Would you like to write about it and reach over 18k compliance professionals? ??

If so, just send me a message to work out the details! ??

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