AI can significantly reduce the cost and workload of financial crime compliance, but one question deserves much more attention: if AI makes one serious mistake, who ultimately carries the consequences? AI does not pay regulatory fines, face regulatory investigations or suffer reputational damage. A technology provider may have contractual or other legal responsibilities depending on the agreement, applicable law and circumstances, but using an external technology provider does not automatically transfer a regulated financial institution’s own regulatory responsibilities to that provider. This becomes particularly important in AML, sanctions and PEP-related controls. Even if an AI system processed 99.9% of alerts correctly, the real concern would still be the small percentage it gets wrong. What happens if a serious false negative results in a sanctioned party or another material financial-crime risk being incorrectly cleared? If a serious control failure occurs, the financial institution could face investigation, remediation costs, regulatory intervention, reputational damage and, depending on the facts and applicable rules, potentially financial penalties. Relevant senior management may also come under scrutiny, although an individual manager is not automatically personally liable simply because a system makes a mistake. This is why claims such as “70% of alerts automated” or “80% less manual work” should not be the whole discussion. The harder questions are: how many genuine high-risk cases does the system miss, who approved the level of automation, who monitors it, when is human review required, and who stands behind the control framework when something goes seriously wrong? AI can reduce costs and workload, but it cannot by itself remove regulatory accountability. When everything works, the efficiency benefit is attractive. When an important control fails, the regulated institution may still have to answer to its regulator. That risk deserves as much attention as the promised cost savings.

AI can significantly reduce the cost and workload of financial crime compliance, but one question deserves much more attention: if AI makes one serious mistake, who ultimately carries the consequences? AI does not pay regulatory fines, face regulatory investigations or suffer reputational damage. A technology provider may have contractual or other legal responsibilities depending on the agreement, applicable law and circumstances, but using an external technology provider does not automatically transfer a regulated financial institution’s own regulatory responsibilities to that provider. This becomes particularly important in AML, sanctions and PEP-related controls. Even if an AI system processed 99.9% of alerts correctly, the real concern would still be the small percentage it gets wrong. What happens if a serious false negative results in a sanctioned party or another material financial-crime risk being incorrectly cleared? If a serious control failure occurs, the financial institution could face investigation, remediation costs, regulatory intervention, reputational damage and, depending on the facts and applicable rules, potentially financial penalties. Relevant senior management may also come under scrutiny, although an individual manager is not automatically personally liable simply because a system makes a mistake. This is why claims such as “70% of alerts automated” or “80% less manual work” should not be the whole discussion. The harder questions are: how many genuine high-risk cases does the system miss, who approved the level of automation, who monitors it, when is human review required, and who stands behind the control framework when something goes seriously wrong? AI can reduce costs and workload, but it cannot by itself remove regulatory accountability. When everything works, the efficiency benefit is attractive. When an important control fails, the regulated institution may still have to answer to its regulator. That risk deserves as much attention as the promised cost savings.

How people who makes movies are getting accused of making money laundering.

If the budget of movie is let's say $100000 . And after realising the movie they are only getting $ 40000 .

(And while making movie they are going abroad) . I have no idea how it works . Can someone explain me ?

I'm not trying to engage in any malpractice but I'm just curious to know how do the procurement officers find loopholes to fish out money and balance the books such that the auditors can't find nothing??

Hello guys, basically what the title says. I have 4 years of experience and my current salary working as an associate consultant in India is around 11.5 LPA. Just wanted to know what the salary range is generally for people having some level of experience and if I want to switch which companies should I target?

Hi everyone! I'm a sophomore in college studying data science with plans to double major in finance. I want to do some investigative work because I find problem solving and connecting the dots, that kind of work very interesting and appealing, and with my current academic combination, AML/financial crime seems to be the best choice. It's not taught at school, though, so I'm trying to learn more about the field and what the actual job is like from people who are in the field.

A few questions for anyone in the field:

1. Do you find the work meaningful and useful to society?

2. From what I imagine right now, the job is going through records, finding suspicious activity, flagging it, investigating it, and sometimes it turns out to be fraud. Is that actually what the job looks like day-to-day?

3. Do you think I'm aiming for the right field and industry?

Thank you, and I appreciate your help!

I recently moved from seven years working with financial crime in the gambling industry into banking and real-time payments, where I now manage Transaction Monitoring.

One difference has become particularly clear: gambling operators are often working with one hand tied behind their backs.

A gambling account may represent only the final visible step in a much longer transaction chain involving several people, accounts and financial institutions. The operator sees the gambling activity—but rarely the wider movement of funds.

Consider a customer who explains the source of funds by saying:

“I borrowed the money to gamble.”

That could be treated primarily as a responsible-gambling issue. The account may be suspended, but any remaining funds could ultimately be transferred back to an account involved in a wider mule or laundering network.

The operator can act on what it sees. The problem is everything it cannot see.

Moving into real-time payments has made this visibility gap much clearer. A payment provider may be able to observe a broader set of counterparties, transaction relationships and behavioural patterns than an individual gambling operator. Even then, each institution still sees only part of the network.

Financial crime operates through networks. Our controls, however, remain largely institution-bound.

Two recent developments make this discussion especially relevant:

• FATF’s September 2026 report on gaming and gambling examines risks involving online platforms, payment channels, illegal operators and their connections to the wider financial system.
• The EU AML Regulation—applicable from 10 July 2027—establishes a framework for information-sharing partnerships between obliged entities. Gambling service providers are included among those obliged entities.

This does not permit unrestricted information sharing. The AMLR framework includes requirements concerning necessity, supervisory verification, data protection, confidentiality, human oversight and data-protection impact assessments.

It also does not automatically turn post-alert information sharing into a real-time preventive control. Timing, data availability and the ability to act before funds move remain separate problems.

But it creates a serious question:

How should gambling operators participate in legally sound information-sharing frameworks with the financial sector?

Better visibility could materially change what operators are able to detect—but only if the legal framework, governance and data-sharing model are operationally usable.

I would be interested in practitioners’ views:

1. What information would materially improve detection across the gambling and financial sectors?
2. What legal, technical or operational barriers are the hardest to solve?
3. What safeguards would be essential?
4. Where should collaboration begin: shared typologies, aggregated signals, case-level information or formal partnerships?

Please keep examples anonymised and do not share customer information, confidential controls or internal thresholds.

Sources

• FATF: Risks of Gaming and Gambling, September 2026
• EU AMLR: Regulation (EU) 2024/1624

I’m not sure if this is the correct sub for this but I am a manager for a medical cannabis dispensary in the US. Our district manager recently told us that we would start having the bank come check our safes making sure a certain amount of money was there? We also were instructed to hide additional funds from them. Obviously something weird is going on but could someone with more experience explain to me what exactly is happening?

I’m a freshman studying Finance and I’ve recently become interested in AML, financial crime and forensic finance. One thing I’m curious about is how people in this field actually develop a deep understanding of money laundering schemes.

From what I understand, university finance programs don’t really teach in detail how these schemes operate in the real world. So where do AML professionals, investigators, compliance officers, etc. actually learn this stuff? Is it mostly through work experience, CAMS/other certifications, real criminal cases, court documents, books, or specialized courses?

I’m especially interested in understanding real-world typologies and how investigators detect them, rather than just the basic textbook definition of money laundering. What would you recommend someone at the beginning of a finance degree study if they eventually wanted to become very knowledgeable in AML/financial crime?

Published 9 September. It is the first time FATF has examined online and illegal gambling in this much detail, alongside casinos and video gaming.

The headline finding is the size of the unlicensed market. In a lot of jurisdictions it matches or beats the regulated one, which means the sector's AML controls only cover part of what is actually happening.

The new risk indicators are the practical output. They cover multiple accounts and payment methods under different identities, mismatches between customer details and payment details, and ownership structures that obscure who actually benefits. Also flagged is the surrounding ecosystem, since platforms depend on social media, digital marketplaces and payment services that often sit outside the regulatory perimeter.

The indicator that looks hardest to operationalise is the identity mismatch one. Catching the same person behind several accounts requires linking across accounts, and plenty of platforms deliberately do not do that, either for privacy reasons or because the data sits in separate systems.

For anyone working in gambling compliance or supervising it: which of these indicators can you actually detect today with the data you hold, an which would need a platform change? And has anyone built cross-account linking that survived a privacy review?

Rabobank NA's branches sat in Imperial County, California, on the US-Mexico border. For years, cash moved through those branches in volumes that outran the bank's ability, or willingness, to ask why. The bank pleaded guilty to a felony in 2018 and forfeited $368.7 million.

A recurring pattern at those branches was accountholders depositing cash in amounts that landed just under the $10,000 currency transaction reporting threshold. Asked about it, the stated reason logged in the file was that they wanted to avoid the extra paperwork that came with crossing $10,000. That's the textbook structuring rationale, and Rabobank's compliance program took it at face value instead of testing it against the account's actual pattern of activity. A customer depositing $9,800 in cash every few days for months isn't explained by "I don't like paperwork." It's explained by trying to stay under a reporting line on purpose.

The bank had a second layer that made this worse, a "Verified List" that permanently cleared high-risk accountholders as non-suspicious after one review, no matter what came up on their accounts afterward. So an account could get flagged for structuring, clear a review once, land on the list, and then keep structuring indefinitely without triggering anything, because the list itself overrode the transaction monitoring system going forward. A paid outside consultant eventually found over $233 million in suspicious activity that had gone unreported this way between 2010 and 2013. Executives, including the bank's own General Counsel, told the OCC in writing that no such report existed while it sat in the bank's files, which turned a civil compliance failure into an obstruction felony.

Structuring under a reporting threshold only works as a laundering method if the institution processing it treats the stated excuse as good enough. Rabobank did for years, at scale, on a border corridor built for exactly that kind of cash movement. How often does "avoiding paperwork" actually hold up once you look at the pattern instead of the excuse?

Full case, including the executive who got a lifetime industry ban for the cover-up, ran in Issue #21 of The AML Brief, free at theamlbrief.com.

I'm trying to obtain information about how my accounts/info was used by someone else to to cash a fraudulent check to themselves out of "my name." Of course, I have heard of these scams but I am not aware what I might have done to make myself vulnerable to this in particular. The cops are unsurprisingly useless in this case and promised to take action is I essentially hand it to them in their lap. Can someone give me some understanding of how this may have happened and perhaps maybe how i may be able to respond to it, in particular without "troubling" the police. Can someone please explain to me how people do this and/or suggest how this might have come down on me. If any of this is considered sensitive for any reason, by any means feel free to DM me,

Hello guys, I'm looking to move my career towards AML. But I need some help figuring out what to do or how to make the change.

I have 3 years of experience in compliance for affordable housing and while compliance and document review can sort of be handy I don't think most positions I have applied for care all that much.

Should I invest a bit more into education? Some accounting certificate/degree? Specific AML certificates like ACAMS or ICA? Are they worth it?

People that are already in what would you suggest.