The Payments Podcast · 2026-08-18 · 16 min
Key moments - from our scoring
Substance score
63 / 100
Five dimensions, 20 points each
Organized fraud has evolved into a networked business model where criminals share tools, techniques, and expertise to target commercial payments across multiple institutions simultaneously. Traditional fraud detection networks were designed for consumer payments and lack the visibility needed for B2B transactions, where relationship dynamics, transaction values, and risk patterns differ fundamentally. Dalit Amitai, Head of Product and Technology for Cyberfraud and Risk Management at Bottomline, discusses why banks historically resisted sharing fraud data due to reputational and legal concerns, but increasingly recognize that AI-powered impersonation attacks, deepfakes, and highly personalized social campaigns require industry-wide collaboration to combat. The episode examines real-world schemes like structuring attacks across multiple banks to stay below individual thresholds and explains how fraud consortiums reduce false positives, improve investigator efficiency, and provide account intelligence that builds confidence in legitimate payees. Recent regulatory developments including FinCEN's Section 314(B) guidance updates and European payment reforms are accelerating this shift, making fraud intelligence sharing a critical industry imperative. The conversation emphasizes that successful collaboration depends on robust data governance, encryption, anonymization, and clear controls - distinguishing between sharing data (risky) and sharing intelligence (value-creating) while maintaining privacy and regulatory compliance.
Fraudsters target the same fraudulent accounts across multiple banks knowing that when Bank A identifies fraud, Banks B and C remain unaware and continue processing payments to the same account, maximizing theft opportunities.
Structuring attacks where fraudsters split a $100,000 theft into ten $10,000 payments across ten different banks and customers - each bank sees normal activity, but the consortium detects a coordinated attack targeting a single receiving account.
Commercial payments involve different relationships, transaction values, volumes, and risk patterns than consumer transactions, making consumer-focused networks unable to provide relevant intelligence for business-to-business fraud detection.
When a consortium confirms an account as legitimate based on multi-year relationships across multiple banks, banks gain confidence to approve legitimate transactions, reducing friction for customers and shrinking the investigator workload required to review suspicious alerts.
Strong encryption, anonymization protocols, clear data governance, strict access controls, transparency into contributed and consumed data, and operation within defined regulatory frameworks are necessary to distinguish between sharing sensitive data versus sharing useful intelligence.
Our reviewer’s read on each dimension, with quotes from the episode.
The episode delivers several substantive ideas about cross-institutional fraud collaboration, including the specific example of fraudsters splitting large transactions across banks to evade detection thresholds and the distinction between shared data vs. shared intelligence. However, the core insight - that fraudsters operate as networks and banks must do the same - is relatively straightforward, and much of the conversation revisits the same points. The episode lacks novel frameworks or surprising findings that would elevate it further.
Fraudsters don't think in terms of Bank A, Bank B, or Bank C. They think about the entire banking system.
They'll take 10,000 from 10 different customers at 10 different banks and send it all to the same account. So when every bank looks in its own system, nothing looks unusual. But if we zoom out and we look across the network, it's obvious that there's a coordinated attack happening.
The core thesis - that banks need to share fraud intelligence to combat organized crime networks - is sensible but not particularly novel or contrarian. The specific example of sub-threshold splitting attacks is concrete and useful, but the broader framing of consortia-based defense and regulatory tailwinds is fairly mainstream industry discourse. The episode lacks first-principles questioning or counterintuitive positions that would mark it as genuinely original thinking.
Fraudsters love hiding in gaps, and consortia help us connect those gaps and see the bigger fraud picture.
Fraud consortiums aren't one-size-fits-all either.
Dalit Amitai holds a legitimate product and technology leadership role at a major payments compliance vendor, giving her relevant operational exposure to the problem space. However, she is speaking primarily as a vendor promoting her own company's solution rather than as an independent practitioner or researcher with hands-on experience managing fraud at a bank or enterprise. This limits her caliber relative to a Fortune 500 CFO or actual fraud investigator at a major financial institution.
Dalit Amitai, Head of Product and Technology, Cyberfraud and Risk Management at Bottomline.
the main and the first use case that we're planning to offer is what we call pay intelligence.
The episode includes one strong concrete example (the $10k splitting attack across 10 banks to hide a $100k theft) and mentions specific regulatory initiatives (FinCEN Section 314(B), European payment service reforms, Treasury's 2026 Risk Assessment, and the Prince Group case). However, these regulatory and macro-level examples are referenced without detail, and there are no named bank deployments, quantified fraud loss metrics, false positive reduction percentages, or specific time-horizons for implementation. The absence of hard data about consortium effectiveness is a notable gap.
I'll take 10,000 from 10 different customers at 10 different banks and send it all to the same account.
FinCEN's updated Section 314 (B) Guidance in the U.S. and European payment service reforms
Owen McDonald asks competent, on-topic follow-up questions that probe deeper into the collaboration thesis and regulatory drivers. However, the conversation lacks meaningful pushback, challenging assumptions, or productive disagreement. The host does not press on implementation barriers, cost-benefit trade-offs, competitive resistance from banks, or the tension between data-sharing and real-world regulatory compliance. The exchange feels more like a structured vendor briefing than rigorous inquiry.
Why do commercial payments present a different intelligence challenge, Dalit?
What are some real-world fraud schemes that banks would most likely miss on their own?
Computed from the transcript - who did the talking, and the words that came up most.
Fraudsters share information and evolve their tactics quickly. So why are financial institutions still fighting fraud in isolation? In this episode, Owen McDonald is joined by Dalit Amitai, Head of Product, Cyberfraud & Risk Management at Bottomline, to discuss how intelligence sharing and industry collaboration can help organizations strengthen their defenses, identify threats faster, and stay ahead of increasingly sophisticated fraud schemes. #FraudPrevention #RiskManagement #Banking #Payments #FinTech
Transcribed and scored by The B2B Podcast Index.
Speaker 0: The Payments Podcast from Bottomline Owen McDonald: Welcome to The Payments Podcast. I'm your host, Bottomline Managing Editor, Owen McDonald. Let's set the mood. It's June 2026, and the US Treasury is taking further action against a Cambodia-based conglomerate, Prince Group, which it alleges is a "transnational criminal organization." Treasury alleges that, prior to its designation, the group oversaw a worldwide money-laundering network. Three months earlier, Treasury's 2026 National Money Laundering Risk Assessment warned that professional fraud networks are providing criminals with expertise and economies of scale. That activity is targeting B2B payments in many ways because fraudsters are helping each other. Banks and their PSP partners are banding together to share fraud intelligence and fight back against this new cooperative fraud typology. For a better sense of the trend, we're delighted to welcome Dalit Amitai, Head of Product and Technology, Cyberfraud and Risk Management at Bottomline. Dalit Amitai, you are most welcome back to The Payments Podcast. Dalit Amitai: Hey, Owen. Great to be here. I'm excited to talk about sharing intelligence, fraud intelligence. Fraudsters love hiding in gaps, and consortia help us connect those gaps and see the bigger fraud picture. Owen McDonald: So, Dalit, fraudsters increasingly operate as coordinated international business networks, often using AI to make their attacks faster and more convincing. How are they exploiting the fact that banks still tend to defend commercial payments within their own institutional silos? Dalit Amitai: The challenge is, Owen, that fraudsters don't think in terms of Bank A, Bank B, or Bank C. Right. They think about the entire banking system. Owen McDonald: Right. Dalit Amitai: Let me give you an example. The fraudsters will open a fraudulent account and then start targeting payers at multiple banks. Maybe Bank A figures out that the account is bad, but Bank B and C don't know that. If that information about the bad account isn't shared, payments can still be sent to the same account from another institution. Sharing such information across banks that an account is fraudulent will help all participants in the consortium. I hope this makes sense. Owen McDonald: It does indeed. It sounds dead on, in fact. Now, banks have historically been reluctant to share fraud information because of reputational, legal, and competitive concerns. What is changing that mindset, Dalit, and why is collaboration becoming more important in protecting business customers from organized crime? Dalit Amitai: Honestly, their hesitation was completely understandable. No bank likes to admit that they're being attacked. Right? Owen McDonald: Right. Dalit Amitai: There are concerns about reputational damage, about customer confidence. Another very important concern, of course, is protecting sensitive data. Information about fraud, if we were to share it, includes account numbers and personal data. The last thing a bank needs is for information related to fraud incidents to create additional risk or become public. And you asked, what is changing? What is changing the mindset? What's changed is really the threat landscape, the fraud landscape. Fraudsters have become much more sophisticated, like you mentioned before, AI is accelerating the trend. Things like impersonation attacks, deep fakes, spoofed websites, and super highly personalized social campaigns are becoming so easy and cheap for criminals to execute. So banks understand that this is no longer a fight that they can win on their own. Fraudsters are collaborating, like you mentioned. It's a network. They share tools. They share techniques. And to get the upper hand back, banks need to do the same. Owen McDonald: Right. It makes perfect sense. I'm almost a little surprised it hasn't happened before now, but we'll get to that. Many established fraud information networks were designed around consumer or person-to-person payments. Why do commercial payments present a different intelligence challenge, Dalit? What risks can be missed when banks lack visibility into B2B payer and payee activity? Dalit Amitai: That's a great question, Owen. And I think the key point is that, just as fraud detection isn't one-size-fits-all, fraud consortiums aren't one-size-fits-all either. So many existing networks today are built around consumer and person-to-person payments, and they do a great job in this space. But commercial payments are different. The relationships are different. The transaction values are different. The transaction volumes are different, and the risk patterns are also different. So, at a very simple level, a consortium helps answer the question, "What do we know about this account? But if I'm a business, right, I'm a business, and I'm paying another business, I will not catch much value from shared intelligence that covers consumer accounts. I'm dealing with a completely different ecosystem. Owen McDonald: So it's that simple, in other words. Dalit Amitai: If a business wants to send a payment to a supplier for the first time. Right? That's the example. The bank wants to understand whether the payee looks legitimate. Are we moving money, sending money to a legitimate account? Owen McDonald: Right. Dalit Amitai: They want to know if other institutions saw suspicious activity associated with that account, and this is where a specifically focused commercial payments, business payments consortium will have that intelligence. Owen McDonald: I'm very curious. I want to drill down on that. What are some real-world fraud schemes that banks would most likely miss on their own, but that become visible when institutions collaborate through the kind of fraud consortium you're describing? Dalit Amitai: So, actually, multiple value points or use cases where the consortium will help. I gave the example before of just sharing the fraudulent accounts, a list of fraudulent accounts, but I want to give another example. Fraudsters know how detection works. They are smart. So they are very good at staying below the radar. Let's say that a bank typically pays close attention to payments over $10,000. If I'm a fraudster and I'm trying to steal $100,000, I'm not going to take it from one customer at one bank. I'll take 10,000 from 10 different customers at 10 different banks and send it all to the same account. So when every bank looks in its own system, nothing looks unusual. But if we zoom out and we look across the network, it's obvious that there's a coordinated attack happening. That's just one example. But there are other advantages or unique value propositions that really come from sharing intelligence. A lot of it is focusing obviously on catching fraud, but a consortium provides additional context. So, one big challenge is that fraud detection usually comes with a lot of false positives. Just because a payment looks suspicious doesn't mean that it is fraudulent. Right? And every time a legitimate payment is stopped or delayed, it creates friction for customers who just want to run their business. Right? If I'm buying a property and this is an unusual payment, it is suspicious, but it's legitimate. Owen McDonald: Sure. Dalit Amitai: So this broader view helps separate real fraud from activity that might seem unusual at first, but is actually legitimate. Because if the consortium is familiar with the account, if other banks saw the account and they see the account and familiar with it for say, three years, and we know that six banks are familiar with it for six years, it gives the level of confidence that this is a valid account and removes the risk, reduces the number of false positives, and the friction with customers. So the other benefit is really an operational benefit. Most banks, they have highly skilled fraud analysts that spend their days investigating suspicious payments. The more false positives that the solutions generate, the larger those teams need to be, right? It comes with a cost. So with a consortium, it will improve the accuracy of alerting, and it will help investigators spend their time investigating the highest risk cases instead of reviewing a large volume of legitimate activity. So the consortium helps in many, many ways. Owen McDonald: I hadn't planned on asking this, but can I ask you to quickly describe, within the context of what you just said, Bottomline's new service, which is known as Fraud Intelligence Exchange? Doesn't it address these very things? I believe it does. Right? Dalit Amitai: It does. And, really, the main and the first use case that we're planning to offer is what we call pay intelligence. So, in the first example I gave, fraudulent accounts will definitely be shared among participants in the network. But the other value is that we can provide what we call account insights or account risk indicators. Those will be about payment patterns and the augmented legitimacy of the account. The pattern, the date range of activity, the more we know about the account, the better it is for the participants and the more confidence they can get about good accounts. So the value is not limited to alerting only on fraudulent accounts, but also on good accounts that will help banks reduce the friction and improve operational efficiency. Owen McDonald: Right. It sounds like the perfect thing for the moment that we're experiencing. Last question. My goodness. This has gone quickly. Recent developments, including FinCEN's updated Section 314 (B) Guidance in the U.S. and European payment service reforms are placing greater emphasis on fraud information sharing. Dalit, how will this affect commercial CFRM teams, and what safeguards around anonymization, encryption, data governance, and permitted use are necessary for banks to collaborate with confidence? Dalit Amitai: I think these regulatory changes are important because they're sending a very clear message. Fraud intelligence sharing is no longer viewed as a nice-to-have. It's becoming a critical part of the industry, and it's a global trend. Right? For commercial payments teams, it means that they can move beyond looking at what is only happening within their own bank. That's really what we see here, and I will need to repeat myself a little bit if it's okay. Owen McDonald: Please. Dalit Amitai: Thank you. So this is important. Instead of investigating suspicious payments within a limited context, banks can make a decision based on signals that they see across institutions. So they can improve fraud detection, reduce false positives, and help investigators focus on alerts the three d matter. And you did ask about technology and about handling data, the right way. Right? Owen McDonald: You're right. Dalit Amitai: None of those really work unless banks trust the way data is being handled. Right? We need to have safeguards. Data should be shared in a way that will protect privacy. We have strong encryption, clear governance, and strict controls on who has access to the information. Banks are also asking for transparency into what data is being contributed and what data is being consumed. They need to know that to have assurance, really, that participating participants are operating within clearly defined regulatory and legal frameworks. That's super critical and super important. Because at the end of the day, banks do not want to share data. They want to share intelligence. This is the distinction. The goal is not to expose customer information. The goal is to help institutions identify risky accounts and suspicious patterns. Owen McDonald: So it's an important distinction and, clearly, we talk about zooming out and looking at the big picture, and that's kind of what this does and allows you to connect the dots as it were. Right? That's what it sounds like. Dalit Amitai: Exactly. And also, while maintaining the highest standards of privacy and security, if you combine this strong governance with modern technology, then the conflict between sharing data, right, and not sharing it is resolved because you get the value, but you're not risking the sensitive data. Owen McDonald: That's right. That's right. And there you have it. As organized crime takes a consortium-style approach to fraud, banks, corporates, and their PSP partners are striking back with new forms of cooperation, leaving the bad guys with fewer places to hide. That's the objective in any case, and collaboration offers a way to scale it and turn the tide. Sincere thanks again to Bottomline's Dalit Amitai for her razor-sharp insights. To our audience, the smartest people in B2B payments, thanks for listening. Hit subscribe. Catch us again on your favorite podcast platforms, including Apple, Spotify, Blubrry, iHeartRadio, and YouTube. Bye for now.
Speaker 0: The Payments Podcast from Bottomline.
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