Consilient

Federated Learning is the AML breakthrough that Regulators and FIUs have been waiting for. Why? Because financial crime is systemic, but supervision is still fragmented. Large institutions see patterns smaller firms never encounter. MSBs and fintechs surface behaviours banks may miss. FIUs struggle to share intelligence across borders. The result? Blind spots that criminals exploit. Federated learning makes collaboration part of the system, without moving data. ⚡ STRs can be measured and compared. ⚡ Rare typologies become visible. ⚡ Regulators and FIUs can demonstrate effectiveness under scrutiny. Full blog here: https://lnkd.in/e_DZ-7Gh

Scams at scale and why existing models can't keep up 👇📕

If you want to understand why scams scale, follow the money. Mule accounts are the conversion engine. Funds extracted through investment scams, BEC, or ransomware don’t just disappear. They move. Quickly. Across institutions. Often across borders. A typical chain looks like this: 1️⃣ Victim sends funds. 2️⃣ Money lands in a newly opened account. 3️⃣ Funds are rapidly transferred across multiple banks. 4️⃣ Proceeds are consolidated or converted. Each bank may see suspicious activity. Very few see the enterprise. Organized networks rely on fragmentation across institutions. Even strong internal monitoring can miss the distributed structure. Regulators are increasingly focused on earlier disruption, particularly around mule detection and authorized push payment fraud. Read more here ➡ https://lnkd.in/egrkNEvv

Child exploitation is not often addressed directly in AML discussions. More commonly, it sits within broader categories. Classified, structured, and reported through existing frameworks. But behind those classifications is a sustained, monetized system operating through the financial infrastructure AML teams oversee every day. This piece looks at: 🟣Why exploitation financing is often under-recognised 🟣 How modern payment systems enable scale 🟣 Why this should be treated as a core AML risk Read it here now: https://lnkd.in/eabc7MQE

We need to stop calling this “online fraud.” What we’re seeing now is industrialized financial extraction. The FBI reports more than $12bn in annual cyber-enabled fraud losses in the US. Investment scams alone account for over $4bn. And that’s just what gets reported. But here’s the part that doesn’t get enough attention: These aren’t isolated criminals. They are structured enterprises. ➡ Dedicated victim acquisition teams ➡ Scripted social engineering playbooks ➡ Malware and BEC toolkits ➡ Affiliate-based ransomware models ➡ Specialised fund movement operators In some jurisdictions, these ecosystems persist for years with limited disruption. The scam starts online. It becomes economically real when funds enter the regulated financial system. That transition point is where the conversation needs to move next. We’ve broken this down in detail here: https://lnkd.in/egrkNEvv

Financial crime networks are coordinated and structured.   Detection has to match that.   And that’s why Consilient and Sigma360 have partnered to integrate:   🟣Perpetual KYC (pKYC) 🟣Transaction monitoring 🟣Federated machine learning across institutions   … Into one single intelligence layer.   This is crucial because:   ➡️ The most serious financial crime patterns sit across multiple institutions   With this approach, institutions can: ✔️ Identify emerging threats earlier ✔️ Detect patterns that were previously missed ✔️ Reduce investigative workload   All while keeping data private.   And this is how financial crime detection becomes more effective at scale.   Learn more here: https://lnkd.in/eP37qiAG

$16 billion in reported US cyber-enabled fraud losses. $4+ billion from investment scams alone. And that’s only what gets reported. Learn more ⬇

We don’t often stop to think about what sits behind alerts, SARs, and transaction monitoring. But we should. In this new piece, Laurence Hamilton reflects on a difficult but important topic: how child exploitation is sustained through financial systems. And why it should be understood as a core AML risk, not a peripheral one. It’s not an easy read. It’s not meant to be. But it’s an important reminder of why AML exists beyond compliance. Read here: https://lnkd.in/eabc7MQE

Humans remain essential to AML: The research backs this ⬇ Across governance, ethics and law, researchers reach the same conclusion. Human oversight is not a safeguard that can be added at the end of the process. It is the mechanism that keeps high-stakes decisions accountable and defensible. The research makes this unavoidable: ↪Human control is required for accountability and for decisions that reflect context and moral reasoning. Davidovic’s work highlights that systems influencing rights or regulatory outcomes must remain under meaningful human authority. ↪Poorly designed oversight turns people into passive approvers. Green warns that without the time, context and authority to intervene, oversight collapses into a rubber-stamp process that adds no real judgment. ↪High-risk systems must include humans with the expertise to challenge automated suggestions. Legal analyses of the EU AI Act also highlight that oversight is not symbolic. It requires individuals who understand the logic behind the system and can question it when needed. ↪Core elements of investigation cannot be automated. Public-sector studies, including Enarsson’s work, show that discretion, contextual interpretation and situational understanding often fall outside what models can encode, yet these factors are central to investigative outcomes. ↪Regulators echo this position. They expect explainability, auditability and a clear record of how decisions were made, especially when those decisions carry legal or regulatory weight. And even AI developers acknowledge the limits. OpenAI’s own system cards caution that advanced models may produce incorrect information and require human review in consequential settings. Human oversight does not slow AML programs down.❌ It anchors them. AI can surface patterns and reduce noise, but the decisions that matter still rely on human intelligence, context and accountability.

Consilient’s view: Humans, Federated Learning and agentic AI ⬇ Consilient’s approach brings together three elements that strengthen AML programs without compromising human judgment. 1️⃣ Collaborative federated AI Learns from patterns across institutions without sharing customer data. Banks gain richer intelligence and stronger signals while maintaining full data protection. 2️⃣ Human-in-the-loop adjudication Keeps investigators in control of decisions. Their rationale improves model performance, builds trust in outcomes and supports regulatory compliance. 3️⃣ Agentic AI copilots Automate investigative tasks and assemble context so analysts can spend their time on true risk rather than manual processing. These components address the two core challenges in AML: ➡Scale and accuracy: AI surfaces behavior that static rules miss and reduces false positives, improving both coverage and efficiency. ➡Nuance and accountability: Human judgment remains at the center of decisions that require explanation, proportionality and regulatory defensibility. In this model, investigators do not step back. They step up. AI handles the volume. Humans make the decisions that matter.