An exclusive article by Fred Kahn
Federated learning is emerging as a crucial innovation in the ongoing battle against financial crime, which remains one of the most persistent and damaging threats to the integrity of the global financial system. Banks, fintechs, and other financial institutions continue to face unrelenting pressure to stop illicit funds from entering the ecosystem. Despite progress, traditional Anti-Money Laundering (AML) technologies still struggle with challenges related to data privacy, cross-border collaboration, and adapting to new threats.
Federated learning is now emerging as a game-changer in the fight against money laundering. This privacy-preserving form of artificial intelligence enables multiple institutions to train machine learning models on their own data, without ever sharing raw customer information or exposing sensitive details. Instead, the algorithm itself travels between institutions, learning from local data and sharing only the resulting model updates.
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How Federated Learning Improves AML Outcomes
The idea behind federated learning was originally developed to address privacy and data security challenges in consumer technology. Its application to AML is recent but promising, especially as regulators such as the European Union’s General Data Protection Regulation (GDPR) and the new EU Artificial Intelligence Act have placed strict boundaries on data sharing across borders. By design, federated learning aligns with these privacy mandates and allows institutions to benefit from collective intelligence, without undermining the confidentiality of customer data.
Boosting Detection Accuracy Without Exposing Data
Money laundering networks often exploit weaknesses that occur when financial institutions work in isolation. Since criminal transactions may touch multiple banks or regions, traditional AML systems—relying only on local data—can miss the big picture. Federated learning bridges this gap. By allowing models to be trained collaboratively on patterns and typologies discovered in many different institutions, the system becomes more attuned to sophisticated and rare criminal behaviors that might otherwise escape notice.
For instance, an AML model trained through federated learning may “learn” that a particular transaction sequence, seen in one country, correlates with a suspicious customer profile in another. This combined intelligence leads to higher detection rates of complex laundering techniques, such as layering through cross-border wires or structuring deposits across several institutions.
Reducing False Positives and Analyst Fatigue
One of the main frustrations for AML professionals is the high rate of false positives generated by traditional transaction monitoring systems. Too many legitimate transactions get flagged, resulting in wasted analyst time and unnecessary regulatory reporting. Federated learning helps address this by creating richer, more nuanced models that can better distinguish genuine threats from normal activity. This improvement is especially valuable for institutions dealing with high transaction volumes or complex, multinational customer bases.
Enabling Collaboration Without Sacrificing Data Privacy
Privacy and data sovereignty are central concerns in any discussion about information sharing. Laws such as the GDPR and national data localization rules strictly prohibit the transfer of sensitive personal data outside specific jurisdictions. Federated learning sidesteps this issue entirely. Each institution’s data remains local and protected. Only model weights or updates—never the raw customer information—are shared. These updates can be encrypted or otherwise anonymized for added security, ensuring compliance with regulations and reassuring customers that their information is not being traded or pooled without consent.
Supporting Global and Cross-Border AML Efforts
Financial crime rarely respects borders. International standards, such as those set by the Financial Action Task Force (FATF), call for cross-border cooperation and information sharing. However, sharing customer data across national boundaries can be a regulatory minefield. Federated learning enables institutions in different countries to participate in joint model training, thus enhancing global AML efforts while fully respecting the sovereignty of local data.
Keeping Pace with Emerging Threats
Criminals are always innovating, seeking new methods to move illicit funds through the financial system. AML technology must evolve just as quickly. Federated learning’s flexible architecture allows institutions to continually update and improve their detection models as new risks emerge—without requiring a massive data transfer or wholesale system replacement. This adaptability is critical as new payment technologies, cryptocurrencies, and digital banking services reshape the financial crime landscape.
How AML Software Vendors are Leveraging Federated Learning
Several forward-thinking vendors are leading the charge in deploying federated learning for AML and financial crime compliance. Each approaches the challenge with different technical strategies, but all share the goal of delivering more effective and privacy-compliant AML solutions.
Lucinity: Privacy-Preserving Collaboration
Lucinity, an AML software vendor headquartered in Iceland, has developed a patented federated learning framework designed specifically for financial crime compliance. According to Lucinity, their system enables multiple banks and fintechs to participate in a shared model training process, with all sensitive data remaining on-premises.
Lucinity’s solution incorporates privacy-enhancing technologies such as homomorphic encryption and differential privacy, making it possible to aggregate learning from diverse sources without the risk of data leakage. The company reports that its federated models demonstrate improved detection of complex money laundering typologies, particularly those involving cross-institutional transactions.
Consilient: AI for Rare Event Detection
Consilient is another pioneer, with an AML platform built from the ground up around federated machine learning. Their system is aimed squarely at one of AML’s biggest technical challenges: the detection of rare and highly complex events that evade conventional rules-based monitoring.
Consilient’s solution allows each participant to train the AI model on its own data, then contributes only model updates (not transaction details or personal information) to the collective learning process. This approach, the company asserts, leads to sharper detection capabilities and a dramatic reduction in false positives.
Tookitaki: Networked AML Intelligence
Tookitaki’s FinCense platform leverages federated learning as a core feature, particularly for institutions operating in Asia-Pacific and other regions with strict privacy regimes. Tookitaki’s federated approach facilitates “collective intelligence”—allowing institutions to collaboratively improve their AML models by sharing non-sensitive model data, but never the underlying transaction or customer records.
The company’s system is designed for both scalability and customization. Institutions can participate in the federated learning network at their own pace, updating their models as new criminal techniques emerge. Tookitaki highlights that its clients have reported higher quality alerts and fewer false positives after switching to federated learning-enabled solutions.
Flower: Open-Source Frameworks for Regulated Sectors
While some vendors offer end-to-end proprietary AML solutions, Flower provides a popular open-source federated learning framework that is increasingly adopted by regulated institutions, including challenger banks and fintechs. Flower’s technology allows institutions to customize their federated learning deployment—choosing their own security settings and model architectures, while still participating in collaborative model training across organizational boundaries.
Institutions using Flower’s framework can comply with national data residency and privacy laws, while benefiting from more accurate, collective AML intelligence. Flower has been used by organizations like Banking Circle to improve cross-jurisdictional monitoring without breaching local privacy requirements.
FinRegLab: Research and Industry Guidance
FinRegLab, a nonprofit research organization, has published influential research on federated learning in AML. Their studies, conducted in partnership with financial institutions and regulators, confirm that federated learning can significantly improve detection rates and reduce false positives, without exposing sensitive personal data. FinRegLab is also developing best practices and governance frameworks to help the industry standardize federated learning in regulated environments.
Overcoming Challenges: What AML Teams Must Consider
Implementing federated learning in AML does require addressing certain obstacles:
- Technical Complexity: Federated learning needs robust IT infrastructure and skilled personnel in machine learning, cryptography, and compliance technology. Smaller institutions may need to partner with vendors or consortia to access these capabilities.
- Standardization and Interoperability: Effective collaboration depends on agreed-upon standards for data formats, model structures, and communication protocols. The industry is making progress here, but achieving full interoperability remains a work in progress.
- Trust and Governance: Trust among participating institutions is paramount. Clear governance models are essential, defining roles, data access policies, and model validation procedures to prevent abuse or manipulation of the learning process.
- Bias and Explainability: AML models—especially those built collaboratively—must be transparent, explainable, and regularly audited to ensure they do not propagate or amplify bias, and that their risk-scoring logic can be traced and justified.
- Regulatory Alignment: Regulators may need to adapt or clarify existing guidelines to explicitly support federated learning as a privacy-enhancing mechanism. Some jurisdictions are already signaling openness to these new techniques, especially as they align closely with existing privacy frameworks like the GDPR and recommendations from global bodies like the FATF.
Future Directions for Federated Learning in AML
The adoption of federated learning is still in its early stages, but several trends suggest it will become increasingly central to AML and financial crime compliance in the years ahead.
- Integration with Secure Multiparty Computation and Blockchain: These advanced cryptographic techniques can further strengthen the privacy guarantees of federated learning, making it virtually impossible for sensitive information to leak during collaborative model training.
- Industry Consortia and Sandboxes: Financial institutions, technology vendors, and regulators are forming alliances to test federated learning in controlled environments (“sandboxes”), developing shared governance standards and technical benchmarks.
- Expansion to Other Compliance Areas: Beyond AML, federated learning is being explored for fraud detection, sanctions screening, counter-terrorist financing, and even ESG (Environmental, Social, and Governance) risk scoring.
- Regulatory Support: Continued engagement between the industry and regulators will be critical, ensuring that best practices for federated learning are codified in official guidance and supervisory frameworks.
Conclusion: Federated Learning is Shaping the Future of AML
Federated learning is more than just a technological innovation; it’s a fundamental shift in how the financial industry approaches collaboration, privacy, and the fight against money laundering. By enabling institutions to share intelligence without sharing data, federated learning aligns perfectly with the evolving demands of regulators and the realities of cross-border financial crime.
Institutions adopting federated learning are already seeing meaningful improvements in detection rates, reduced false positives, and enhanced compliance with global privacy laws. The technology is not a panacea, but it is a powerful enabler—one that will likely underpin the next generation of AML and financial crime compliance solutions.
As industry standards mature and regulatory frameworks adapt, federated learning has the potential to drive a new era of trust, effectiveness, and agility in the global effort to stop money laundering.
Related Links
- General Data Protection Regulation (GDPR)
- EU Artificial Intelligence Act
- Financial Action Task Force (FATF) Recommendations
- Bank Secrecy Act (BSA) Regulations
- FinRegLab (Non-Profit): AI and Federated Machine Learning in AML
Other FinCrime Central News About Federated Learning
- How Federated Learning Can Enhance AML/CFT in Fund Management
- Using Federated Learning for AML in Hong Kong Banks
