The Challenges of Centralized Machine Learning in Finance

Federated Learning

Financial Crime

April 1, 2025

In today’s increasingly complex financial landscape, financial institutions are under pressure to detect and combat money laundering and other financial crimes. The sophistication of fraud tactics, coupled with the sheer volume of transactions, has escalated the challenge of protecting both institutions and their customers. While machine learning (ML) offers significant potential for improving Anti-Money Laundering (AML) efforts, centralized systems in use today still face substantial limitations. These challenges hinder financial institutions from utilizing ML to its fullest potential, leaving gaps in fraud detection and compliance.

Key Challenges Facing Centralized ML in AML Efforts

Despite its promise, centralized machine learning in the context of AML is plagued by a number of systemic challenges. Financial institutions, particularly banks, often find themselves unable to leverage ML effectively in their fight against financial crime. In this post, we’ll examine the key barriers that limit the success of centralized ML and the significant risks they pose to both operational efficiency and regulatory compliance.

Regulatory and Security Barriers to Collaboration

Money laundering and financial crimes are often orchestrated across multiple institutions. Criminals exploit gaps between banks, conducting fraudulent activities that span jurisdictions and financial organizations. While collaboration between institutions could provide a more comprehensive view of suspicious behavior, existing legal and regulatory frameworks severely limit this cooperation. Financial institutions are bound by stringent privacy laws, including GDPR, and banking secrecy regulations, which restrict the sharing of sensitive data across borders.

Furthermore, concerns over cybersecurity risks discourage banks from exchanging data, fearing potential breaches that could compromise customer privacy and expose them to legal and financial liabilities. Even when willing, financial institutions are reluctant to share proprietary data with competitors, concerned about losing their competitive edge.

Without a solution that allows for collaboration while maintaining strict privacy and security standards, financial institutions will continue to struggle in the fight against financial crime.

Data Scarcity: The Challenge of Insufficient Fraud Data

One of the most pressing issues facing ML models in AML is the limited availability of labeled data. Fraud detection systems require large, diverse datasets of fraudulent activities in order to learn to recognize patterns and make accurate predictions. However, financial institutions typically have access to a limited number of confirmed fraud cases within their own datasets. This creates a major data imbalance—while banks may have ample data on legitimate transactions, the data on actual fraud cases is sparse.

As a result, machine learning models struggle to identify complex or emerging fraud tactics, leading to a high number of false positives—where legitimate transactions are flagged as suspicious. This not only wastes valuable resources but also damages customer trust by disrupting legitimate business. The lack of fraud data also hampers banks’ ability to adapt to new and evolving criminal strategies, leaving them vulnerable to increasingly sophisticated financial crimes.

Without access to a broader and more diverse dataset, machine learning systems cannot effectively evolve to meet the ever-changing tactics of financial criminals.

High Operational Costs of AML Monitoring

Implementing and maintaining effective AML monitoring systems is costly. Financial institutions, especially smaller banks, are forced to allocate substantial resources to build and sustain ML models, data storage infrastructure, and compliance teams. This investment is essential to stay ahead of fraud, but it comes at a high price. Operational costs, including staffing, infrastructure, and training, significantly impact the ability of smaller financial institutions to remain competitive and effective in their AML efforts.

Moreover, even large institutions often struggle with the inefficiencies generated by false positives. Each flagged transaction requires manual investigation, diverting resources from more critical fraud cases and burdening compliance teams. This inefficiency is not only a drain on resources but also a threat to the institution’s ability to detect real criminal activity.

The cost of implementing and maintaining a robust AML system is unsustainable for many institutions, especially when returns are diluted by false alarms and limited fraud data.

Detection Gaps in Centralized Systems

Centralized ML models are constrained by the data that banks have access to, which fundamentally limits their effectiveness. Since each institution can only train models on fraud cases that it has already identified, the system becomes limited to recognizing only familiar patterns. If a bank has not encountered a particular type of fraud, its models will be unable to detect it. This makes it difficult to adapt to new fraud tactics, particularly those that span multiple institutions.

Moreover, since fraud detection remains siloed within individual institutions, even if one bank identifies a new fraud pattern, it cannot easily share that intelligence with others. This lack of communication and collaboration further weakens the overall system and leaves gaps in detection.

The inability to share fraud detection insights across institutions means banks are at a disadvantage when facing increasingly coordinated and sophisticated financial crimes.

The Risk of Change: FCC Officers and the Reluctance to Adopt New Approaches

As an FCC officer, you are tasked with ensuring that any new system implemented to fight financial crime is reliable and meets regulatory requirements. The potential for failure is a significant concern: if a new technology fails to detect a money laundering case, you, as an officer, will be held accountable. This is why many FCC officers are understandably hesitant to adopt new technologies, even if they hold the promise of improving fraud detection. The current systems, though imperfect, are understood and have established workflows.

Any new approach must guarantee reliability and prove its compliance with regulatory standards before it can be trusted. The consequences of adopting an unproven solution can be severe, and many officers prioritize stability over innovation to mitigate compliance risks.

For FCC officers, the challenge is not just about adopting new technology but ensuring that any new system is secure, reliable, and compliant with regulatory standards.

Conclusion

The challenges facing centralized machine learning in AML are clear: data scarcity, regulatory barriers, operational inefficiencies, and the limited ability to detect emerging fraud patterns. As financial institutions continue to grapple with these obstacles, the need for an innovative solution becomes more urgent. While centralized systems have their place, they are reaching their limits in terms of scalability and effectiveness. The future of AML technology lies in overcoming these limitations—enabling institutions to work together while ensuring privacy, security, and regulatory compliance.

Stay tuned as we explore how new technological innovations could transform the fight against financial crime, offering a way forward that addresses these pressing challenges without compromising on security or compliance.