How model intelligence is transforming financial crime detection

Financial crime is a global crisis, with an estimated $800 billion to $2 trillion laundered annually through the financial system. Alarmingly, less than 1% of these illicit transactions are detected. Despite significant efforts, many institutions are falling behind in combating money laundering and other financial crimes.

The core issues plaguing traditional Anti-Money Laundering (AML) processes are:

• Inefficiency: Many banks still rely on manual inputs and legacy systems, driving up costs and introducing a high margin for human error.
• Resource drain: In the U.S. alone, financial institutions spend approximately $180 million each year on salaries for AML analysts, primarily to prepare Suspicious Activity Reports (SARs).
• Low detection rates: Even with substantial resources dedicated to the task, success in identifying criminal activity remains frustratingly low.
• Siloed machine learning: While some institutions have implemented ML models to assist in identifying suspicious activity, these systems often operate in isolation, resulting in limited detection scope and a flood of false positives.

This is where advances in model intelligence come into play. Federated Learning, in particular, is transforming how financial institutions detect crime by allowing them to collaborate on risk detection without sharing sensitive data. This approach improves accuracy and enables institutions to stay ahead of emerging threats while reducing the burden on their teams.

The status quo of financial crime detection: Where the gaps lie

Many financial institutions still rely on outdated Anti-Money Laundering (AML) processes, characterized by:

#1. Limited Machine Learning (ML) implementation

Some institutions have integrated ML models to address inefficiencies. This is great progress. However, these models typically operate in isolation, using only their own institution’s data. As a result, this siloed approach misses broader patterns of financial crime spanning multiple organizations and jurisdictions.

#2. Overwhelming false positives

Isolated ML models tend to produce an excess of alerts. With so many alerts, AML teams struggle to identify genuine threats in a sea of noise. This not only increases the risk of missing actual suspicious activities but also makes it challenging to conduct timely investigations.

#3. Manual-heavy workflows

The flood of false positives places a significant burden on analysts, leading to delayed investigations. Not all alerts are investigated, creating risks for the organization and leaving gaps for money launderers to exploit. This reliance on manual processes not only strains resources but also leaves institutions vulnerable to undetected threats

Ultimately, it creates a false sense of security. While institutions may believe their ML models are effective, the reality is more complex. Without access to a wider pool of data and insights, these models struggle to tackle the evolving landscape of financial crime.

Model Intelligence: Federated Learning and its impact on financial crime detection

Federated Learning is a groundbreaking solution that allows multiple institutions to collaborate without the need to share sensitive data. This approach addresses one of the fundamental barriers in the fight against financial crime: the inability to work together across institutions and jurisdictions without compromising data privacy.

To put this into perspective, here’s a quick comparison of traditional machine learning models and Federated Learning:

This collaborative approach to model intelligence preserves privacy while allowing institutions to share crucial behavioral insights that would typically remain isolated. Federated Learning creates a more comprehensive understanding of criminal activities by uncovering patterns that traditional models might overlook. Through this pooling of intelligence, financial institutions leverage a more diverse and extensive dataset, enhancing the accuracy and effectiveness of financial crime detection.

Institutions then get the best of both worlds. 

It not only minimizes false positives but also enhances the identification of high-risk entities and suspicious activities across multiple organizations. This empowers financial institutions to outpace increasingly sophisticated money laundering schemes and criminal networks. Importantly, it achieves this without compromising data privacy or regulatory compliance.

Collaboration in action: How Federated Learning increases detection rates by 300%

Consilient’s Federated Learning solution has proven it can reduce false positives by over 80% and increase detection by 300%, particularly in scenarios where existing rules are not tailored to the portfolio. As we’ve discussed, these gains stem from Federated Learning’s ability to tap into a broader pool of insights, allowing for more comprehensive and accurate threat identification.

Consider this scenario: a single bank might detect unusual activity within its dataset, but without comparing this behavior to trends at other institutions, it may miss its connection to a larger pattern of coordinated financial crime. Federated Learning allows this bank to benefit from insights gathered across multiple organizations, enabling the detection of anomalies indicative of complex, sophisticated threats.

🔍In fact, in one Tier-1 US Bank’s case, Consilient improved efficiency by 75%.

Cross-institutional collaboration reveals patterns that individual organizations often overlook. Criminal networks operate across borders, exploiting gaps in isolated systems. Federated Learning bridges these systems, providing institutions with a broader view of criminal behavior. 

The bottom is line; by working together, financial institutions can stay ahead of criminals who constantly adapt their methods. Federated Learning turns collaboration into a strategic advantage, enabling more efficient and effective operations in the ongoing fight against financial crime.

Consilient: A solution for secure collaboration

Financial crime is evolving, but many institutions remain stuck in outdated methods. It’s time to challenge the industry: Why aren’t more companies investing in future-proof technologies like Federated Learning? The approach to AML has remained unchanged for too long, resulting in inefficiencies and missed threats. Investing in prevention, through collaboration and advanced model intelligence, is not just better—it’s essential. The cost of inaction is far greater than the investment required to stay ahead. By embracing Federated Learning, institutions can take proactive steps to outpace increasingly sophisticated money launderers.

Keen to learn more? Here are the key benefits of Consilient’s solution:

✅Enhanced control

• Identifies high-risk entities and abnormal behaviors more effectively
• Accesses a wider pool of behavioral insights across institutions
• Spots patterns of suspicious activity that may otherwise go unnoticed
• Enables proactive risk management and anticipation of emerging threats

✅Augmentation of existing tools

• Integrates seamlessly with current AML and risk management systems
• Incorporates diverse data from multiple institutions
• Improves accuracy and effectiveness of existing tools
• Detects new types of risks, especially those hard to identify with traditional models

✅Robust assurance

• Serves as a second line of defense for financial institutions
• Validates and reinforces findings of existing risk management systems
• Provides an additional layer of security
• Increases confidence in detection processes

✅Standardization of risk management

• Ensures consistent approach to financial crime detection across all branches
• Facilitates oversight and compliance demonstration to regulators
• Particularly valuable for large, complex organizations
• Streamlines AML efforts and achieves uniform risk management

▶To learn more watch this video: How Consilient Federated Learning works

Model intelligence: The future of financial crime detection

As financial crime grows increasingly sophisticated and global, traditional methods are no longer sufficient. Innovative approaches like Federated Learning are essential for staying ahead of emerging threats–reshaping how institutions detect and prevent illicit activities. 

By learning from multiple independent datasets, Federated Learning empowers banks to:

• Significantly reduce the number of false positive alerts,
• Lower operational costs,
• Improve the detection of criminal behavior using insights from a wide array of data rather than just their own data.

Additionally, Federal Learning provides organizations with regular updates and improvements as the model is continuously trained with data from both the bank and other participating entities.

Talk to us if you are keen to learn more about Federated Learning. We’d genuinely love to help.

_{Sources: https://www.bankdirector.com/article/the-high-cost-of-the-suspicious-activity-report/} | _{https://www.mckinsey.com/industries/financial-services/our-insights/banking-matters/network-analytics-and-the-fight-against-money-laundering}