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Core AML/CFT model

Core AML/CFT model for enhanced transaction monitoring and customer due diligence

Introducing our advanced AML/CFT model, a game-changing solution for all banks seeking to elevate their technology-driven risk controls.

Developed in collaboration with leading banks, this model is designed to dramatically improve the precision of transaction monitoring while significantly reducing operational expenses through efficiency gains.

Key features

Customers are assigned a dynamic risk score based on 12-month transactional behavior, patterns, and interactions. High-risk scores trigger alerts for further investigation by the FI's compliance team.

Alongside the model score, the Consilient information allows the organization and, importantly, the investigators to understand the key factors behind an account's flagging. This allows the organization to pinpoint the key transactions to investigate quickly and the high-risk activity that has driven the alert.

Federated learning aggregates knowledge from diverse institutions (e.g., banks, regulators, financial intelligence units) while maintaining data locality.

This model benefits from this diverse and extensive dataset identifying accounts that are generating suspicious activity.

This collaborative approach enhances the model’s ability to identify patterns and risks that might be missed by systems relying on isolated datasets.

Continuous learning is crucial for machine learning models' ongoing relevance and effectiveness, particularly in dynamic and evolving environments. It allows them to adapt to new patterns and trends.

Adversarial evolution: In areas like AML, criminals actively adapt their methods to evade detection. Continuous learning ensures machine learnign models stay ahead of these tactics. Regular updates refine the model's understanding, improving accuracy and reducing errors in prediction or classification.

In areas like AML, criminals actively adapt their methods to evade detection. Continuous learning ensures machine learning models stay ahead of these tactics. Regular updates refine the model's understanding, improving accuracy and reducing errors in prediction or classification.

Consilient utilizes XGBoost (Extreme Gradient Boosting) to deliver fast, accurate, and scalable models ideal for banking transaction data. With tools for feature importance, overfitting control, and bias-variance management, XGBoost ensures reliable performance. Validated in real-world settings, it provides a ready-to-deploy solution for AML/CFT challenges.

Federated learning allows machine learning models to be trained directly on local datasets within the confines of an organization's secure infrastructure. Only the aggregated model updates, not raw data, are shared across participants.

By avoiding centralized data pooling, federated learning minimizes the risk of data breaches, reducing the security concerns often accompanying inter-agency or inter-institutional collaborations.

Consilient uses the Ensembling technique to address key challenges in developing precise and efficient models. Ensembling in federated learning leverages the strengths of local models while mitigating their weaknesses.

By integrating diverse insights without compromising privacy or efficiency, ensembling creates a robust, accurate, and scalable approach to federated learning, 

Traditional AML machine learning models often rely on SAR training data. However, many SARs are filed defensively rather than in response to genuine risk.

The Consilient approach instead incorporates multiple typology models, many trained on specific high-risk behaviors like those of MSBs or precious metals dealers. This makes it easier to screen for a broader range of risks.

Key benefits

Dramatic reduction in false positives

High volumes of false positives can overwhelm compliance teams and inflate operational costs.

Our Federated AML/CFT Model has achieved an 88% reduction in false positive alerts, enabling your team to concentrate on high-priority cases and significantly improving operational efficiency.

Exceptional precision in detection

The model's extraordinary precision significantly reduces the risk of undetected financial crime, providing your bank with more robust, more reliable safeguards.

Our model delivers outstanding precision in detecting suspicious activities, enabling organizations to achieve a new level of accuracy in their AML/CFT efforts. The high detection rate ensures that genuine threats are identified swiftly, enhancing your bank’s ability to prevent financial crime effectively.

Seamless integration with existing systems

The model’s impressive results are realized alongside standard (heuristic) transaction monitoring rules, with no need for additional investigations.

This ensures that your bank can quickly and easily integrate the model into existing systems, realizing immediate benefits and anticipating highly positive new detections.

Dramatic reduction in false positives

High volumes of false positives can overwhelm compliance teams and inflate operational costs.

Our Federated AML/CFT Model has achieved an 88% reduction in false positive alerts, enabling your team to concentrate on high-priority cases and significantly improving operational efficiency.

Exceptional precision in detection

The model's extraordinary precision significantly reduces the risk of undetected financial crime, providing your bank with more robust, more reliable safeguards.

Our model delivers outstanding precision in detecting suspicious activities, enabling organizations to achieve a new level of accuracy in their AML/CFT efforts. The high detection rate ensures that genuine threats are identified swiftly, enhancing your bank’s ability to prevent financial crime effectively.

Seamless integration with existing systems

The model’s impressive results are realized alongside standard (heuristic) transaction monitoring rules, with no need for additional investigations.

This ensures that your bank can quickly and easily integrate the model into existing systems, realizing immediate benefits and anticipating highly positive new detections.

Achieve unmatched accuracy and optimal efficiency

Our Federated AML/CFT Model is an exceptional product for large and mid-tier banks looking to leverage cutting-edge technology to enhance their risk controls and achieve substantial operational savings.

Its unparalleled detection accuracy, combined with a dramatic reduction in false positives, provides a powerful tool to strengthen your bank’s compliance and risk management strategies while improving efficiency and reducing costs.