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High-Risk Typology models
Federated Learning models for AML – Uncover hidden high-risk
typology models
Our cutting-edge federated learning models to uncover hidden high-risk typologies is a transformative solution designed to help financial institutions (FIs) proactively identify and mitigate risks associated with customers operating in globally identified high-risk sectors.
These high-risk sectors — including Money Service Businesses (MSBs), Precious Metals Dealers, Casinos, Not-for-Profit Organizations (NPOs), and emerging areas like virtual asset service providers (VASPs) — present heightened potential for illicit activities. Our solution equips financial institutions to identify customers who may be covertly operating within these sectors without disclosure. This ensures robust compliance, mitigates risk exposure, and upholds financial integrity with confidence.
Key features
Consilient has built over 15 high-risk typology scores based on patterns of those particular typologies. These scores, built using 12 months of behavioral activity, aid organizations in identifying heavy cash transaction-based businesses and high-risk sectors such as MSB, Casino, not-for-profit, precious metals, and others.
Organizations can use this score to identify hidden risks and customer due diligence activities. As an assurance tool, these scores enable organizations to manage clients and risks continuously.
This model benefits from a diverse and extensive dataset of known behaviors of high-risk typologies. By aggregating knowledge, federated learning enhances the model's understanding of global money laundering typologies without requiring direct data sharing.
This collaborative approach improves 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 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 AI 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,
Financial Institutions have a view of the behaviors of high-risk behaviors. By combining these pictures from multiple organizations, Consilient can clearly understand the transacting behaviors of these types of accounts. Typology risk scores enable sector-specific behavioral profiling.
Key benefits
Proactive risk detection
Uncover hidden risks before they escalate into significant threats, protecting the FI from potential financial loss, regulatory penalties, and reputational damage.
Enhanced risk insight
Stay ahead of regulatory requirements by leveraging a solution that evolves with Regulatory guidelines, ensuring ongoing alignment with global AML/CTF standards.
Optimize operational efficiency
Automating the detection of high-risk behaviors and generating actionable insights can reduce the burden on compliance teams, allowing them to focus on investigation and remediation.
Compliance leadership
Position your FI as a leader in AML innovation, offering enhanced protection and trust to your customers and stakeholders by adopting the latest in federated learning technology.
Proactive risk detection
Uncover hidden risks before they escalate into significant threats, protecting the FI from potential financial loss, regulatory penalties, and reputational damage.
Enhanced risk insight
Stay ahead of regulatory requirements by leveraging a solution that evolves with Regulatory guidelines, ensuring ongoing alignment with global AML/CTF standards.
Optimize operational efficiency
Automating the detection of high-risk behaviors and generating actionable insights can reduce the burden on compliance teams, allowing them to focus on investigation and remediation.
Compliance leadership
Position your FI as a leader in AML innovation, offering enhanced protection and trust to your customers and stakeholders by adopting the latest in federated learning technology.
Uncover high-risk sectors, ensure compliance, preserve trust
Our federated learning models for AML represent the future of risk management in the financial sector.
By identifying behaviors associated with Regulatory high-risk sectors and uncovering undeclared customer activities, we provide FIs with the tools needed to stay compliant, protect their operations, and maintain trust in the global financial system.