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How federated learning and machine learning are reshaping customer risk ratings
Customer Risk Ratings (CRRs) have long been integral to anti-money laundering and counter-terrorist financing (AML/CFT) frameworks.
How financial institutions can tackle top risks in 2025 with AI...
As 2025 unfolds, financial institutions face an evolving array of challenges. The recent Executive Perspectives on Top Risks Study conducted by Protiviti and NC State University, which garnered insights from over 1,200 board members and executives, sheds light on the primary concerns encompassing financial crime compliance, AI integration, and increasing regulatory oversight.
How secure collaboration is transforming financial crime detection
Money laundering activities in the United States alone are estimated to total $300bn annually. In 2023, penalties for anti-money laundering (AML) violations in the...
What is the role of federated learning in RegTech?
Federated learning is a machine learning technique that enables multiple entities to train a model together while keeping their data decentralized. In an era where AI models are taking shape, such a technology will be able to play a key role in the expansion of RegTech.
Revolutionizing risk management: The transformative impact of federated learning
The global financial landscape is increasingly plagued by the complexities of financial crime compliance. As the severity of global money laundering grows, major financial institutions are entangled in a dense web of risks and regulations.
Consilient launches its Federated Learning RegTech solution to market
Consilient, which is on a mission to transform how businesses address financial crime, has launched its financial crime detection and prevention solution to market.