Tag: model governance

Banks have long treated fraud and money laundering as separate disciplines, even though the same customer, account or transaction can carry signals relevant to...

Key views on factors FIs prioritise when selecting a third-party RegTech solution: The Global State of RegTech surveyed 300 FI decision-makers and 100 vendors...

Key views of Primary concerns of AI adoption in risk and compliance across FIs and vendors: Global State of RegTech report surveyed 300 FI...

AI in financial services is no longer a “nice to have”. Boards are asking where it can reduce cost and improve decision-making, executives are...

Imagine a financial institution preparing to deploy a new AI tool designed to streamline anti-money laundering (AML) alerts. The promises are compelling: faster triage,...

False negatives represent one of the most serious, yet least visible, risks in AI-powered compliance systems. While false positives often dominate the conversation, a...

AI is transforming AML, but one critical challenge persists: transparency. In regulated industries, credibility rests on explainability. If executives can’t understand a model, they...

Artificial intelligence is no longer a future prospect for financial services; it is a present reality, evolving quickly and reshaping the compliance landscape. From enhancing risk detection to improving operational efficiency, AI offers immense potential. However, its rapid adoption also brings fresh regulatory questions, new risks, and ethical challenges.

As artificial intelligence (AI) continues to advance, its role in financial crime prevention is growing, with organisations now considering AI as a foundational element in their risk management strategies.

Bias in AI is currently a hot topic in the media and among regulators and industry experts.