Tag: model governance

As AI agents become more common in commercial lending, banks face a critical question: which decisions can be delegated to technology, and where must...

Prometeia, the Italian risk management and advisory specialist, has completed the implementation of its Prometeia Model Journey (PMJ) platform at the African Development Bank...

Insurers have access to increasingly sophisticated pricing technology, but a growing execution gap could be limiting the value they get from it. Earnix argues...

Financial institutions have long treated fraud and anti-money laundering (AML) as separate disciplines, run on separate timelines by separate teams. Fraud detection demands split-second...

The most predictive model an insurer builds is often the one it can never put into production, according to InsurTech pricing specialist Earnix. Machine learning...

Boards across financial services are no longer debating whether to use AI, but how quickly they can put it into production. Executives see opportunities...

Imagine your organisation has identified a promising AI system to manage anti-money laundering (AML) alerts. It promises quicker triage, fewer false positives, and even...

False negatives are emerging as one of the most dangerous blind spots in AI-driven compliance systems. While much of the attention has historically been...

In the world of anti-money laundering (AML), credibility depends on more than mathematical performance. If models cannot be explained clearly, executives won’t trust them...

Artificial intelligence has firmly embedded itself in financial services, transforming compliance functions in ways unimaginable a decade ago. It promises smarter risk detection, operational efficiency, and faster insights. But with these opportunities come new regulatory challenges, ethical concerns, and the growing threat of AI-powered crime.