Despite best intentions, compliance with AI regulations poses real world challenges.

Black box models such as deep learning systems can be difficult to interpret, making it hard to comply with explainability and audit standards.

Model Risk management frameworks need to evolve beyond traditional financial models to address AI specific concerns.

Data drift, feedback loops and algorithmic retraining are all new concerns, which did not need to be considered previously.

Cross-border complexity is another major challenge.

A model that complies with Singapore's fit principles may not meet the eus AI's Act, stricter documentation and human oversight requirements.

Finally, financial firms also face resource constraints.

While fintechs often innovate faster, larger institutions struggle to retrofit legacy systems to meet evolving standards.