It’s not enough to build models—they must work in the real world.
The banking sector has reached a clear inflection point. Most institutions now have AI pilots or proofs of concept underway. But very few have turned those into operational success.
The reason: They haven’t closed the gap between innovation and execution.
And when it comes to AI, only execution counts.
Where most banks fall short
Despite years of investment, most banks still struggle with three fundamental barriers:
1. Model operations (ModelOps)
Many institutions can build models. But few can:
- Deploy them consistently
- Monitor performance drift
- Recalibrate based on real-world feedback
- Provide explainability for key decisions
2. Data lineage
Without clear, documented lineage, AI models become black boxes. Regulators won’t tolerate that, and nor will internal audit teams.
If you can’t show where your data came from, how it changed, and why it’s trustworthy, your models are a risk—not an asset.
3. Business integration
Too many AI initiatives sit in labs and not workflows.
So, you end up with any number of smart models that never influence real decisions or whose outputs go unread and unused.
Operational AI means models are embedded directly into frontline tools—delivering decisions, not just insights.
Standards like BCBS 239 are back and nonnegotiable
Originally issued in 2013, BCBS 239 set the standard for risk data aggregation and reporting. For years, it was considered a back-office compliance requirement.
But not anymore.
With the AI Act and broader regulatory scrutiny rising, BCBS 239 has reemerged as the foundation for operational AI:
- Strong governance
- Traceability
- Reconciliation of risk and finance data
- Right-first-time reporting
Where AI delivers real operational value
AI isn’t just about personalisation or prediction. When deployed properly, it creates measurable value in core operational areas, including:
- Intelligent compliance: Real-time monitoring of transactional data reveals anomalies, flags risks, and generates explainable alerts—all traceable back to source data
- Fraud detection: Dynamic behavioural models evolve with changing tactics, integrated into transaction systems for real-time prevention and not just detection
- Environmental, social, and governance (ESG) scoring: Automated analysis of emissions, supply-chain risk, and disclosures are critical for reporting and investment strategy but only defensible if fully traceable
These are not “nice to have” use cases. They’re becoming central to how banks compete, comply, and survive.
Follow the thread, or risk losing the plot
There’s no shortage of innovation in banking AI. But operationalisation is what separates winners from the rest. Operational AI is the only AI that matters.
That means:
- Models deployed at scale
- Built on trusted, traceable data
- Monitored, explained, and governed
- Driving real decisions, not just dashboards
If you’re ready to know more about how to operationalise AI in banking, read this article about implementing a signal-oriented approach.