In banking today, traceability isn’t a technical detail—it’s your first line of defence and your differentiator. In fact, losing the thread can become an existential threat, endangering your license to operate.
With AI now supporting front-line decision-making, banks are facing a new nonnegotiable: explaining every decision, every input, and every outcome.
Customers expect it.
Regulators demand it.
And soon, competitors will exploit it. If you can’t follow the thread of your data, you can’t prove the integrity of your AI.
Traceability: from "nice to have" to "nonnegotiable"
The last few years saw an explosion of AI experimentation in financial services, but those proofs of concept now face far greater scrutiny.
Regulators want to know:
- Where did this data originate?
- Was it altered or enriched?
- How did the model interpret it?
- Why was this decision made?
If a bank can’t answer those questions precisely and quickly, the risks multiply:
- Regulatory fines
- Inaccurate reporting
- Legal exposure
- Reputational damage
Suddenly, a promising AI use case becomes a compliance crisis.
What happens when banks lose the thread?
The dangers aren’t theoretical. They are already happening:
- AI models making decisions that banks can’t explain to auditors—or worse, to regulators
- Inconsistent, unverified data leading to wrongful loan denials, unfair credit scoring, or undetected fraud
- Regulatory reviews (like those under BCBS 239) exposing critical gaps in governance—and triggering public and private reprimands
- Massive reputational fallout when customers discover opaque or unfair AI-driven outcomes
The industry knows how serious the problem is. In insurance, for example, leading figures have admitted they have "no clue how to implement" the European Union’s new AI rules, a confession that should send a chill down the spine of every risk officer in banking.
Data lineage: the foundation of trust
Many institutions still treat data lineage as a behind-the-scenes IT concern. That mindset is dangerously out of date.
Today, traceability is a strategic imperative—shaping everything from capital reporting to credit scoring, and from climate disclosures to customer communications.
With regulatory frameworks such as the EU AI Act and BCBS 239 now enforcing explainability and control, banks must prove:
- Where every datapoint came from
- How it’s used across systems
- Who touched it and when
This is about more than compliance. It's about confidence.
One AI to rule them all? Not so fast…
As AI scales, many banks are tempted to deploy a single large language model (LLM) to power everything from customer service to financial forecasting.
Sounds great in theory. In practice, cost and complexity can quickly spiral out of control.
- LLMs are compute-heavy, especially when scaled across enterprise workloads
- They often lack the specificity needed for regulated financial decisions
- Explainability becomes harder the further they drift from narrow use cases
There is a smarter approach.
Pick the right tool for the right job
Use task-specific AI models where precision matters most. Always tie those models back to trusted, traceable data.
This not only keeps compute costs down; it keeps your risk exposure in check.
What “following the thread” really means
Following the thread is more than tracking data. It’s a practice that should underpin the entire organisation. It means:
- Connecting data lineage with model governance
- Embedding explainability wherever AI is implemented
- Enabling compliance teams to audit AI in real time
- Giving business leaders confidence in the outcomes they deploy
Most importantly, it builds a foundation banks can trust: to scale safely, innovate responsibly, and lead with confidence.