In today’s boardrooms, Generative AI (Gen AI) is the star of the show. Executives are captivated by tools like ChatGPT, dreaming of automated advisors, regulatory co-pilots, and hyper-personalized customer experiences.
And while the possibilities are real, there’s a growing risk in the rush: skipping over explainable machine learning in the race to deploy GenAI.
In regulated industries like financial services, that’s more than a technology misstep. It’s a business liability.
Generative AI is not Decision-Making AI
Let’s get one thing clear: LLMs like ChatGPT are not designed for structured decision-making. They excel at natural language tasks like summarization, content generation, and question answering.
But when it comes to decisions that impact real people—things like approving a loan, flagging a fraudulent transaction, or triggering an anti-money laundering alert—you need systems that are explainable, auditable, and built for accountability.
Imagine telling a regulator, “Our LLM decided this account should be closed.” That’s not going to cut it.
Why Responsible AI Starts with Explainable ML
Even with GenAI dominating the conversation, explainable machine learning (ML) offers unmatched value for financial institutions:
1. Explainability Builds Trust Across the Business
With interpretable models—like decision trees, logistic regression, or models enhanced with tools like SHAP—you can:
- Justify decisions to regulators and internal audit.
- Understand which features drive outcomes.
Detect and correct bias.
Explain model behavior to non-technical stakeholders.
Without this transparency, every model becomes a trust risk.
2. It’s Required—Both By Law and Common Sense
Explainability isn’t optional in many regions:
- GDPR guarantees individuals the right to explanation for automated decisions.
- U.S. regulators like the OCC and CFPB scrutinize bias and model transparency.
- Basel guidelines push for model accountability and auditability.
If your model can’t explain itself, it’s likely non-compliant.
3. Explainable ML Teaches Responsible AI Behavior
Understanding the fundamentals of how models work—how they learn, what features matter, how bias sneaks in—builds the cultural and technical muscle your teams need to responsibly adopt more complex systems, including GenAI.
Explainability is not a step you skip. It’s a foundation you build on.
4. LLMs Are Even Less Explainable
Large language models are black boxes trained on internet-scale data. They may provide compelling answers, but:
- Their outputs can’t be traced back to clear logic.
- They hallucinate, confidently inventing incorrect or misleading information.
There’s no built-in audit trail.
Deploying GenAI without understanding ML is like flying a plane before learning to drive.
5. You Can Use GenAI with Explainable ML
This isn’t either/or. GenAI can actually support explainable ML:
- Use ML to score fraud risk.
- Use ChatGPT to translate that score into natural language for analysts or customers.
- Use LLMs to summarize model performance for non-technical executives.
Done right, LLMs act as a layer of clarity, not confusion.
Skipping ML for GenAI Is a Strategic Mistake
At many companies, there’s a temptation to leapfrog directly into GenAI. But that often leads to:
- Unexplainable outputs in high-stakes use cases.
- Misaligned investments without business ROI.
- Reputational risk when things go wrong.
Building a strong foundation in explainable machine learning helps you scale GenAI responsibly with clear governance, proper safeguards, and business-aligned outcomes.
Building the Foundation of Explainable AI
Generative AI is transformative, but explainable ML is proven, mature, and essential—especially in finance. Use GenAI where it fits.
But don’t ignore the models that already power your core decisions and that do so in a way that regulators, customers, and business partners can trust. Because that trust isn’t optional.
Ready to build the explainable foundations of your next AI use case? Let’s talk today.
