The generative AI wave has reached financial services. Boards are asking about it. Customers are curious. Vendors are everywhere. And internal teams, from product to risk, are under pressure to make something happen. But before getting swept into the next proof of concept, it’s worth asking a basic question:
Does everyone involved actually understand how this stuff works?
Because what’s blocking progress right now isn’t ambition, it’s alignment. Without a shared understanding of the foundational concepts behind generative AI in finance, teams end up misjudging what’s possible, overspending on misfit solutions, and burning time on avoidable false starts.
So instead of diving headfirst into tools and demos, let’s slow it down and unpack the 12 terms that every stakeholder should know. This isn’t about buzzwords. It’s about building the fluency you need to assess vendors, mitigate risks, and scale capability the right way.
What makes generative AI models work?
Let’s start with the machinery under the hood.
Large language models (LLMs) are what most people mean when they talk about generative AI. These models are trained on massive datasets, everything from books to web content, to predict the next word in a sequence. That might sound simple, but in practice it enables complex tasks: drafting documents, summarising emails, answering queries, and even writing code.
The real breakthrough came with transformers, a model architecture introduced by Google in 2017. Transformers allow models to understand relationships between words more effectively by attending to context. This is why LLMs like GPT-4 can stay coherent across long passages and perform better on logic-heavy tasks.
But there’s a limit to that coherence. That’s where tokenisation and context windows come in. Before models can work with language, they break it into “tokens”, units that might be as small as a character or as large as a word. Every model has a context window, which defines how many tokens it can “see” at once. Exceed that limit, and the model starts losing track.
In the context of generative AI in finance, knowing how many tokens a model can handle directly affects whether it can process long documents, detailed customer records, or large-scale inputs without dropping crucial information.
How can you control what generative AI delivers?
Even with a powerful model, the output depends heavily on how you use it.
Prompt engineering has emerged as a make-or-break skill. The way you phrase your request can significantly impact quality, tone, and relevance. A vague prompt yields vague answers. A well-structured one, possibly including examples or constraints, can make the difference between “interesting” and “useful”.
Then there’s temperature, a configuration setting that controls how deterministic the model is. A temperature of 0 makes it repeatable and consistent, good for compliance-heavy contexts. A temperature closer to 1 makes the model more creative, which may suit marketing or idea generation, but can also increase the risk of hallucination.
If you need more than prompt tweaks, there’s always fine-tuning, where you train a base model further on your own data. This can align outputs with your domain or brand, but it’s not a casual decision. Fine-tuned models are harder to audit and more expensive to maintain and can introduce new risks if the training data isn’t clean or representative.
In regulated environments like finance, the key question isn’t just “can we fine-tune this?”; it’s “Can we control and audit this reliably if we do?” This is especially important as generative AI in finance moves from isolated innovation to business-critical use.
How do you make generative AI useful for enterprise tasks?
Understanding the model is one thing. Making it useful across a business is another.
Enter embeddings. These are vector representations of text, mathematical fingerprints that make it possible to compare meaning rather than just keywords. Embeddings are the backbone of features like semantic search and recommendation engines. If you’re trying to match customer queries to policy language or detect anomalies in large datasets, embeddings are essential.
Then there’s Retrieval-Augmented Generation (RAG), which gets around a fundamental limitation: models can’t access real-time information unless you build that in. With RAG, you combine the model with a search mechanism that pulls in current, contextual data from your own sources, like databases, intranets, or document stores. The model then uses that information to generate a more grounded response.
For tasks like internal knowledge management, regulatory query handling, or even onboarding journeys, RAG makes generative AI in finance feel enterprise-grade.
One more trick: chain-of-thought prompting. Instead of asking the model for an answer outright, you prompt it to walk through its reasoning step-by-step. This tends to produce more accurate and interpretable outputs, particularly for multi-step logic problems or numerical tasks. In finance, where explanations matter as much as answers, this technique is especially useful.
What are the real risks of using generative AI in finance?
Let’s not sugar-coat it. GenAI can and will go wrong, especially if you assume it’s smarter than it is.
Take zero-shot learning. It’s impressive that models can generate outputs without seeing many examples, but this ability comes with blind spots. They generalise based on patterns from their training data, which may not match your use case, customer base, or regulatory environment.
That brings us to hallucinations, when models make things up. Confidently. Persuasively. Sometimes with fake references or legal-sounding language. This isn’t a rare glitch; it’s an architectural trait. Without built-in fact-checking, every GenAI interaction carries a risk of plausible nonsense.
Then there’s the compliance piece. Governance, data handling, and auditability aren’t afterthoughts; they’re prerequisites. You’ll need to monitor prompts and responses, control what data flows through the model, and ensure outputs meet regulatory expectations.
The stakes are higher when using generative AI in finance because one unchecked output could mean reputational damage, compliance breaches, or customer harm. If you can’t explain or control how your AI system made a decision, you’re already exposed.
Understand first, scale with infrastructure after
Chances are your organisation already has generative AI and financial technology on the roadmap—and maybe even a budget allocated. That’s a strong start.
But turning strategy into reality takes more than fluency in the terminology. It takes delivery infrastructure that enables multiple teams—product, tech, risk, and procurement—to explore vendors, test solutions, and deploy capabilities safely and efficiently.
That’s where NayaOne comes in. We’re not another dashboard or sandbox. We’re the infrastructure layer that enables financial institutions to discover, test, and scale external digital, AI, and financial technology capabilities—without vendor sprawl, PoC fatigue, or compliance drag.
Understanding generative AI in finance is step one. Executing at scale, with controls, confidence, and alignment, is what sets successful institutions apart.
