OpenAI vs Copilot: What Banks Can Learn From La Trobe

La Trobe University is planning to roll out 40,000 licences of ChatGPT Edu to students and staff by the end of FY27—and it’s a telling sign of how AI buying decisions are being made when the stakes are real. Microsoft Copilot had the early advantage in the organisation, yet OpenAI became the dominant tool in practice.

If you work in banking or fintech, this matters more than it sounds. Education is a high-volume, high-variation environment—thousands of users, wildly different tasks, and a strong need for governance. That’s a pretty good proxy for what happens when generative AI moves from a controlled pilot into frontline financial workflows.

This post uses La Trobe’s OpenAI-vs-Copilot shift as a practical lens for the AI in Finance and FinTech series: how institutions should evaluate competing AI platforms for fraud operations, credit decisioning, customer service, analyst productivity, and model risk management.

La Trobe’s decision isn’t about “which AI is smartest”

The most useful way to read La Trobe’s move is simple: adoption wins strategy battles. A tool can be “official,” centrally sponsored, and integrated into a vendor suite, yet still lose mindshare to the product users actually prefer.

At La Trobe:

• ChatGPT Edu is set for broad student and staff deployment (40,000 licences by FY27).
• A first tranche of 5,000 licences is targeted by the end of the current financial year.
• Microsoft Copilot remains in play for staff, but it’s no longer the centre of gravity for large-scale student usage.

They didn’t publicly explain the “why,” which is common in enterprise AI decisions. Often the reasons are a mix of usability, output quality, speed, workflow fit, and internal champions—not a single feature checklist.

For financial institutions, the takeaway is direct:

The winning AI platform is the one that gets embedded into daily work without constant enforcement.

That’s less about hype and more about “time-to-value” in real teams.

AI platform competition is already shaping finance

Banks and fintechs are watching the same competitive dynamic play out across generative AI platforms:

• OpenAI-style chat experiences that feel fast and flexible
• Microsoft-style copilots that sit inside productivity suites
• Specialist models and internal assistants that focus on narrow risk, compliance, or data domains

In finance, the competition isn’t only about model quality. It’s about how safely you can put AI next to money movement, customer data, and regulated decisions.

Three areas show this clearly.

Fraud and financial crime: speed is useless without control

Fraud teams don’t need a chatbot that’s “creative.” They need consistent triage, accurate narrative summaries, and fewer false positives.

Where generative AI helps in fraud operations:

• Summarising alert history into a clean case narrative
• Drafting SAR/SMR-style write-ups (with strict human review)
• Creating investigator copilots that answer: “What’s the next best step?”
• Turning messy notes into structured fields for downstream analytics

Where platform choice shows up:

• Data boundaries: Can you guarantee sensitive data doesn’t leak into prompts, logs, or training?
• Auditability: Can you reconstruct why an output was produced for an internal audit?
• Workflow fit: Can investigators use it inside their case management tools, not a separate tab?

In my experience, most fraud AI pilots stall not because the model is weak, but because the governance layer is vague: unclear prompt logging, unclear retention, unclear approval for use with customer identifiers.

Credit scoring: LLMs don’t replace the score, they support the decision

For credit, a common misconception is that generative AI will “decide” approvals. It shouldn’t.

A better pattern is:

• Keep the credit scorecard / ML model as the decision engine.
• Use generative AI for explanations, evidence gathering, and consistency.

Practical examples:

• Summarise application documents into a structured risk memo
• Standardise analyst notes so exceptions are comparable
• Draft customer communications that align with policy language
• Create internal Q&A assistants for credit policy interpretation

Platform evaluation questions that actually matter:

• Can the model be constrained to only cite approved policy and approved data?
• Can it provide traceable references to the documents used?
• Can you enforce tone, disclaimers, and approved language automatically?

If you can’t answer those, your “credit copilot” becomes a liability.

Algorithmic trading and research: productivity is the easy part

For market research teams and quants, generative AI can speed up:

• Code scaffolding for backtests
• Summaries of earnings call transcripts
• Drafts of investment memos
• Rapid scenario framing

But the hard part is still the same: verifying inputs and preventing contaminated outputs.

The risk is subtle. Analysts may unknowingly accept a plausible-sounding summary that contains a wrong number or a misattributed claim, then that error leaks into a report, then into a decision.

This is where “Copilot vs ChatGPT” becomes less relevant than:

• Do you have retrieval with approved sources?
• Do you have confidence signalling (what the model knows vs guesses)?
• Do you have review workflows that fit how research is produced?

A tool that’s slightly less impressive in free-form conversation can still be the right choice if it’s easier to control and verify.

The real evaluation framework: fit, governance, and adoption

Most companies get AI tool selection wrong by treating it like traditional software procurement. With generative AI, the failure modes are different.

Here’s a framework I’d actually use for banks and fintechs deciding between platforms (including OpenAI-style tools and Microsoft Copilot-style tools).

1) Measure adoption the way finance teams work

Don’t measure “monthly active users” and call it success. Measure workflow penetration.

Useful adoption metrics:

• % of fraud cases where AI created the first draft narrative
• % of call centre chats where AI produced an approved response suggestion
• Time saved per KYC refresh (minutes, not vibes)
• Reduction in rework: fewer escalations, fewer incomplete submissions

If you can’t tie usage to a workflow KPI, you’re just funding curiosity.

2) Treat governance as a product requirement, not a policy document

Financial services need more than “don’t paste sensitive info.” You need enforceable controls.

Non-negotiables:

• Prompt and output logging suitable for audit (with appropriate access controls)
• Clear retention and deletion rules
• Role-based access and environment separation (dev/test/prod)
• Guardrails for restricted topics and disallowed actions
• Human-in-the-loop checkpoints for regulated outputs

A bank can’t afford a setup where the compliance team is surprised six months later.

3) Plan for multi-model reality

La Trobe is keeping Copilot for staff while deploying ChatGPT Edu at scale. That’s a blueprint many banks will follow: one size won’t fit all.

A practical approach is:

• One “productivity copilot” for email/docs/meetings
• One “risk copilot” for fraud, AML, complaints, and policy Q&A
• One “engineering copilot” for code and data tasks

The secret is not fewer tools—it’s fewer uncontrolled tools. Centralise identity, logging, and governance so teams can use what fits without creating a shadow AI mess.

What this says about 2026 planning for Australian banks and fintechs

Late December is when a lot of teams are quietly locking their 2026 roadmaps. The timing of La Trobe’s announcement (mid-December) is a reminder that AI programs are moving from experimentation to commitments measured in tens of thousands of licences.

In Australia, we’re also seeing AI adoption pushed by:

• Workforce skills initiatives (training at scale)
• Expanding local compute investment and data centre capacity
• Board-level pressure to show productivity gains and risk controls

For banks and fintechs, 2026 planning should assume:

• Your staff will compare tools and prefer the one that feels easiest.
• Your regulators (and internal audit) will ask how outputs are governed.
• Your competitors will ship faster if your AI is trapped in pilots.

The best teams stop arguing about brands and start engineering repeatable, governed deployment patterns.

A practical “next 30 days” checklist for AI leaders in finance

If you’re leading AI adoption in a bank, lender, payments firm, or fintech, here’s what I’d do in the next month—before more licences get bought and more habits form.

1. Pick one workflow (fraud case summaries, KYC refresh, complaints response drafting) and instrument it end-to-end.
2. Define a red list: what data and what actions are prohibited, and how you’ll enforce it technically.
3. Set up a model evaluation harness using your own examples (50–200 real, anonymised cases beats generic benchmarks).
4. Decide where human review is mandatory and bake it into the UI.
5. Create an internal “AI style guide”: approved language, disclaimers, escalation triggers.
6. Publish a short scoreboard monthly: adoption, time saved, error rates, and incidents.

That’s how you avoid the classic outcome: widespread use, unclear controls, and a painful reset.

Where OpenAI vs Copilot really lands for fintech teams

La Trobe’s OpenAI-first shift is a signal that user pull can outrun vendor push, even in organisations that already run Microsoft tooling. In finance, you’ll see the same pattern unless you design an environment where the preferred tool is also the safest tool.

If you’re serious about generative AI in finance—fraud detection workflows, credit scoring support, analyst research, or customer experience—don’t start by asking which platform is “better.” Start by asking which platform you can govern, measure, and scale.

If you want a second set of eyes on your evaluation approach, build a shortlist and pressure-test it against three things: workflow fit, auditability, and adoption. Then decide.

Where do you think your organisation is most likely to see “shadow AI” first: fraud ops, credit, customer support, or product engineering?