ASX Banks & Tech Rally: What AI Signals for 2026

The ASX 200 closed Friday up 0.4% to 8,621.4, with banks and tech doing most of the lifting. That’s not just a feel-good end to the week—it's a useful clue about what investors are paying for heading into 2026: earnings resilience, better risk controls, and scalable automation.

Zoom out and the pattern gets clearer. US stocks jumped after an inflation update that revived hopes for rate cuts. Lower (or even just less scary) rates tend to push investors toward growth again, and growth today often means software and data. For Australian financial services leaders and fintech operators, this is where AI in finance stops being a slide deck and starts being a balance-sheet story.

Here’s the stance I’ll take: the “banks + tech up” tape is increasingly an AI story, even when the headlines talk about inflation. AI is becoming the operating system for fraud prevention, credit decisioning, customer service, compliance, and market-making. If you’re running a financial product, building a fintech, or allocating capital, you should read the ASX move as a signal about who’s compounding capability—and who’s accumulating risk.

Why banks and tech climbed on the ASX (and why it matters)

Answer first: Banks rose because investors see a path to steadier margins and fewer nasty surprises if rates peak, while tech rose because markets reward scalable earnings—especially when AI is embedded in the product.

On Friday, big banks finished higher (with one flat), and local tech names posted strong gains. Meanwhile, miners and energy dragged. That sector split matters for anyone following AI-driven investment strategies because it reflects a familiar risk-on rotation: cashflow confidence + growth optionality.

Inflation expectations are the spark, not the fuel

The immediate trigger was improved sentiment after inflation data supported the idea of future rate cuts. Markets love lower rates because they:

• Reduce discount rates used in valuations (helping growth stocks)
• Ease pressure on borrowers (supporting banks’ credit quality)
• Encourage risk appetite (supporting equities broadly)

But the fuel behind sustained outperformance isn’t the inflation print. It’s whether companies can protect margins and grow earnings per customer. That’s where AI adoption shows up: fewer manual processes, faster decisions, and better risk selection.

“Banks + tech” is a single theme now: data advantage

Banks used to be valued like utilities with a lending book. Now, the best-run banks look more like data businesses with a banking licence. Tech firms serving finance (and finance teams inside banks) are valued for how reliably they can turn product usage into durable cashflows.

AI accelerates both:

• Banks use AI to reduce losses, reduce cost-to-serve, and improve retention
• Fintech and SaaS providers use AI to increase user productivity and expand pricing power

The quiet AI drivers behind bank share strength

Answer first: Bank stocks benefit from AI when it measurably reduces losses, improves operational efficiency, and strengthens compliance—three areas investors care about even more when scrutiny is high.

The same week bank shares rose, courts increased penalties in a high-profile misconduct matter. That contrast is instructive: markets can reward banks’ earnings power and punish weak controls. AI helps only if governance is real.

1) Fraud detection and scam prevention (where AI pays back fastest)

Fraud and scams are one of the clearest ROI lines in AI in financial services. The win condition isn’t “we use machine learning.” It’s:

• Fewer false positives (less customer friction)
• Faster interdiction (less loss)
• Better scam typology detection (less repeat exposure)

Modern scam rings move faster than rule-based systems. Models that score behaviour patterns—device signals, transaction graphs, mule account networks—can improve detection. The banks that operationalise this well tend to see:

• Lower remediation cost
• Lower charge-offs
• Better customer trust scores

If you want one sentence to remember: AI doesn’t just stop fraud; it prevents churn caused by fraud.

2) Credit decisioning: better risk selection, not just faster approvals

Credit scoring is often pitched as speed. Speed is nice, but investors pay for loss performance across the cycle.

AI-driven credit decisioning typically improves outcomes when it:

• Incorporates more granular cashflow signals (especially for SMEs)
• Detects early distress (missed payrolls, cash buffers shrinking)
• Adjusts limits dynamically (reducing exposure before default)

Done poorly, it creates model risk and fairness issues. Done well, it reduces tail losses and stabilises portfolio performance—exactly what the market wants when macro uncertainty is still around.

3) Compliance and conduct risk: AI won’t save you if controls are weak

A lot of executives hope AI will reduce compliance cost. It can—but only after you fix fundamentals.

The practical sequence I’ve seen work:

1. Standardise data capture and case management
2. Train models to triage alerts (not to “decide guilt”)
3. Add human-in-the-loop review and audit trails
4. Monitor drift, bias, and outcome quality monthly

If you skip steps 1–2, you get “AI theatre” and more exceptions. If you skip step 4, you get a system that quietly degrades.

Strong governance is a profit driver. Weak governance is a valuation haircut.

Why ASX tech strength is a fintech signal (not just a growth trade)

Answer first: Tech stocks rise sustainably when their products become essential workflows. AI features are becoming the new “must-have” layer in finance software.

Local tech names moved sharply higher on specific company news, but the bigger point is that enterprise buyers—banks included—are shifting budgets toward tools that help teams do more with fewer people.

AI in fintech: where customers will actually pay

In late 2025 and into 2026, I’d bet on these AI-in-fintech use cases as the ones that earn pricing power:

• Financial operations automation: reconciliations, exception handling, close processes
• Risk and compliance workflow copilots: policy search, case summaries, evidence packaging
• Customer support automation with guardrails: faster resolution without hallucinated answers
• Treasury and cash forecasting: scenario generation tied to real cash movements

The common thread is simple: AI that reduces hours, reduces errors, or reduces losses gets funded. AI that produces clever text but doesn’t change outcomes gets cut.

Algorithmic trading isn’t the whole story anymore

Markets often associate AI in finance with algorithmic trading. That’s dated.

Trading and market-making certainly use models—pricing, hedging, execution optimisation—but the larger opportunity in financial services is the boring stuff:

• Underwriting
• Collections
• KYC and AML operations
• Disputes and chargebacks
• Contact centre deflection

“Boring AI” tends to be the most profitable AI.

What the Santa rally narrative gets right (and what it misses)

Answer first: Seasonal rallies happen, but the more durable signal is what investors are rotating into: scalable, data-driven businesses and banks with credible control environments.

Late December often comes with lower volumes, fewer capital raisings, and a mood shift. That can help equities drift up. But if you’re building an AI in finance roadmap—or allocating budget—you don’t want your strategy anchored to seasonality.

Here’s what matters more than the calendar:

The 2026 scorecard: metrics that will decide winners

If you’re inside a bank, fintech, or wealth manager, these are the metrics AI initiatives should move:

• Cost-to-income ratio: AI should reduce unit costs in operations and service
• Net losses / charge-offs: improved risk selection and early intervention
• Fraud loss rate: especially authorised push payment scam rates
• Time-to-decision: credit, disputes, onboarding, and exceptions
• Regulatory findings and remediation cost: fewer repeat issues, faster closure

If your AI program can’t point to at least two of these with hard numbers, investors will eventually treat it as overhead.

Practical next steps: an AI playbook for banks and fintech leaders

Answer first: Start with one high-volume workflow, instrument it end-to-end, and prove measurable impact in 8–12 weeks before expanding.

If you want to convert “AI interest” into “AI advantage,” here’s a simple approach that works in financial services.

Step 1: Pick the workflow where pain is undeniable

Good candidates are processes with:

• High manual volume
• Clear error cost
• Existing data trails
• Measurable cycle times

Examples: scam claim intake, credit policy exceptions, AML alert triage, disputes, arrears management.

Step 2: Design for controls from day one

Financial services AI needs guardrails. Build:

• Human approval points
• Audit logs (who saw what, who changed what)
• Model monitoring (drift, false positives/negatives)
• Data minimisation (only what you need)

Step 3: Measure what the market cares about

Don’t measure “model accuracy” in isolation. Measure:

• Dollars saved
• Loss prevented
• Minutes removed from cycle time
• Customer complaints reduced

Step 4: Scale with a product mindset

Once you’ve proven impact, treat it like a product:

• Versioning and change control
• Training and adoption
• Clear ownership (business + risk + tech)

This is how AI becomes part of operations—not a one-off experiment.

Where this leaves the ASX—and your 2026 priorities

Banks and tech lifting the ASX is a reminder that markets reward two things at once: confidence in earnings and confidence in execution. AI supports both, but only when it’s applied to specific workflows and governed like a risk system, not a marketing feature.

For readers following this AI in Finance and FinTech series, the signal is straightforward: the next year will favour organisations that treat AI as operational infrastructure—fraud, credit, compliance, service—not as a trend to sprinkle on top.

If you’re planning your 2026 roadmap, ask your team a blunt question: Which two financial metrics will improve because of AI by the end of Q1—and how will you prove it?