AI has moved beyond experiments in many Australian firms, and by 2026 it's showing up across banking, underwriting and trading desks in practical ways. This piece maps where things are now, what’s changed recently, how the big players are using AI, and what professionals should do next.
Quick reference
Snapshot, fast:
- Major deal: Infosys agreed to buy 75% of Versent for A$233 million (about US$152.4m), keeping Versent local but adding AI capabilities to large enterprise projects.
- Regulation to watch: Consumer Data Right (Open Banking) and stronger expectations from APRA/ASIC on governance for algorithmic decision‑making.
- Skills employers want: ESG, data storytelling, technology literacy, critical thinking, AI and ethics — highlighted by CPA Australia for the mid‑2020s.
- AI vendor mix: hyperscalers (Microsoft, Google, Amazon) plus local systems integrators and cloud specialists such as Versent.
Current state — where Australian finance stands in 2026
At the moment, many banks and insurers have pushed several pilots into production and are running AI in live systems. Fraud detection, anti‑money‑laundering (AML), claims automation and customer chatbots are now routine. Institutional trading desks use machine learning models for signal generation and risk monitoring. Fintechs supply nimble point solutions. Big banks and insurers still run most critical systems in‑house or with long‑term partners, but they increasingly bolt on cloud AI services for speed.
Two trends are shaping the market. First, data portability under the Consumer Data Right has opened up new data for some lenders and advisers. Second, pressure for trustworthy AI has forced governance conversations up to the board level. Regulators and industry bodies are asking for controls, model testing and human oversight.
Key developments that mattered in 2024–26
Several developments set the pace for 2026.
- Strategic acquisitions. In 2026 Infosys agreed to buy a 75% stake in Melbourne‑based Versent for A$233m, keeping Versent's brand and 650 staff but adding AI tools and global scale to local cloud services.
- Vendor maturity: CRN’s 2026 AI 100 names dozens of specialist vendors across cloud, security, data and infrastructure, so banks can increasingly choose proven stacks rather than build every component themselves.
- Workforce re‑skilling. CPA Australia flagged six skills employers want by 2025: ESG, data storytelling, technology literacy, critical thinking, AI and ethics. Firms investing in these skills get fewer false starts on AI projects.
Top picks and analysis: the technology and partners to watch
Which platforms matter? Three categories matter most — cloud providers, analytics platforms and specialised vendors.
- Hyperscalers: Microsoft Azure (including Azure OpenAI Service), Google Cloud (Vertex AI) and AWS all provide managed model hosting, data warehouses and applied AI services. Australian banks favour multi‑cloud strategies to avoid vendor lock‑in.
- Local integrators: Versent (now with Infosys backing), Accenture, Deloitte and local cloud specialists remain key because they understand APRA/ASIC obligations and legacy banking systems.
- Point vendors: companies offering fraud detection, claims automation, robo‑advice and voice analytics — many are subscription products and integrate via APIs.
Price signals: large organisations still budget seriously for model hosting and data work; for big banks those costs can run into the mid‑to‑high thousands each month, depending on scale and SLAs. Smaller banks and insurers often adopt SaaS point solutions at licence fees that scale with customers and volumes.
Comparison table — where banks, insurers and traders differ
| Area | Primary use | Typical tech | Regulatory focus |
|---|---|---|---|
| Retail banking | Customer service, credit scoring, fraud detection | Chatbots, ML scoring, real‑time analytics | Consent, fairness, explainability |
| Insurance | Claims triage, pricing, risk modelling | Computer vision, NLP, actuarial ML | Model validation, data lineage, consumer outcomes |
| Trading / markets | Signal generation, execution, risk monitoring | Time‑series ML, low‑latency infra, model ops | Market conduct, latency/explainability |
Industry impacts — banking, insurance, trading
Banking: AI has cut manual processing and sped decisions. Loan assessments that once took days are now quicker, thanks to automated document extraction and risk models. Fraud teams use behavioural models that detect anomalies in seconds. But banks still wrestle with bias in scoring models and with integrating new data sources into legacy systems.
Insurance: automation and image recognition are speeding up claims for many insurers, letting them settle smaller motor claims much faster. Insurers are also testing broader datasets like telematics and weather feeds to sharpen pricing.
The flip side: customers want transparency on how prices are set, and regulators are watching for unfair discrimination.
Trading: quant teams are experimenting with bigger alternative datasets and generative tools to model scenarios. A few desks use agent‑style automation for workflows, but most remain cautious about running these systems in live trading. Speed and explainability matter in markets. Risk teams now run continuous stress tests that include model performance risk.
Expert views — what practitioners and regulators say
Industry leaders sound practical. Many CIOs say AI is a tool — not a silver bullet. There’s enthusiasm for automation where it reduces repetitive work, and scepticism where models make opaque decisions about people’s money. Regulators press the same point. APRA and ASIC expect governance, testing and human oversight. And professional bodies like CPA Australia are pushing accountants and finance teams to learn tech, data and ethical judgement.
At the vendor level, global firms stress integration. That’s why the Versent‑Infosys deal got attention — it paired local cloud skills with a global AI stack. The message from integrators: build on cloud‑native platforms, but invest in data quality and control frameworks.
Practical tips — what finance teams should do now
Start small, scale safely. That’s the advice across the sector.
- Focus on data hygiene first. Garbage in means garbage out. Establish clear data lineage and access controls before deploying models.
- Create a model governance framework. Classify models by risk, run pre‑deployment testing and schedule ongoing performance checks.
- Invest in skills the market wants. CPA Australia’s six areas — ESG, data storytelling, tech literacy, critical thinking, AI and ethics — are a good checklist for hiring and training.
- Use cloud partners for speed, local integrators for compliance. A hybrid approach reduces time to market without ignoring APRA/ASIC expectations.
- Monitor costs closely. Large language models and continuous real‑time scoring can create unpredictable cloud bills unless capped and monitored.
Privacy and safety — what to watch
Privacy isn't optional. The Office of the Australian Information Commissioner (OAIC) guidance and the Consumer Data Right create duties around consent, purpose and minimisation. Firms must document why they use personal data, how long they keep it and how models affect outcomes. Explainability matters in disputes. So does the ability to revert to a human decision maker.
Security also matters. The CRN AI 100 highlighted cybersecurity vendors as central to AI adoption. Shadow AI — teams using uncontrolled third‑party tools — is a real risk. Good controls, inventory of models and strict access policies reduce that risk.
What’s next — five signs to watch in 2026–27
Watch these signals:
- More local partnerships between global AI firms and Australian systems integrators — like the Infosys‑Versent deal — to combine scale and local know‑how.
- Stronger regulator guidance on model audits and consumer protections tied to AI outcomes.
- Growth in subscription SaaS models for niche finance tasks — pricing and claims — replacing bespoke builds.
- Upskilling programs across finance teams, with firms embedding AI and ethics training into professional development.
- Tighter cost controls as banks and insurers learn to manage cloud spend for live, model‑rich services.
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AI in Australian finance is now about trade‑offs: speed versus explainability, automation versus oversight, innovation versus regulation. The winners will be the organisations that pair smart tech choices with clear governance, better data and staff who know both numbers and machines. Expect the next 18 months to sort experiment from business‑as‑usual — and to put AI where it delivers clear, traceable value.
This article was created with AI assistance.
