Government IT Modernisation: Signals for FinTech AI

Australia’s mid-year budget update quietly delivered a big message to anyone building AI in finance: infrastructure is back on the agenda. The Australian Taxation Office (ATO), Australian Federal Police (AFP), Australian Energy Regulator (AER), and the Department of Veterans’ Affairs have secured new funding to modernise core technology platforms—ERP, data systems, digital services, and ICT capability.

If you work in banking, payments, or fintech, it’s tempting to file this under “government catching up.” I think that’s the wrong read. The smarter interpretation is that public sector modernisation is a demand signal: more reliable data pipelines, stronger identity and authentication, more resilient critical services, and a higher bar for cyber security and governance. That raises expectations for the private sector too—especially for AI-driven financial services.

This matters because AI outcomes depend on plumbing. Fraud models, credit decisioning, financial crime monitoring, and personalised financial products don’t fail because a model isn’t clever enough. They fail because data is fragmented, controls are inconsistent, and systems can’t support real-time workflows.

What MYEFO funding tells us: digital foundations beat “AI projects”

The clearest takeaway from the MYEFO announcements is this: modernisation isn’t a side quest—it’s the prerequisite work that makes AI safe and useful.

The update included a substantial AI adoption package for government, but the more revealing story is the “unsexy” spend:

• ATO: $60.9m over two years to develop a second-pass business case for enterprise resource planning (ERP) modernisation.
• AER: $44.1m over five years, plus $7.1m per year ongoing to upgrade core data and digital systems and reduce regulatory burden.
• Veterans’ Affairs: $137.3m over three years for ICT, digital, and data enhancements.
• AFP: five years of funding to strengthen ICT capabilities, with the amount redacted for national security reasons.

Here’s the point fintech teams should notice: these aren’t shiny chatbot initiatives. They’re platform and data investments—the same kind that determine whether AI in finance is compliant, auditable, and fast enough for real operations.

Why finance should care about government ERP and data upgrades

Finance teams sometimes treat government systems as “external dependencies” that don’t affect product roadmaps. In practice, they shape:

• Identity and verification expectations (how users authenticate for high-trust services)
• Data quality norms (what “good data hygiene” looks like in regulated environments)
• Incident tolerance (how much downtime the public will accept for critical digital services)
• Audit and traceability standards (what regulators expect to see when decisions are automated)

When agencies modernise, they don’t just improve internal efficiency. They also tighten the ecosystem: vendors, integrators, and adjacent industries—finance included—are expected to operate at similar maturity.

The hidden link between public modernisation and AI in finance

Answer first: public sector IT upgrades create the conditions that make financial AI more reliable—by improving data integrity, digital identity, and operational resilience.

Banks and fintechs typically focus on three AI hotspots: fraud detection, credit scoring, and personalisation. Each one relies on consistent data and resilient systems.

1) Better data systems raise the floor for model quality

The AER investment is framed around modernising core data and digital systems and reducing regulatory burden. That sounds sector-specific, but the underlying pattern is universal:

• better data capture
• clearer data definitions
• fewer manual reconciliations
• stronger lineage and auditability

In fintech terms, this is the difference between training a credit risk model on a patchwork of fields versus training on a governed dataset where you can explain exactly where each input came from.

A practical rule I use: if you can’t trace a feature end-to-end, you shouldn’t automate decisions with it. Government modernisation programs tend to force that traceability.

2) Digital services modernisation changes user expectations

Veterans’ Affairs funding explicitly targets ICT, digital, and data enhancements. When government services improve, the public recalibrates what “normal” looks like:

• faster turnaround
• fewer forms
• clearer status updates
• more proactive communication

That expectation transfers to financial services quickly. If someone can update details with a government agency in minutes, they’ll be less forgiving of a bank that takes days for the same workflow.

AI can help (document classification, assisted customer service, intelligent routing), but only if the back office isn’t held together by brittle integrations.

3) Security and resilience are now table stakes

The MYEFO tech items also include resilience funding for critical services, including improvements around emergency communications and authentication in health systems. Even if you’re not in those sectors, the policy direction is obvious: critical infrastructure must keep working.

For AI in finance, resilience means:

• your fraud detection can degrade gracefully (not fail open)
• your model serving can operate under partial outages
• your identity controls remain strong during incidents

AI amplifies both capability and risk. The more you automate, the more you need disciplined engineering.

What fintech leaders should copy from these programs (and what to avoid)

Answer first: copy the sequencing—modernise platforms and governance before scaling AI; avoid building AI on unstable legacy workflows.

Government programs are famous for complexity, but they also reflect hard-earned lessons from running services at national scale.

Copy this: invest in “boring” modernisation first

If you’re trying to scale AI in finance, prioritise:

1. Master data management for customer, account, merchant, and device identities
2. Event-driven data pipelines for near-real-time fraud and monitoring use cases
3. Role-based access and audit logging across data, models, and prompts
4. Model risk management processes that match your regulatory exposure

ERP modernisation at the ATO is a reminder that core finance and procurement systems aren’t optional. If your fintech is growing, ERP maturity becomes a control plane for:

• spend governance
• vendor risk
• internal controls
• audit readiness

And those directly affect your ability to ship AI safely.

Avoid this: “AI theatre” without operational ownership

A common failure mode in both government and private sector is building prototypes that nobody owns in production. The fix is simple and unpopular: assign an accountable operator.

If you can’t answer these questions, you’re not ready to scale:

• Who is on-call when the model drifts?
• Who approves feature changes?
• What’s the rollback plan if a fraud rule or model update spikes false positives?
• What’s the customer remediation path when automation gets it wrong?

A practical checklist: AI-ready modernisation for banks and fintechs

Answer first: an AI-ready financial institution has governed data, secure identity flows, measurable controls, and production-grade monitoring.

Here’s a concrete checklist you can use in planning sessions. It’s designed to be blunt.

Data and integration

• Single source of truth for core entities (customer, account, merchant)
• Documented data contracts between producers and consumers
• Lineage from source system to feature store to model output
• Latency targets aligned to use case (fraud is minutes/seconds; credit can be slower)

Model governance (especially for credit and financial crime)

• Clear model purpose statements and prohibited uses
• Versioning for models, features, and training datasets
• Regular bias and stability testing (with defined thresholds)
• Human review paths for high-impact decisions

Security and resilience

• Strong authentication and step-up verification for risky actions
• Segregated environments and secrets management
• Model and prompt injection testing for AI-assisted channels
• Incident runbooks that include AI components (not just infrastructure)

Customer experience

• Explainability that a customer service rep can actually use
• Appeals processes for automated outcomes (credit and fraud blocks)
• Proactive notifications when automation takes an action

If you’re missing more than a few items, the honest move is to focus on modernisation first. You’ll still deliver value—often faster than forcing AI into broken workflows.

What happens next in Australia’s AI-in-finance landscape

Answer first: modernisation funding increases the pace of ecosystem-wide expectations: better digital services, tighter governance, and faster adoption of AI across regulated industries.

Australia’s banks and fintechs are already investing heavily in AI for fraud detection and customer service automation. Government’s renewed focus on core systems adds pressure in three ways:

• Procurement and vendor scrutiny: stronger expectations around security, data handling, and operational resilience.
• Regulatory maturity: better internal data systems often translate into better oversight and more detailed reporting requirements.
• Public trust: when government services improve, consumers demand similar reliability from financial apps—especially around identity, disputes, and uptime.

From a lead-generation perspective (if you sell into finance or government), the opportunity is straightforward: organisations modernising platforms need help with data architecture, AI governance, and secure deployment patterns—not just model building.

A reliable AI program is mostly a reliability program: clean data, clear ownership, strong controls, and disciplined operations.

If you’re planning your 2026 roadmap, treat these announcements as a cue to sanity-check your foundations. Where are you still depending on manual workarounds, spreadsheet reconciliations, or fragile integrations? That’s where your AI ambitions will stall.

The question worth asking now isn’t “Where can we add AI?” It’s: Which systems and processes must be modernised so AI can carry real operational load without increasing risk?