When Algorithms Meet Appetite

Australia’s financial sector faces a peculiar dilemma. Algorithms can crunch numbers faster than you can say ‘credit assessment,’ but they’re about as useful as a chocolate teapot when it comes to understanding why a profitable mining company suddenly needs emergency funding during a commodity spike.

The tension isn’t just theoretical. Mid-to-large enterprises don’t want cookie-cutter solutions – they want financing that actually fits their business. Sure, algorithms promise speed and efficiency, but try explaining to a machine why a seasonal retailer’s cash flow looks dire in February but fantastic in December.

This balancing act between machine-speed decisions and human insight defines modern Australian finance. We’re seeing it play out in regulatory frameworks, boardrooms, and deal structures across the country. The question isn’t whether algorithms work – it’s whether they work well enough on their own.

 

Finding that balance is exactly why regulators are insisting on more than just code – in fact, the rule-makers have stepped in to write the guardrails for our digital finance experiment.

Regulatory Guardrails and Digital Ambitions

The Australian Prudential Regulation Authority (APRA)’s CPS 230 framework doesn’t mess around. It mandates that boards and senior management embed cyber-risk governance into their core processes, not just leave it to the IT department. The regulation pushes financial institutions toward accountability and proactive cyber resilience.

Meanwhile, Australia’s National AI Strategy aims big. The Commonwealth Scientific and Industrial Research Organisation (CSIRO) roadmap projects an A$315 billion impact by 2028. The government’s appetite for AI-driven financial services is clear, but so are the expectations for responsible deployment.

These dual forces create a delicate push-pull. Institutions get both the green light to innovate and the red tape to keep them honest. The real test comes when theory meets practice, where algorithms face their first encounter with messy reality.

Theory Meets Reality

The Commonwealth Bank shows what happens when machine learning hits the ground running. They’re using AI for fraud detection, personalised insights, and credit scoring – the bread-and-butter of algorithmic banking.

But here’s where it gets interesting. Seasonal cash-flow patterns still throw these systems for a loop. A Christmas retailer’s financials in March look like a disaster movie, even when the business is perfectly healthy. Bespoke project financings? Forget about it – algorithms struggle with anything that doesn’t fit their training data.

The technology isn’t bad. It’s just that real business is messier than any dataset can capture. Someone still needs to look at those anomalies and figure out what’s actually happening.

That same hybrid approach is reshaping how organisations even manage their vendor networks.

From Audits to Partnerships

BDO Australia’s analysis under APRA’s CPS 234 reveals something fascinating about third-party risk management. Joseph Green, BDO Australia’s risk advisory director, advocates for AI to handle the grunt work while humans focus on strategy.

Green notes that ‘AI is becoming an essential tool for financial services organisations looking to streamline third-party risk management.’ The shift he describes is telling – organisations are ‘moving away from audit-based relationships and toward more proactive, partnership-driven models.’ Translation: we’re done playing gotcha with vendors and ready to actually work together.

It’s almost refreshing. Instead of showing up once a year with clipboards and stern expressions, risk teams can now build ongoing relationships focused on ‘real-time risk mitigation and continuous improvement,’ as Green puts it.

The automation of routine tasks frees up human teams for the conversations that actually matter. Strategic oversight becomes possible when you’re not drowning in compliance paperwork.

And freeing up that headspace shines a spotlight on the specialists who actually turn raw algorithm outputs into tailored finance packages.

Decoding the Machine

Complex financing deals have a way of making algorithms look like enthusiastic but clueless interns. Martin Iglesias, a credit analyst at Highfield Private with over 20 years in corporate banking, knows this better than most.

Take his work securing a $10-million construction facility for an educational expansion. The algorithmic assessment flagged unusual cash flows that looked risky on paper but made perfect sense when you understood the project timeline. Iglesias applied his systematic approach, blending quantitative analysis with qualitative insights to structure a deal that worked.

He’s also helped arrange over $30 million in combined facilities for a national real estate agency, spotting sponsor nuances that models couldn’t capture. Then there’s the online retailer he helped scale from medium-size to $250 million in annual revenue through working-capital facilities – the kind of growth trajectory that would give any algorithm nightmares.

The pattern is clear: algorithms provide the starting point, but experienced analysts like Iglesias turn those outputs into actual financing solutions. It’s like having a really good research assistant who occasionally needs adult supervision.

At the other end of the spectrum, executive teams have their own version of ‘adult supervision’ to ensure strategy and code stay in step.

Executive Oversight in Digital Finance

At the executive level, the challenge shifts to aligning algorithmic tools with corporate appetite. Andrew Cartledge, interim CEO of WiseTech Global, brings his CFO background to this balancing act.

Cartledge works on configuring automated cash-management portals and hedging algorithms alongside tailored credit facilities. His approach focuses on integrating real-time transaction data with strategic planning, ensuring that algorithm outputs align with revenue targets and cost projections.

His willingness to extend his role beyond 2025 reflects the ongoing nature of this work. Digital finance isn’t a set-and-forget proposition – it requires continuous calibration to match corporate objectives with machine capabilities.

The executive perspective matters because algorithms don’t understand context like market positioning or competitive dynamics. Someone at the top needs to translate those broader strategic considerations into parameters the machines can actually use.

It turns out that same lesson – knowing when to step in – applies just as much to consumer-facing algorithms.

Consumer Algorithms Need Oversight

Afterpay’s model reveals an uncomfortable truth about consumer finance: algorithms are terrible at knowing when to say no. Co-CEO Nick Molnar has built a platform serving 16.2 million users across 16,500 retail partnerships, but the real work happens in the oversight.

The ‘buy now, pay later’ model uses algorithms to offer four interest-free instalments, but Molnar’s team maintains human override systems for spending alerts and credit-limit reviews. Because apparently, teaching a computer the difference between ‘I need this’ and ‘I want this’ remains beyond current technology.

This hybrid approach keeps the convenience while preventing financial disasters. When algorithms suggest someone can afford another purchase, human teams can step in and suggest maybe they shouldn’t. It’s like having a sensible friend who stops you from buying that third pair of shoes you definitely don’t need.

And that blend of speed and human sense is exactly what defines the sweet spot in financial services.

The Sweet Spot Between Speed and Sense

Australian financial institutions are finding their rhythm in this hybrid world. APRA’s regulatory framework provides the boundaries, while professionals like Martin Iglesias, Andrew Cartledge, and Nick Molnar show how human judgment fills the gaps that algorithms can’t handle.

The pattern is consistent across sectors: algorithms handle the heavy lifting, humans provide the context and common sense. It’s not about choosing sides – it’s about knowing when to trust the machine and when to trust your instincts.

As it turns out, the secret ingredient in financial services isn’t faster algorithms or smarter humans – it’s knowing which problems need speed and which need wisdom. Just like that chocolate teapot from the beginning, some tools are brilliant for specific jobs and useless for others.

The trick is knowing the difference.