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June 10, 2025
On a day about differentiation, Tim Moloney started by acknowledging a statement of fact – “banking is a highly commoditised business.”
Standing out is a struggle in a market structure dominated by a small number of large, well-established institutions in the ‘Big Four’. This oligopoly leads to limited product separation and similar pricing frameworks.
Plus, regulatory uniformity through strict compliance and standardised practices enhances stability but also homogenises offerings.
Consequently, many consumers make decisions based on price or convenience rather than brand loyalty or innovation – reinforcing the commoditisation effect.
“Winning in this space is all about customer experience,” said Moloney, speaking as Head of Digital Strategy and Head of Financial Institutions Sales – Global Markets Division at ANZ Bank.
“We believe that the future of customer experience lies at the intersection of personalisation, predictive analytics and agentic AI. Each of these are powerful in isolation but transformative when combined, which presents a great opportunity for us at ANZ.”
In headlining Builders Day at AWS Summit Sydney 2025, Moloney detailed the meticulous journey of ANZ in enhancing customer experience – highlighting the business value of incorporating practical thinking into bleeding-edge innovation projects.
The headline was the upcoming launch of amie, a “multi-agent chatbot” built for bankers to act as a personalised market analyst.
But while the destination demonstrates strong market appeal, it’s the journey in this case that carries significant weight as Australian businesses grapple with how to truly maximise the potential of AI.
“As professionals in technology and data, we’re all keenly aware that AI isn’t just changing the way that we work, it’s fundamentally reshaping the way that we engage with our customers,” Moloney added. “And we’re just at the beginning.”
According to Moxie Research, 87% of Australian businesses will allocate additional budget to AI projects in 2025. In addition, 78% will build a cross-functional team to set AI strategy while 69% will define clear AI business objectives.
Laying the AI building blocks
The institutional arm of ANZ continues to maintain leadership in the Australian market, ranking no.1 for overall customer relationship strength in the Coalition Greenwich 2024 Large Corporate and Institutional Relationship Banking Survey, Australia.
Active across 29 countries, the markets business processes over three billion data points on a daily basis, running over 1000 customer analytics across thousands of product combinations.
“Everything from multi-year structured bonds to spot foreign exchange, priced in milliseconds,” Moloney outlined. “And every product and customer portfolio is continually changing in line with global markets.
“In fact, it’s this state of constant flux that’s been one of our key analytical challenges. AI is helping us solve this puzzle in real time and at scale.”
ANZ’s approach to AI has been “iterative and considered” with ethics and customer security, safety and privacy considered “non-negotiable” in all innovation work.
Even though capability expansion has been underway for a while, the bank truly realised its ambition in AI through the release of Signals in 2021. This delivers personalised, actionable market intelligence to bankers, whether that’s notification of an upcoming deal maturity or a potential restructuring opportunity.
For Moloney, this was the AI acceleration moment.
“Signals turned us from being reactive with data to proactive,” he shared. “But it was still just a one way conversation.
“So early in 2023, we started exploring whether we could augment Signals with chat capability. But we pretty quickly worked out that given the diversity and complexity of underlying data, some sort of agent-based solution was going to be required.”
Following strategic collaboration with AWS’ prototyping team in 2023 – and a trial with select commercial bankers last year – the business is preparing to go live with amie. This is short for ‘ANZ Markets Insights Engine’.
From ideation to implementation, the timeline is as follows:
“At its core, amie is a multi-agent chatbot that seamlessly integrates with Signals,” Moloney explained.
“It will allow our bankers to ask pretty much anything about markets, whether it’s about research, news, policies or of course, customer analytics. We like to think of amie as a personalised markets analyst that our bankers can call on any time, anywhere.”
Moloney cited AWS as a “cornerstone partner” throughout the innovation journey, first utilising Simple Storage (Amazon S3) to access scalability, data availability, security and performance features.
Expansion to data science on SageMaker followed and now includes AI on Bedrock – plus data governance, lake formation and scaling its institutional data and analytics platform on SageMaker Unified Studio.
“This partnership has been transformative in helping us build towards our data, machine learning and AI ambitions,” Moloney added.
Armed with the benefit of hindsight, Moloney shared noteworthy observations from the AI build phase at ANZ which are continuing to help shape future strategy and execution.
“The first of these is on user experience,” he noted. “Bounded within solid guardrails, we’ve learned that balancing time against accuracy and quality of output is critical.
“If one of our bankers is talking to a customer, they need information in seconds, not tens of seconds or minutes. But it can’t be at the expense of quality or accuracy.”
In short, the triangular guardrails shaping AI decisions are:
“What we’ve learned is that by utilising different tools and frameworks for different use cases, we’re able to optimise and much better manage those trade offs,” Moloney added.
For example, ANZ’s toolkit includes Text2SQL for simpler data retrieval and joins with recent customer activity, alongside function calling for more complex analytics such as portfolio restructuring.
Baseline RAG is used for news and research retrieval and graph RAG for more complex policy and knowledge navigation.
“Another area is personalisation,” Moloney detailed. “Now, it doesn’t matter what industry you operate in, we know that customers expect a highly personalised experience.
“At ANZ, we’re actively working to build this but we’re conscious of the fact that it needs to be done in a really considered and safe way. Neural networks are great at capturing complex interactions but traditionally it’s been at the expense of transparency, explainability and accessibility.”
Moloney said large language models (LLMs) have transformed accessibility through intuitive chat interfaces with ANZ “closely monitoring” some of the “remarkable research” that’s currently going on into interpretability and alignment.
“But what we have found so far is that GenAI can genuinely complement more traditional machine learning methods,” Moloney expanded. “It generates contextualised content that dynamically responds to evolving customer preferences and needs.”
Based on Moxie Research, 84% of Australian businesses are currently running internal AI working groups to shape strategy and drive adoption.
While progress varies based on maturity, the key benefits of cross-collaboration between business units is to primarily drive efficiency and process optimisation (59%).
This is alongside creating innovation and a competitive edge (51%), plus enhancing customer experience (50%) and kick-starting change management and integration processes (49%).
On AI, what’s next for businesses?
By way of definition, agentic AI refers to AI systems capable of autonomous decision-making and action, often pursuing goals without constant human oversight.
Unlike traditional AI models that require specific prompts or supervision, agentic AI operates with a degree of independence – planning, executing tasks and adapting to environments. This evolution is driven by advancements in LLMs, reinforcement learning and multi-modal capabilities.
The rise of agentic AI marks a shift from passive tools to active collaborators, with applications in coding, personal assistants and complex workflows. As these systems mature, they promise to transform productivity, creativity and human-AI interaction across industries.
From a practical perspective, Moloney said agentic AI helps solve a problem that is familiar to all businesses across Australia, regardless of company size or industry sector.
How do you demonstrate real business value with the technology in a way that’s scalable but doesn’t require you to commit to a substantial, usually multi-year, upfront investment?
“Agents support a modular approach where each agent can tackle a particular opportunity and if it’s successful, it just gets added to the stack,” Moloney explained.
“Their degree of autonomy can be carefully calibrated to the guardrails required and benchmarks can be set and tested at both the agent and the application level.”
Naturally, Moloney said this modular approach becomes “exponentially more powerful” when agents are portable.
“And the really exciting part at present is not just that AI agents are becoming a lot smarter, it’s through standardised protocols like MCP and other emerging agent-to-agent frameworks, they’re becoming a lot more portable and interoperable,” he continued.
Solutions such as aime demonstrate that subject matter experts – for example, an interest rate specialist – can now build an agent that captures their deep domain knowledge.
“Sort of like cloning themselves and their expertise,” Moloney described.
“Those agents can then be deployed across the entire organisation, irrespective of which applications different teams use. This federated model scales expertise and not just the underlying technology.”
Strategically, Moloney said ANZ understands that AI is about more than just efficiency.
Rather, agentic AI enables the business to deliver personalised expertise to every interaction, whether that’s a banker better understanding a customer’s complex needs amid global market volatility, or just simply having wider access to specialist knowledge instantly.
“At ANZ, we’re building for this future with deliberate, safe steps, creating an exceptional customer experience and through that, helping customers focus on what they do best, which is grow their businesses,” Moloney added.
By 2029, agentic AI will autonomously resolve 80% of common customer service issues without human intervention, leading to a 30% reduction in operational costs.
That’s according to Gartner, which claims that the technology is poised to “revolutionise” the way service interactions are conducted.
While previous AI models were limited to generating text or summarising interactions, the analyst firm believes that agentic AI introduces a “new paradigm” where AI systems possess the capability to act autonomously to complete tasks.
Both customers and organisations will leverage this technology to automate interactions through the use of AI agents and bots, fundamentally reshaping the relationship between service teams and their customers.
“Agentic AI has emerged as a game-changer for customer service, paving the way for autonomous and low-effort customer experiences,” added Daniel O’Sullivan, Senior Director Analyst at Gartner.
“Unlike traditional GenAI tools that simply assist users with information, agentic AI will proactively resolve service requests on behalf of customers, marking a new era in customer engagement.”
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