For many financial institutions, robotic process automation is still associated with small IT pilots. At Heritage Bank in Australia, however, automation has moved deep into operational banking workflows.
The Australian lender, which is the country’s biggest mutual bank, has deployed digital workers across a wide range of processes, from financial crime reporting to loan processing and customer service tasks.
According to a detailed case study published on the website of the bank’s software testing partner UiPath, the bank has tested, integrated and automated around 80 processes since launching its automation programme nearly a decade ago.
The rollout offers a window into how mid-sized banks are increasingly using automation not only to cut operational workloads but also to support broader digital resilience strategies that rely on stable, well-tested workflows interacting with multiple core systems.
David Johnston, the bank’s former intelligent automation and process excellence manager and these days general manager Innovation & Growth at Autism QLD, explained in the case study report that the technology quickly proved its value once the first bots were tested and went live.
“These bots are entering information into the core banking system around fraud alerts,” Johnston said.
“They’re putting notes into our customer relationship management systems around payments or transfers that processed with addresses that changed and a whole bunch of other things,” he added.
“Our robots even post about promotions, anniversaries, and birthdays on our corporate intranet.”
The robots are now so integrated into everyday operations that staff across the organisation encounter them regularly, Johnston continued.
“So, realistically, everyone at Heritage knows all the robots and sees them doing things,” Johnston noted in the case study. “There wouldn’t be a person at Heritage going through their lives without being touched on by UiPath.”
Scaling automation
Heritage Bank is one of Australia’s largest customer-owned financial institutions and the country’s biggest mutual bank.
Headquartered in Toowoomba in Queensland, the organisation provides retail banking products including mortgages and savings accounts through roughly 60 branches across Australia.
Like many banks of its size, Heritage faces pressure to modernise legacy processes while maintaining strict regulatory compliance and operational resilience.
Automation became one of the bank’s key strategies for addressing these challenges.
To support the rollout, Heritage established a centralised RPA Centre of Excellence responsible for identifying automation opportunities, developing workflows and ensuring bots interact safely with production systems.
Johnston led a team of developers within the centre, supported by automation champions embedded in business units across the bank.
Together they designed, tested and deployed digital workers capable of interacting with core banking platforms, customer relationship management systems and internal reporting tools.
“When you start to use AI in various ways, it really broadens the type of process that you can go after from an automation perspective.”
– David Johnston
One of the first successful automation use cases emerged in the bank’s financial crime operations. Investigations and law-enforcement requests often require banks to compile detailed transaction histories and customer data across multiple systems.
Previously, staff had to manually gather the required information from core banking platforms, CRM tools and other databases.
Heritage built an RPA bot capable of automatically collecting and compiling this information once investigators provide the required parameters.
“The financial crimes use case was very successful,” Johnston said. “These requests come through sporadically and are dealt with by the bots and the process is very scalable.”
For banks operating under increasingly strict regulatory oversight, automating investigative workflows can significantly reduce response times while maintaining auditability.
AI rollout
After proving the value of RPA in operational workflows, Heritage began integrating artificial intelligence into its automation programme.
A key opportunity emerged in the loan application process, particularly in analysing customer spending patterns to estimate living expenses.
Regulatory expectations require lenders to assess borrowers’ financial behaviour in detail, meaning staff often need to review large volumes of transaction data.
Initially, rule-based automation could classify only around 40–50% of transactions, leaving the remainder for manual review.
To address this, Heritage implemented machine-learning models within its automation environment to analyse transaction histories and categorise spending.
The improvement in automation levels was substantial. The bank now expects to automate roughly 90% of the transaction analysis required to generate living-expense reports.
Automation and testing intersect
For QA and software testing teams inside financial institutions, the Heritage Bank case highlights an important shift: automation is no longer confined to test environments or IT back-office tasks.
Instead, it is increasingly embedded in production banking operations, where bots interact directly with core banking platforms, compliance systems and customer databases.
This makes robust testing, monitoring and governance of automation workflows essential.
Each digital worker must reliably interact with multiple systems while maintaining compliance with regulatory requirements and internal controls.
Johnston believes the combination of RPA and AI significantly broadens the types of banking processes that can be automated.
“And when you start to use AI in the various ways that UiPath offers, it really broadens the type of process that you can go after from an automation perspective,” he shared in the case study.
Johnston added that organisations should focus less on theoretical debates about AI governance and more on delivering practical solutions.
“It’s important to keep delivering solutions so that it becomes less of a conversation about AI governance and more about the delivery of AI,” he was quoted as saying.
The Heritage Bank rollout illustrates a wider trend across financial services: automation programmes are increasingly becoming a core component of digital resilience strategies.
As banks deploy bots and AI models across operational processes, QA and software testing teams are taking on a broader role in validating automation logic, integration stability and production reliability.
Ensuring these digital workers operate safely inside live banking environments is quickly becoming as important as testing the software systems they interact with.
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