Heritage Bank Deploys 80 Automated Processes Across Core Banking

From pilot to production at scale

Heritage Bank, Australia's largest mutual bank, has moved robotic process automation far beyond the experimental phase. Over nearly a decade, the institution has deployed digital workers across approximately 80 processes spanning financial crime reporting, loan processing, and customer service operations, according to a case study published by automation vendor UiPath.

The Toowoomba-based lender operates roughly 60 branches and faces the same pressures as many mid-sized financial institutions: modernizing legacy systems while maintaining regulatory compliance and operational resilience. Automation has become central to that strategy, with bots now integrated deeply enough that staff across the organization encounter them daily.

David Johnston, who formerly led intelligent automation and process excellence at Heritage, explained that digital workers now enter fraud alerts into core banking systems, update customer relationship management platforms with payment details, and even post employee announcements on the corporate intranet.

Financial crime compliance as the breakthrough use case

One of Heritage's earliest automation successes came in financial crime operations. When law enforcement or regulators request transaction histories and customer data, staff previously had to manually compile information from multiple systems—a time-consuming process that slowed response times.

Heritage built an RPA bot that automatically gathers and compiles this information once investigators provide the necessary parameters. Johnston noted that these requests arrive sporadically, making the automated workflow highly scalable. For banks under increasingly strict regulatory oversight, automating investigative tasks reduces response times while maintaining full auditability.

Machine learning pushes loan processing automation to 90%

After proving RPA's value in operational workflows, Heritage integrated artificial intelligence into its automation program. A key application emerged in loan underwriting, where regulatory requirements demand detailed analysis of borrower spending patterns to estimate living expenses.

Initially, rule-based automation could classify only 40 to 50 percent of transactions, leaving the remainder for manual review. By implementing machine-learning models within its automation environment, Heritage now expects to automate roughly 90 percent of the transaction analysis required for living-expense reports.

The bank established a centralized RPA Centre of Excellence to identify automation opportunities, develop workflows, and ensure bots interact safely with production systems. Johnston led a team of developers within the center, supported by automation champions embedded across business units.

Why it matters

Heritage's deployment illustrates how automation is shifting from back-office IT tasks to production banking operations where digital workers interact directly with core platforms and compliance systems. This evolution places new demands on quality assurance and software testing teams, who must now validate automation logic, integration stability, and production reliability as rigorously as they test the underlying banking systems. For mid-sized institutions, the case demonstrates that automation at scale is achievable without the resources of global banks—but requires disciplined governance and cross-functional collaboration.

Johnston emphasized that combining RPA with AI significantly expands the range of processes banks can automate. He encouraged organizations to focus on delivering practical solutions rather than getting stuck in theoretical debates about AI governance.

The details were first reported by QA Financial in a case study based on Heritage Bank's partnership with UiPath.