By Pascal Allix (pictured), Regional Vice President, Financial Services, Appian ANZ

Australia’s banking sector is navigating a period of sustained operational pressure. Regulatory expectations continue to expand, customer interactions are increasingly digital and real-time, and economic uncertainty is elevating risk management demands. Against this complex backdrop, many financial institutions’ operations remain reliant on legacy systems that were not designed for the speed, transparency and adaptability today’s banking requires.

For CIOs, the challenge is not simply modernisation but also demonstrating measurable operational value while maintaining stability and regulatory compliance. Increasingly, banks are recognising that transformation is not simply about replacing core platforms. Instead, progress is being achieved by improving how work flows across systems, data and teams.

Compliance pressure is reshaping operational priorities

Regulatory scrutiny continues to intensify in Australia. APRA’s CPS 230 Operational Risk Management standard is reinforcing expectations around operational resilience, third-party risk management and service continuity, while anti-money laundering obligations, scam prevention expectations and governance requirements demand transparency in decision-making.

These pressures are exposing operational challenges inside banks. Manual processes, fragmented systems and inconsistent data flows can introduce compliance risk, delay regulatory responses and limit audit traceability.

As regulatory expectations rise, many institutions are recognising that improving compliance outcomes requires greater operational visibility and control across end-to-end processes.

AI adoption is accelerating, but impact depends on integration

Artificial Intelligence (AI) is moving rapidly from experimentation to everyday use across financial services in Australia. According to the Finance Sector Union (FSU), in 2025, 36% of Australian finance workers said they often use AI, almost double the 19% the union’s survey found in 2024. Additionally, the share of people who never use AI dropped from 24% to just 13%.

Yet adoption alone does not guarantee impact. Early uses have often focused on standalone tools or isolated use cases, delivering incremental gains but limited enterprise value.

AI delivers the greatest benefit when embedded within optimised operational processes. When integrated into end-to-end workflows, AI outputs become transparent, auditable and measurable. Human oversight can be built into decision pathways, ensuring outcomes remain consistent with regulatory expectations and institutional policies.

Data and process foundations determine AI success

AI effectiveness is also directly tied to the quality and accessibility of data. Many banks continue to operate with fragmented data, where information is dispersed across legacy systems and departmental platforms.

A unified data fabric can connect these sources, creating a consistent pipeline that supports AI insights while ensuring outputs flow directly into operational processes. This approach not only improves predictive accuracy but also enhances compliance monitoring, fraud detection and cyber-risk oversight.

Using Appian’s data fabric, UK bank NatWest created a unified data model that integrated 14 disjointed processes. By automating 46% of data in its governance processes, NatWest was able to decrease product governance cycles time from 4.5 days to less than 20 minutes.

Equally important is process design. Process intelligence can help banks to continuously analyse operational data to reveal bottlenecks, rework loops and compliance vulnerabilities that may otherwise remain hidden. This visibility enables institutions to refine processes end-to-end rather than optimising isolated tasks.

Banking operations increasingly operate in a model where AI assists with analysis and recommendations while humans provide oversight and judgement. Optimised processes ensure work is routed efficiently between systems, digital tools and staff, allowing both human and digital workers to perform effectively.

Risk, compliance and fraud prevention remain primary AI priorities

AI is already demonstrating value in risk and compliance functions, where growing regulatory scrutiny and rising financial crime require more adaptive controls.

In lending and credit processes, AI-powered automation can enhance compliance checks, support document verification and improve fraud detection while accelerating decision-making. Credit assessment, Know Your Customer (KYC), anti-money laundering (AML) and collateral management processes, which are traditionally complex and resource-intensive, are well suited to automation.

Fraud prevention is another critical application. Traditional rule-based monitoring systems struggle to detect evolving fraud patterns. AI can analyse behaviour across vast data sets to identify previously missed patterns, anomalies and suspicious relationships, improving detection accuracy. It also enables investigators to access relevant information faster, improving response times and case resolution.

In this environment, transparency and auditability are essential. Regulators increasingly expect institutions to demonstrate how risk decisions are made and governed. AI systems must therefore operate within structured processes that ensure decisions can be reviewed, explained and validated.

Governance, risk and operational resilience in an AI era

In highly regulated sectors such as banking, data governance is essential. Institutions must carefully evaluate how AI models are trained and how data is used. Financial institutions face risks related to bias in algorithms, misinformation generated by AI systems, cybersecurity threats and intellectual property protection.

Public AI models trained on shared datasets may introduce intellectual property and data privacy risks. Private AI models, by contrast, are trained on proprietary data and operate within secure environments, ensuring sensitive information remains within institutional boundaries. This distinction is particularly important for compliance, risk analytics and customer data protection.

While AI can accelerate analysis and highlight insights, expert human review ensures decisions remain accurate and appropriate. Governance controls, audit trails and risk oversight frameworks act as guardrails, preventing overreliance on automated outputs.

Operational resilience is also central. CPS 230 reinforces the need for institutions to maintain service continuity and manage operational risk across critical functions. Technologies that enhance visibility, standardise processes and support rapid response to disruptions contribute directly to these resilience objectives.

From operational complexity to intelligent banking

AI is not a standalone solution to banking’s operational challenges. Its value is proven when it is supported by connected data, well-designed processes and strong governance frameworks.

As regulatory expectations rise and operational demands intensify, Australian banks are shifting focus from isolated technology deployments to integrated operational improvement. Coupled together, automation, process optimisation and AI are enabling institutions to strengthen compliance, improve risk oversight and enhance customer experiences while maintaining trust.

For CIOs, the next phase of transformation should be focused on enabling intelligent operations that deliver transparency, resilience and measurable value across the enterprise.