The Growing Adoption of AI in Banking in Australia and New Zealand

By Iman Ghodosi, Managing Director Australia and New Zealand at Backbase

As Artificial Intelligence (AI) continues to revolutionise industries worldwide, its influence on banking in Australia and New Zealand is becoming increasingly pronounced. The adoption of AI within the banking sector in both countries is accelerating, driven by the shift towards digital banking, evolving consumer expectations, and the need to meet stringent regulatory standards while improving operational efficiency.

Both large and mid-tier banks are leveraging AI to spearhead digital transformation, focusing on automation and personalised customer experiences. These innovations enable banks to deliver quicker, more customised digital interactions, meeting the growing demand for intuitive, user-friendly platforms. Furthermore, some banks are integrating generative AI to overcome the limitations of legacy systems, providing more flexible and responsive operations to stay ahead of the rapidly changing digital landscape.

AI applications in the Australian and New Zealand banking sectors are diverse, ranging from automating routine tasks to enhancing customer engagement and improving risk management and regulatory compliance. These advancements give banks a competitive advantage in an environment where efficiency and customer satisfaction are increasingly critical​

Some of the most significant ways in which AI can add value for banks by driving growth and efficiency include:

• Personalised financial services at scale:
  Personalisation has become a key differentiator in the banking industry, and AI enables banks to offer tailored financial services on a large scale. By analysing customer data – such as spending habits, income, and life events – AI-powered recommendation engines can suggest appropriate financial products and investment strategies.

  This ability to offer the right product to the right customer at the right time can significantly enhance cross-selling and up-selling efforts. For example, a bank could use AI to recommend specific investment opportunities to clients based on their financial profiles, thereby improving customer satisfaction and loyalty.

• Customer service automation:
  Customer service is another area where AI can have a substantial impact. Many customer inquiries are repetitive and can be addressed through AI-powered chatbots that use natural language processing (NLP) models. These chatbots can handle common queries and seamlessly escalate more complex issues to human agents when needed.

  By implementing AI in customer service, banks can reduce the need for large call centre teams, cut response times, and lower overall operational costs. Additionally, well-trained AI systems can enhance customer satisfaction by providing timely and accurate responses.

  As an example, Beyond Bank is working to improve its call centre experience for customers through the deployment of AI tools and chatbots. The bots allow the call centre to respond to 50% more chat queries than in the past with the capability being made available 24/7.

• Process automation and streamlined operations:
  One of the most sought-after applications of AI in banking is process automation. By utilising advanced robotic process automation (RPA), banks can automate repetitive tasks such as account reconciliation, document processing, and compliance reporting.

  This not only reduces the scope for human error but also lowers operational costs. AI-driven automation enables banks to optimise both rule-based tasks and complex decision-making processes that rely on data insights.

  For example, National Australia Bank is using AI tool OpenAI to streamline the way paralegals review trust deeds as part of financial transactions. The bank reviews around 15,000 trust deeds each year and each has traditionally taken a staff member around 45 minutes to capture relevant details. OpenAI can complete this task in just one minute.

• More accurate credit risk assessment:
  Assessing credit risk is a time-consuming task that requires analysing multiple data points, including customer behaviour and transaction history. AI improves both the speed and accuracy of this process by analysing a broader array of data swiftly.

  Through predictive models, banks can generate dynamic and personalised risk profiles, leading to better-informed lending decisions. This not only reduces the likelihood of defaults but also allows banks to extend credit to a broader range of customers, thus driving growth.

  As an example, Bendigo and Adelaide Bank is making increasing use of AI tools to streamline its home loan application and approval process. The bank is relying on home lending platform Tiimely in which it has an investment.

• Enhanced fraud detection and prevention:
  AI’s capacity to detect patterns that elude human analysis makes it a powerful tool for fraud detection. Banks can deploy machine learning algorithms trained on vast sets of transaction data to identify suspicious activities and abnormal patterns.

  The early detection of fraudulent activities helps banks minimise financial losses and enhances customer trust. By adopting AI-driven fraud detection systems, banks can move beyond traditional rule-based methods to a more dynamic and proactive approach, safeguarding both their assets and their customers.
  

Key considerations for AI adoption in banking

While the potential of AI in banking is significant, its adoption is not without challenges. Banks must navigate factors like regulatory compliance, market readiness, and resource allocation. Some of the key considerations for banks looking to integrate AI into their operations include:

1. Avoiding siloed implementations:
   AI should not be treated as just another point solution. Instead, it should be integrated into the bank’s broader strategic vision, enhancing both internal processes and customer-facing services. This holistic approach ensures that AI creates value throughout the entire value chain.
2. Starting small, then scaling:
   Adopting AI does not require a complete overhaul of existing systems. Banks can begin by using AI for targeted applications, such as providing personalised financial recommendations or automating specific tasks. As regulatory frameworks evolve and banks gain more experience with AI, they can expand its use across other areas of operation.
3. Empowering employees, not replacing them:
   AI should be seen as a tool that complements human abilities rather than replacing them. By augmenting the skills of employees, banks can enhance productivity and improve decision-making processes. This approach also helps to address concerns about job displacement and promotes a collaborative environment where AI and human expertise work together.

The path forward

AI is reshaping the banking sector, offering opportunities for banks to boost efficiency, reduce costs, and provide more personalised services. However, the successful adoption of AI in banking requires careful planning and a willingness to adapt to a rapidly evolving technological landscape.

While some banks have taken a cautious “wait-and-see” approach due to regulatory uncertainties, others are moving quickly to integrate the technology into their operations. The banks that strike the right balance between innovation and risk management will be best positioned to thrive in the AI-driven future.