Generative AI (GenAI) has already begun to transform key aspects of the financial-services sector, both in Australia and across the globe. Forward-thinking firms are right now harnessing this powerful, fast-emerging technology for a wide range of use cases – from customer-service automation to product recommendation, and fraud detection, to name but a few.

GenAI’s potential to drive efficiency, improve customer experience and increase compliance within the sector has been highlighted by the IMF, ¹ while a recent EY teams survey of European financial-services CEOs revealed that more than half (51%) have already integrated AI into their capital allocation, while 43% plan to do so within the next 12 months.²

GenAI is clearly powerful and highly regarded, with early adopters enjoying a pathway to strategic advantage while avoiding disproportionate costs. With these benefits in mind, organisations within the Australian financial-services sector are well advised to start integrating GenAI into their business strategies.

GenAI adoption, however, is not straightforward. The challenge for companies is balancing the strategic benefits of this breakthrough technology with the need to navigate operational, data and compliance risk.

GenAI is quite unlike other forms of artificial intelligence. Many business leaders expect it to behave like conventional software, delivering a consistent set of outputs for the same inputs. However, rather than following a rigidly defined deterministic path, GenAI uses probability to generate outputs – which can vary.

To understand the difference, consider a scenario in which three academics are given an identical writing assignment. All three academics complete their papers with the same level of professionalism, but just like probabilistic GenAI, each paper is unique. This variance and non-repeatability are an important area of consideration for heavily regulated financial-services firms, as GenAI may not be the appropriate tool when certainty is required to achieve compliance.

Other key risks associated with GenAI include:

It is important to note that GenAI models are trained on general knowledge across a wide range of topics. Therefore, GenAI models typically require additional training to understand financial-services terminology and data. GenAI also relies on prompt engineering – a methodology for asking GenAI appropriately structured questions, designed to deliver the most consistent outputs.

However, GenAI excels at aggregating and summarising text, assessing large volumes of data, and providing cognitive search across large corporate-knowledge repositories. When used in the right context, GenAI can deliver a step change in knowledge-worker productivity. For example, one EY client was able to double their high-risk customer capture rate thanks to GenAI.

A GenAI financial-services use case may look compelling on paper, but it is important to take a wider view of project value, comparing full cost with the projected return on investment to determine whether the use case is truly financially viable.

For example, GenAI can be particularly resource-intensive. Creating large-language models requires significant computing power, and GenAI also needs high-quality data, sector-specific training and prompt engineering in order to generate the desired outputs. High levels of governance and oversight may also be needed to manage risk and facilitate regulatory compliance, which may make a solution particularly expensive in practice.

Careful prioritisation of GenAI use cases is key to navigating the challenges around risk, governance and cost. EY clients successfully adopting GenAI tend to take a test-and-learn approach. This involves identifying relatively small, low-risk use cases that complement rather than disrupt an organisation’s existing IT, process and governance ecosystem.

“According to global EY research, 91% of organisations are using AI primarily to optimise operations, develop self-service tools like chatbots, or automate processes; only 8% are driving innovation, such as new or improved offerings.”

Australian tier-one financial-services companies generally have robust IT engineering capability and a sound technology backbone, so embracing a test-and-learn approach plays to the sector’s strengths.

For example, using GenAI in a contact centre to summarise a customer interaction can be a powerful but relatively low-risk first-use case. When there is sufficient confidence in the tool, it can then be evolved to enable customer-service agents to extract relevant real-time information during client calls. GenAI can perform lower-risk tasks like these faster than a human agent, enabling the agent to focus on the customer, make better decisions, streamline the interaction, and significantly improve the overall customer experience. EY clients have achieved up to an 80% increase in contact centre effectiveness leveraging GenAI.

Vendors such as Microsoft, ServiceNow and others also continue to release new GenAI capabilities in their products. Organisations should not discourage adoption of these tools – but should ensure vendor contracts have appropriate clauses to address GenAI-specific concerns. Organisations should also have clarity on how to measure GenAI usage within vendor platforms, understand the value being delivered, and have appropriate controls in place to manage risk.

Adopting a test-and-learn approach also gives an organisation an opportunity to learn about GenAI and evolve its existing AI guardrails and governance frameworks – without exposing itself to unacceptable levels of risk and cost.

Evolving guardrails is just one way EY teams are now helping clients across Australia and New Zealand optimise the GenAI life cycle. Underpinned by the EY Responsible AI Framework, this life cycle features four key steps which are central to successful GenAI implementation:

The most successful adopters of GenAI within the Australian financial services sector are likely to be those who use the technology to improve efficiency and productivity. These companies will focus on lower risk use cases that expedite rather than disrupt existing corporate systems and processes.

To be part of this group, firms will need to prioritise the greatest value-creation opportunities based on how GenAI can improve the organisation’s bottom line, using tools such as the EY.ai Value Accelerator to identify AI use cases that boost revenue, reduce cost and optimise EBITDA.

Once organisations have prioritised individual use cases, avoided risk and generated compelling value, they can then use this success as the launchpad for a longer-term vision and direction for GenAI within their organisation.

Author:

Lukas Bower

Content provided by