As financial services organisations use data that’s highly sensitive and fiercely governed, many are waiting for regulators to distribute policy and standard guidelines – expected in the medium term – to confidently invest in developing a greater number of underlying models driven by generative AI. In the absence of regulation in Australia there is likely to be some slowing of AI adoption at scale in a general sense, and this may have a greater impact on Financial Services due to the existing regulations around data usage. While a ‘wait and see’ approach may seem preferable, organisations who can utilise their existing frameworks to leverage AI sooner may benefit from the efficiencies it brings, rather than pausing AI adoption until the regulatory landscape is more established. In the interim, use cases relating to banker support and call centre operations are likely to be beneficial, more so than how they provide products and customer advice.

We’re exploring how generative AI can be used to structure external data in banking to help generate new client leads, automate pitch decks in institutional and corporate banking, guide customer service, offer credit and pricing analysis – even aggregate and collate the climate footprint and commitments of current and potential clients. It could also pave new pathways for growth in the institutional banking sector by making products less costly to develop.

For investment managers and financial advisors, generative AI can help collate information and produce consistent financial insights based on large unstructured data – such as an individual’s retirement goals, spending and relevant choices – before providing a statement of financial advice. In insurance, large language models can drive faster, more accurate lead generation and screening as organisations underwrite and evaluate clients. Generative AI is also being widely to optimise code generation and developer efficiency.

One regulation is in place and opportunities for driving value at scale are clear, the sector’s use of generative AI will accelerate, as will enthusiasm for AI’s potential when organisations feel prepared for embedding AI into their core processes that drive value. 

With Australian AI regulations not expected until 2025, financial organisations must set their own AI governance frameworks to manage emerging risks and capture the benefits against a backdrop of changing regulations, standards and community expectations.

Most banks are already using various forms of AI in core and high-risk functions. However, there’s inconsistent understanding of the risks involved and how to manage them across the business and in the AI applications themselves. 

Leading organisations are coordinating AI strategy and governance across the business, informed by and flexible to rapid technological advances, global standards and best practice. It’s important for companies to set their north star by defining their AI ambitions and how they will achieve them safely and responsibly through trustworthy AI. 

On the cybersecurity front, organisations need to take preventative measures to defend against increasingly sophisticated attacks. Many will consolidate their extensive cyber tooling, narrowing in on fewer vendors and moving to cloud-native technologies. They are also de-risking the impact of third and fourth-party breaches and what those mean for their data, looking at ways to better detect and manage weaknesses. 

Within insurance and wealth, many are starting their cyber uplift journey. Meanwhile, banks are investing heavily in application programming interface (API) security, and as regulatory scrutiny increases, their boards want assurance that governance, risks and reporting are under control. 

Quantum computing has emerged as a potential technological gamechanger, revolutionising the very essence of computation as we know it. The implications are profound for industries like the data-driven financial services sector, potentially unlocking unprecedented efficiencies and opportunities while introducing new risks and challenges. 

With many financial services organisations holding personally identifiable information (PII), bad actors may already be harvesting encrypted data in case they can eventually decrypt it using a quantum computer. In banking, there’s an even greater risk: a quantum computer may be able to compromise the encryption methods used for securing tractions and fortifying communication channels between institutions, potentially causing substantial outages, damage and remediation activities. 

Preparing for this isn’t as simple as upgrading encryption. Understanding an organisation’s cryptographic landscape is challenging, and any changes to encryption in these complex environments is not only costly and time consuming, but also extends to a broader ecosystem including updates to hardware in point of sale systems, ATMs and back-end servers. To mitigate this risk, banks would be prudent to better understand their cryptographic landscape and vulnerabilities and start building a roadmap together toward a quantum secure future.

Alongside the risks, of course, are the immense advantages that quantum computers can bring to financial services. In Australia and abroad, research is already exploring how this technology could be used to detect and prevent fraud and financial crime, predict catastrophic financial events, optimise investment decisions, and reduce bias by better understanding the quality of data used in decision making.

As for most tech trends, the larger banks will lead the way for developer experience, taking inspiration from overseas. They’re already investing heavily in AI, particularly to provide developers with first-class tools, improve their productivity and lower the cost of delivery. Banks have also maintained good communities for their developers, which continues to be crucial for talent retention. 

Importantly, developers are just one part of the whole. Larger businesses need to consider the end-to-end experience: an amazing developer experience can only achieve so much if it takes three months to approve a business case. 

In insurance, package configuration is still dominant. The industry is littered with half-executed transformations that have only added complexity to their tech environment. To elevate the developer experience, organisations need to continue down the path of modernising their platforms to those with modern engineering languages thereby creating more career options across the industry.

Financial institutions need a healthy tech foundation to move, innovate and capitalise quickly on emerging technology. But many have a complicated tech stack with a mix of systems collected over the decades. At best, these stacks are hard to integrate with new technology; at worst, they need a complete overhaul. This technical debt hasn’t eased for companies that pursued cloud transformation at its lowest common denominator by simply moving old, clunky tech to someone else’s data centre. 

A shift in mindset is key to solving this. Some banks are already stepping back from technical debt to focus on technical wellness, where sweeping, all-consuming modernisation projects make way for phased, incremental modernisation as part of the organisation’s corporate muscle. Importantly, it’s seen as basic hygiene that drives of competitive advantage, with financial ownership and accountability given to the necessary teams. Organisations are also better understanding the difference between cloud-enabled and cloud-native, and core modernisation is now part of board agendas.