Innovation and sustainability in Financial Services: where to from here?

Last month, Sandstone Technology attended the Future of Financial Services conference in Sydney, staged annually by FST Media. The theme was a compelling one: ‘Pioneering Purpose Driven Innovation and Sustainability to Reshape the Future of Financial Services’. As always, it was a coming together of key industry thought leaders and experts from banks, fintechs and other financial institutions (FIs), exploring issues that affect us all. 

The responses to the topic from speakers and other delegates were varied, and in some cases, a real wake-up call for the sector. We’ve grouped our main takeaways into three areas: 

1. Mobilising your workforce to meet your transformational objectives
2. Current and future use cases for driving productivity and efficiency through generative AI
3. The importance of data

1. Are you making the most of your talent?

To kick off day one, Paul Norman, CIO, Personal Bank, from NAB walked us through their transformation program. By breaking down the silos in their global workforce, they’ve been able to meet challenges in customer expectation and experience. This innovation has also reduced their time to yes across their home loan journey. By shifting the mindset from onshore vs offshore to the creation of a ‘one team’ approach, and ensuring product and tech are working closer than ever before, they are able to support a ‘follow-the-sun’ model and drive and empower their staff to solve complex problems. 

We heard a similar endorsement for bringing your people along on the digital journey from keynote speaker Sanjoy Sen, MD and Group Head Consumer Banking at DBS. According to Sanjoy, investing in future-ready, cutting-edge infrastructure allows banks to scale at speed. Having your people understand the power of digitisation and automation gives you a leading edge. You can move fast when you need to and take advantage of the data and analytics at your fingertips. 

Viewing technology as an enabler of enhanced customer experiences, one that also reduces manual tasks and reduces your overall cost to serve, is the way most FIs are approaching their transformation programs — with one question laid bare: to buy or to build? Sanjoy’s advice was: stay true to your core. What are you good at? Does this fall within your wheelhouse? If not, find a partner. Relationships with fintechs should be viewed as partnerships that enable different parts of the financial services value chain — not always as a competitor to your internal teams.  

More and more FIs are looking at how they can break down silos and ensure teams are set up for success by inserting digital skillsets into multiple business units. And they’re seeing results. Bringing, product, tech, data and analytics together alongside sales and marketing, backed by the right digital tools, allows for personalisation at a customer level and removal of friction throughout the customer journey.  

2. Where are you on the generative AI journey?

If you’re not already investigating how generative AI (or ‘gen AI’) and large language models (LLM) can assist your business efficiencies and/or improve the overall customer experience, you may be falling behind. And that applies equally whether you’re a financial institution or a financial technology player supporting the industry. There was barely a session that did not touch on AI in some way. 

As an industry, we’ve moved very quickly from embedding fully digital experiences into the customer banking journey, to using AI and ML to drive cognitive banking. Just look at use cases surrounding next best offers/conversations, push/pull notifications and OCR technology that can read documentation. In fact, we’re now staring down the barrel of LLM and gen AI being used as an every-day tool for driving conversational banking, i.e., hyper-personalised conversational experiences tailored for each individual in real time with no human interaction. 

However, gen AI is not just driving the customer experience, it’s also being used for back-end processes such as text to code, knowledge base management and improving speed to information. LLM and gen AI are powerful drivers of middle and back-office efficiencies.  

Tim Hogarth, CTO at ANZ Bank, predicts that within two years, gen AI could have moved out of the exploration stage and into adoption. We are witnessing the fastest rate of change of any emerging technology to date. As he put it, we are moving out of a ‘If this… then that…’ approach very quickly into a ‘Based on this data... it’s a probability that…’ approach. The predictive nature of gen AI and the level of accuracy it brings with it suggests we will be able to predict consumer behaviour and pre-empt their next steps before they take them. 

Yet, many are still questioning whether it’s worth it. 

The simple answer is yes. 

Westpac Group CTO, David Walker, provided potent evidence. At Westpac they have 150 engineers currently using gen AI in AI-pair programming, and they’ve already experienced a 46% overall uplift in productivity. 83% of their developers said these tools are helping them learn faster. David is expecting the number of developers using AI-pair programmers to increase to 1000 by the year’s end.

3. Why is data so important?

The way we’re using data has changed. As per point 2 above, we are moving at speed to implement gen AI and LLM into the banking ecosystem. However, the use of these technologies needs to be underpinned by one key ingredient… data. 

And the integrity of this data is key for the success of any form of decision-making capability. We need to maximise the value from the data, using data modelling to predict and detect scenarios, as well as for insights or evidence to help improve our decision-making capability.

Seeding a data-driven mindset throughout the entire organisation is a critical part of any FI’s data strategy. From the top down, people need to understand the importance of the data, with leaders providing the right level of transparency on how it’s used and assigning appropriate levels of access to the data.

Making the right data accessible to the right people at the right time is necessary to meet both customer expectations and corporate objectives. This can only be achieved by having one source of truth (i.e., data lake). With analytical data science and business intelligence capabilities managed through one technology platform, the organisation can bring its data strategy to life. 

With the rate of change we’re experiencing, and the volumes of data being collected, currently the highest ever in living history, these arguments all come with a word of warning. There needs to be ongoing conversations around retention periods, validity, privacy and security of data. And organisations must always be asking: how quickly does the data we hold become out of date, and what percentage of it becomes obsolete? 

The rapid acceleration in technological advancement and shifts in consumer behaviour are presenting us with many challenges as an industry. The question is how we respond to them with a social, moral and ethical mindset.