Australian banks have had an unsettling year. Global instability in the financial services sector combined with the nation’s cybersecurity and Privacy Act reforms are putting more pressure on banks with regard to how they manage and use data.
At the same time, the rise of the digital economy has changed the banking customer. He is now more price sensitive and has much less brand loyalty than in the past. According to a recent Australian Banking Association Senate Inquiry submission, almost 70% of banking customers are now switching between lenders.
The challenge is that if banks want to attract, maintain, and grow customer accounts, while also remaining resilient in managing operational expenditure, they can no longer rely on an old school approach in the new, generative-AI world.
In banking, there is now a battle between the coded world and the low code/no code world. And coded systems don’t operate well with generative-AI technologies.
For example, generative-AI requires strong data quality, governance, and cadence. Coding, however, requires less stringent requirements. Put simply, it means that banks relying on hard-coded data management technology are not setting themselves up for success in a generative-AI world.
Unfortunately, many Australian banks still rely heavily on in-house coding. They are paying the price for not having the level of agility that is now required in a digital world. Because of hard coding, these banks are stuck with incredibly siloed business units and data.
The potential for generative-AI in banking
In banking, as well as other sectors, we are already starting to see the potential for generative-AI to have major impacts on productivity, roles and staffing. As well as cost savings for data management. Particularly regarding:
- using generative-AI and natural language processing to find data, which can literally reduce days of work down to hours or even minutes.
- autonomous data matching, merging and survivorship rules, which historically could take months and can now be completed within two weeks;
- building data pipelines where generative AI can leverage data catalogs and metadata to autonomously move data around an organisation.
- building rules for data quality, which is a process that normally takes weeks but is now reduced to minutes with auto rule building.
- data classification that can literally take months or even years to complete is reduced to days. This has a huge positive impact on data privacy and governance due to the speed, scale, and completeness. Because the AI-powered technology can now go across every data set in an organisation and classify data.
- associating business terms to data assets to improve data literacy, which helps overcome what is often a Top 3 data issue for banks.
Using generative-AI to engage with banking customers
The loyalty of Australian banking customers is under threat. Australia’s interest rates have hit an 11 year high due to inflationary pressures. Combined with rising business costs and cost of living pressures, this is impacting how customers feel about their bank.
To retain and grow customer accounts, a bigger focus must be placed on ease of doing business with the bank. With more digital-first customers, there is now a push for Australian banks to adopt new business models that embrace generative-AI. That is to achieve more personalised customer engagement.
The holy grail is to gain a single 360-degree view of each customer. Highly targeted customer engagement is an opportunity that banks can easily take advantage of, once they can get their customer data out of the siloes. Generative-AI has a major role to play in delivering a full view of the customer.
Fit for business data is a critical requirement for generative-AI in banking
To achieve a complete view of the banking customer, data must be ‘fit for business use’. This means that it should be:
- understood by all users
The cost of not having data that is fit for business use is steep: customer attrition. For example, 15 customers out of every 100 will leave their financial institution annually.
This is why banks are now spending more in five key areas.
- Modernising applications. Such as implementing an intelligent, single end-to-end platform that unifies clean, trustworthy data from customer facing systems such as websites and mobile applications to internal systems like customer servicing, sales, and marketing.
- Operationalising AI and machine learning in areas such as ChatGPT. Enterprise-grade, generative-AI powered data management platforms such as CLAIRE GPT, let organisations interact with their data assets in natural language, and reduces a data user’s time spent on key data management tasks by up to 80%.
- Open banking APIs. Bank CTOs and CIOs recognise the need for more data sharing in order for open banking to meet changing market and regulatory demands.
- Data literacy that enables executives and employees who are day-to-day users to have the knowledge they require to truly lead and support a data-driven organisation while ensuring critical data privacy, security and governance.
- Centralised and controlled ESG reporting solutions such as the Intelligent Data Management Cloud for ESG Sustainability that enables them to be transparent and accountable in their ESG reporting.
Australian banks continue to be held back by the widespread use of legacy data management tools. Many of which are more than a decade old. A lack of data literacy among banking executives and employees; and the proliferation of sensitive data growing every day.
This creates a perfect storm where there is limited capacity to get data out and match it across systems, and to people, when and where it’s needed.
As a result, data sprawl is becoming a real challenge. If governance is not enforced to address these issues, banks can end up replicating the same data problems over and over again, and exacerbate the problem.
The solution lies in technology agnostic cloud-native data management solutions that can integrate seamlessly with existing bank systems and applications, scale in accordance with data fluctuations and changes in the banking environment, and provide secure data sharing so that the right people have access to the right data when and where they need it.
That is the magic bullet that Australian banks need to get data out of siloes and make it useable and actionable in ways. It will drive better business decisions, mitigate risk, and enhance customer experiences. In return, we will reap the benefits of a more stable and resilient banking environment for the future.