Across the ever-evolving financial services landscape, banks are exploring how they can use AI technology to increase productivity, drive efficiencies and deepen customer relationships. Many banks are currently grappling with a pivotal decision - whether to invest in developing AI capabilities in-house or partner with external suppliers.
There are range of concerns with the use of AI in banking, including risk management, data security, privacy, bias, misinformation and lack of transparency. These concerns have led to some banks developing their own AI models and products in-house, which may offer greater control and the ability to tailor the approach to the bank's unique needs, however it is not without significant challenges. There are myriad hurdles when banks embark on developing and rolling out new AI based software in a compliant and cost-effective way.
AI technology relies on the ability to capture, manage and maintain accurate, robust data on a secure platform. Without a clear, future focused strategy for AI technology rollout across large and complex organisations, many banks have been unable to transition from experimentation of specific use cases to implementation at scale. It is a deeply complex and challenging process to firstly prove the technology works in production, but to then deploy software that works in real world scenarios and delivers tangible results. There have been countless cases of technology projects falling short and failing to achieve return on investment.
Banks and other financial institutions are renowned for building extravagantly costly and inflexible technology, and they often struggle to overcome the problem of siloed legacy IT systems, fragmented data sets and antiquated operating models. When dealing with outdated technology it is very challenging for banks to integrate old data sets with new systems and seamlessly connect these into a wider ecosystem.
The cost and complexity of these projects tend to favour larger banks with significantly higher resources required to invest in research and development, with most AI products exceeding budget and underdelivering on customer experience.
Finding and attracting talent is also part of the challenge for banks looking to keep AI development in-house. Data science has recently ranked as one of the topmost in-demand jobs in Australia, so competition to recruit the right talent is fierce.
The capability of expert AI external suppliers to deliver leading edge technology with the scale and pace required to achieve transformational change often far exceeds what banks could achieve with in-house resources at a fraction of the cost.
Specialist AI firms are constantly evolving data-driven technology through ongoing innovation, investment in research and development and refining AI models to push boundaries to maintain a competitive edge. These firms are adept at pivoting quickly to diversify products to address emerging market needs. They also attract the brightest minds in the industry by providing opportunities to be at coalface of driving the development of AI software from inception to deployment.
Many of these ‘business-ready’ products can unlock significant value in shorter timeframes which enables banks to hit targets quickly. Software designed by specialist firms can deliver real-time, scalable, automate outputs, which can have a profound impact on the bottom line within a matter of months.
Many examples in the Customer Owned Banking sector demonstrate the success of partnering with expert firms to outsource software development. A wide range of banks have engaged AI specialists to provide leading capabilities, facilitating swift and cost-effective outcomes for both customers and frontline teams. These strategic partnerships have enabled them to outpace many of their larger competitors by improving efficiency and meeting the evolving needs of customers.
The opportunity in banking is undeniably linked with the unlocking of AI's extensive potential, and those who successfully navigate this path will unlock significant value, while solving the productivity conundrum.
Josh Shipman is the co-founder and co-CEO of Elula