In fintech, as with any industry, AI can help transform time-intensive and costly processes into something slicker, smarter and more sustainable. The key is to invest in AI early on – this enables people to develop as the company grows, creating upskilling and professional development opportunities for everyone. AI can reduce unnecessary tasks, but it can also reinforce and bolster existing roles, thereby empowering intelligent scaling alongside a highly AI-proficient workforce.
Shachar Bialick
Founder & CEO, Curve
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There is no doubt scale efficiencies can be achieved from GenAI within any organisation, FinTech or otherwise but there needs to be a number of foundations steps taken before you are in a position to see or benefit from those efficiencies sought.
At Swoop, we made the mistake of getting GenAI in too quickly into the product without properly thinking about how it works within a customer workflow or that most customers aren’t starting from a position of normality when engaging with GenAI. While seeing the immediate strength of GenAI; by not laying the correct platform to build on the core API and training the model with our data and spending time on product design and research, we set up ourselves up for poor adoption and minimal efficiencies gain.
However, the lesson has been learned and what we can see from taking the time to work more closely with off the shelf GenAI and training it with our own dataset of 200k SMB accounts and 100k+ funding applications, we’ve started to make big efficiency gains in how our data base is growing and the accuracy and depth of of its data layer. Our next challenge is to crack user adoption to help them achieve their outcomes with GenAI as an enabler. We’re super energised as an organisation to crack into this challenge as having set up Foundation 2.0 – we’ve definitely got our eyes set on the medium term efficiency gains and stellar product our customers will enjoy.
Ciaran Burke
COO & Co-Founder, Swoop
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Most start ups I know welcome scale efficiencies with open arms. Doing more with less people is always the preference. What becomes a key point here though is that the skill level of those people in the business must be high. You have to have employees capable of using new technologies effectively and give the ownership to these people to move at pace. With higher skill level comes higher salary, so from a cost efficiencies perspective 50% reduction in headcount won’t necessarily mean 50% reduction in staff cost, more likely closer to 30%. Which is still material to any business.
George Dunning
Co‑founder & COO, Bud Financial
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I recently came across a study that found generative AI can increase productivity by up to 66% when it comes to performing specific tasks. This is yet another demonstration that we’re interacting with technology of massive potential and that not a single business can afford to ignore it.
In my observation, fintech is at the forefront of this transformation. The vast majority of founders I’m aware of are investing substantial resources to identify business cases where AI can be applied. At Moonfare, we see tremendous opportunities in using AI to hyper-personalise user experience, automate operations and improve fraud detection.
Business leaders — in fintech and beyond — also agree that scaling AI-enabled solutions across the organisation can produce extraordinary results, hardly achievable by any other technology of the same maturity. McKinsey and Accenture research, for example, shows that strategic investments into AI generate three to four times higher returns, originating from things like improved customer satisfaction, workforce productivity and asset utilisation.
If done right, the benefits start to compound after companies acquire enough domain strength. In essence, the more AI is used, the more it improves itself. It starts to self-optimise, potentially requiring less manual intervention as it handles larger, more complex tasks with greater precision. From this point of view, Klarna’s bet on its AI chatbots makes complete sense.
However, it’s also clear that scaling AI-enabled solutions on a level of an enterprise is exceptionally difficult as it depends on a multitude of factors such as data quality, risk controls, tech architecture and talent. It’s unsurprising to me that 76% of respondents in an Accenture survey acknowledge they struggle when it comes to expanding the technology across their business.
So, how to scale AI cost-efficiently and without compromising on the quality of the services or products? Many pieces need to fall into place: securing access to high-quality data, identifying high-impact quick wins and putting the right people in the right roles are just some of the essentials. Those who can piece everything together and stay committed will be well-positioned to lead as AI continues to shape the future.
Steffen Pauls
Founder & CEO, Moonfare
