Data is seen as the biggest technical challenge for 81% of fintechs worldwide, hindering their ability to enhance services and adopt next-gen technologies according to research commissioned by InterSystems.
The survey polled 500 senior decision-makers at fintechs across 12 countries, including the UK and Ireland, North and South America, and Australia and Southeast Asia.
It found that the data challenges encountered were split between leveraging data for analytics, machine learning, and artificial intelligence and connecting to customers’ applications and data / legacy systems.
Security was also noted as a significant problem for 40% of respondents while 39% mentioned cloud support / multi-cloud deployment and administration.
In terms of adopting new technologies over the next 12 months, the cloud tops the list for over half (51%), closely followed by plans to invest in data management technology (48%), artificial intelligence (AI) and machine learning (ML) (45%) and data fabric technology (42%).
However, a lack of flexibility within their current environment to integrate new technology as well as a lack of internal expertise / skills were mentioned as the main issues with integrating these new technologies.
“While the complexity of cloud and its costs are major challenges for fintechs, especially as they grow, if they take the right approach to cloud deployments, these organisations can reap significant rewards and deliver those back to their end customers,” said Mike Hom, head of financial services solutions, InterSystems.
He added, “Boosting training and knowledge, along with building their offerings on cloud-first solutions that avoid vendor lock-in and which can be implemented within a hybrid environment will allow fintechs to increase agility, scalability, and security and ensure that their solutions appeal to a wider customer base.”
Not surprisingly, the survey notes levels of investment into these technologies differ depending on the maturity of the organisation.
While the more established fintechs are focusing more on data management, cloud and data fabric initiatives, those in the early stages are more likely to prioritise investments in AI and ML.
It said this indicates that more established organisations are focused on overcoming the data issues they are facing before considering implementing new technologies like AI/ML, which rely on data.
As to the drivers, 55% pointed to customer demand / improve competitiveness, 52% want to improve scalability and reliability and 48% are hoping to increase agility. Around 47% want to enable better integration with customers and third parties.
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