This is a sponsored post, by Michael Hom, Head of Financial Services Solutions, InterSystems. InterSystems are Gold Sponsors of the upcoming FinovateEurope in London, March 22-23.
Last year was a record breaking for the global fintech sector, with investment reaching $102 billion – an annual increase of 183%. This growth was in large part spurred on by the pandemic which brought about major changes in consumer banking and spending habits, with eight in 10 people in the U.K. alone now using fintech products for banking and payments. At the same time, demand for fintech is also growing due to increased digitization among incumbent banks as these institutions try to keep pace with evolving customer demand for digital services and applications.
However, despite this growth, fintechs, much like more traditional financial services institutions, face a range of technical challenges which if not addressed could stall their progress. This was evidenced in recent research from InterSystems, which found that a staggering 81% of fintechs globally see data issues as their biggest technical challenge. Therefore, with data vital to everything from making informed decisions to delivering personalized services, addressing these challenges needs to be a priority for fintechs if they are to sustain the momentum of 2021.
The implications of fintechs’ data struggles
The data challenges being faced by fintechs fall under two distinct issues. Firstly, 41% of fintechs globally say they are unable to leverage data for analytics, machine learning (ML), and artificial intelligence (AI), while 40% of fintechs experience difficulties in connecting to customers’ applications and data systems. This indicates that not only are fintechs often unable to use their data effectively, but also they are struggling with data silos and integration.
These issues can have implications for fintechs such as hindering their ability to make informed decisions about the types of products and services they should be offering customers, and how they can continue to innovate to meet evolving customer needs. Additionally, for B2B fintechs in particular, integration challenges will make it more difficult to sell their applications to enterprise customers who need solutions that fit seamlessly within their existing infrastructure and that allow them to obtain the much-needed flow of bidirectional data.
On top of this, the data challenges cited by fintechs could hinder their ability to comply with financial regulations. Not only is this a concern from a regulatory standpoint, but it also may put the 93% of fintechs that hope to unlock the opportunities of partnering with incumbent banks at a disadvantage. After all, security and regulatory compliance are essential for banks and are key considerations when making decisions about which fintechs and firms to work with.
Time for a change of data architecture
Consequently, to build on the growth they have experienced over the last year and to be in the best position to capitalise on lucrative relationships with incumbent banks, fintechs globally must begin to address the problems with their data management. The starting point must be to find a way to bridge data silos and make integration easier.
Within the wider financial services sector, traditional firms, such as JPMorgan, Citi, and Goldman Sachs, are turning to data fabrics to solve these data challenges and provide a consistent, accurate, real-time view of data assets. A new architectural approach, data fabrics access, transform, and harmonize data from multiple sources on demand. By weaving together different data sets, from both within and outside the organization, and providing easy and uniform access to data, a smart data fabric can help fintechs to generate insights that can be used to get to know their customers better and gain complete visibility to accelerate business innovation.
This type of data architecture will also allow fintechs to create a bidirectional gateway between their applications and their enterprise customers’ production applications, legacy systems, and data silos. This approach will help those fintechs to ensure that their solutions can be quickly and easily integrated within their customers’ existing environments, which is particularly beneficial for fintechs looking to collaborate with banks.
‘Smart’ or enterprise data fabrics elevate this approach further by embedding a wide range of analytics capabilities, including data exploration, business intelligence, natural language processing, and ML directly within the fabric. This makes it faster and easier for organizations to gain new insights and power intelligent predictive and prescriptive services and applications.
As such, smart data fabrics address both the data integration challenges facing fintechs and their currently inability to use data with more advanced technologies such as AI and ML to extract valuable insights. As smart data fabrics allow existing legacy applications and data to remain in place, thereby removing the need to “rip-and-replace” any of their existing technology, this approach also enables fintechs to maximize their previous technology investments.
With so much potential within the global fintech sector, implementing a smart data fabric will allow fintechs to address their most pressing data challenges. They will have the ability to make more informed decisions based on accurate information and insights, deliver the products and services their customers need, and collaborate with other institutions. Ultimately, this will ensure fintechs are in the best possible position to make 2022 an even more successful year than the last.
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