Published by Jessica Weisman-Pitts
Posted on December 8, 2022
5 min readLast updated: February 2, 2026
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By Tim FitzGerald, EMEA Financial Services Sales Manager, InterSystems
In today’s highly-competitive, rapidly evolving market , financial services organisations are under constant pressure to find ways to generate more revenue and keep ahead of the pack by developing new products and services more quickly, while all the time still relying on only their existing resources.
In recent times, this has led many financial services organisations to turn to external fintech solutions to help speed up the innovation process and rapidly attain new digital capabilities. As a result, fintech partnerships have become critical components of financial institutions’ growth strategies, rather than the technology experiments they once were.
Partnering with fintech’s gives financial services firms the opportunity to access innovation. However, many financial services firms can bear bitter witness to the fact that arduous and costly integration can see the value of such initiatives dwindle before their very eyes, and sometimes end up being lost altogether. Common challenges can range from unforeseen issues tying up IT resources, project costs spiralling out of control, and timescales sliding drastically from what was planned or what was desirable. Ultimately, these delays can result in the loss of any competitive edge as rivals take advantage and launch similar solutions much faster.
So, to ensure innovation success, it’s vital that financial services firms can quickly and easily leverage and provision new fintech services and applications by seamlessly integrating them with their existing production applications and data sources.
Getting integration right
Fintechs have become increasingly attractive over time as they typically have access to the latest technologies, modern application methodologies, and deployment platforms. But, for banks to make effective use of these cutting-edge applications and tools, those technologies need to be woven into their existing infrastructure, much of which is likely to be based on legacy technology.
Successful integration therefore requires an understanding of the intricacies and idiosyncrasies of those legacy systems. It also demands knowledge of the underlaying data architecture and how to connect the new technology to systems that weren’t built to be connected to in such a way. While this problem is far from insurmountable, getting it right will take time, resources, and budget.
Careful consideration is also required when taking on the integration to make certain that the resulting architecture does not become overly complex. After all, if it comprises of multiple technology layers from different vendors, all with different versions and releases, any future change could stymy the bank’s ability to capitalise on the benefits they set out to achieve.
The next priority will be to decide how data from existing systems is best fed into the new system and in what format. To get around this, it’s all too easy to layer extraction tools upon a plethora of other tools, including transformation tools, data lineage solutions, master data management, databases, and data lake technologies. However, firms will then be left with a multi-headed monster that nobody fully understands.
Such an approach to data integration is also, by its very nature, complex and costly to design, deploy, manage, and maintain. Fortunately, adopting a smart data fabric approach, a next generation architecture, can provide a way for financial services firms to overcome these challenges.
Bidirectional connectivity – and how to attain it
By making use of a smart data fabric, financial services firms can connect and collect real-time event data and obtain unmatched integration capabilities using a single holistic platform alone. This approach helps to eliminate the complexity and inefficiencies of manual integration and other legacy approaches, allowing firms to integrate applications faster and more efficiently. It does this for firms by essentially creating a dynamic real-time, bidirectional gateway between cloud-based fintech applications and their own production applications and data assets.
The smart data fabric integrates real-time transactional and event data, along with historical and other data from the large number of different back-end systems in use by financial services organisations today. It transforms the data into a common, harmonised format to feed cloud fintech applications on demand, thereby delivering seamless, real-time, bidirectional connectivity and integration with the firm’s existing legacy enterprise data, production applications, and data sources.
Not only does this help organisations to realise faster time to value and achieve implementations that are both simpler and easier to maintain, but it also gives institutions the agility needed to drive rapid innovation and keep critical initiatives on track. In addition to this, it helps to future proof their architecture by making it easier to incorporate any fintech applications and technologies available in the marketplace, thereby empowering them to react to new opportunities and changes in their environments.
Ultimately, there is great value to be unlocked from fintech solutions and applications. However, that is only possible through fast and simple integration. By implementing a smart data fabric-enabled data gateway, financial services organisations can make certain they are able to quickly and easily integrate new solutions within their existing infrastructure to ensure they can keep pace in a fast-evolving landscape.
Fintech, or financial technology, refers to the integration of technology into offerings by financial services companies to improve their use of financial services.
Integration in finance refers to the process of combining different financial systems or technologies to work together seamlessly, enhancing efficiency and data flow.
A smart data fabric is an architectural approach that enables seamless data integration and management across various systems, facilitating real-time data access and analytics.
Legacy systems are outdated computing systems or applications that are still in use, often posing challenges for integration with modern technologies.
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