When ‘low-code’ software arrived, it was supposed to revolutionize how banks bring projects to market. Forrester Research, an analyst firm, coined the term in 2014 for platforms designed to simplify application development. These platforms are built on tools from Microsoft and Salesforce, empowering people without technical backgrounds to create and modify products quickly and easily.
A typical promise of that era was that low-code would enable the “visual development of entire application portfolios, integrating with existing systems and delivering applications across all devices.” It was an undeniably brilliant idea. But a decade later, most serious product development in banks isn’t being done by ‘laypeople.’ Instead, banks continue to rely on thousands of technical professionals, often working alongside external consultants, to develop products.
Low-code technologies are widely used today. The 2023 Forrester Developer survey shows that 36% of financial firms use low-code for most of their development work. However, core transformation remains a significant challenge. The work required to modernize legacy systems and integrate new tools has exposed inherent limitations in low-code platforms, especially when tackling the larger banks’ more complex requirements.
That’s beginning to change. Combining artificial intelligence (AI) with existing low-code technology is poised to spark a new revolution. Banks may finally be able to reimagine customer experiences and product offerings more strategically, delivering innovation without the bottlenecks of long development cycles.
It starts with data
For any innovation, though, banks must first build a data pipeline, the system or infrastructure that facilitates the movement, transformation, and analysis of data from multiple sources to where it can be stored, processed, and utilized.
However, building a data pipeline isn’t a strategy; it’s simply a foundational step that needs to be executed. Banks can significantly reduce the time and effort required to construct these pipelines by leveraging low-code platforms powered by Gen AI. These platforms enable functionalities like improved customer offerings, optimization of household-level data, and the creation of advanced customer relationship models. The result? Better consumer experiences and increased revenue for the bank.
Once these pipelines are operational, banks can dramatically accelerate innovation. The speed at which they can trial and deploy additional capabilities on top of the pipeline is reduced by up to 60% while maintaining rigorous data standards and quality. Products that previously took over a year to introduce to the market can now be built and launched in fewer weeks. This newfound agility enables banks to be first movers when new opportunities arise, ensuring they stay ahead of competitors.
Non-technical product owners and development teams can engage in the process much earlier. With generative AI and large language models becoming integral to their daily workflows, they are better equipped to intuitively understand how to interact with and modify products. Working with real-time data sets allows them to test hypotheses and adjust almost instantaneously.
How AI can help speed up core transformation
For core modernization projects, integrating existing and legacy core banking systems remains one of the most challenging aspects of the process. Low-code platforms address this by offering robust integration capabilities through APIs and connectors.
At the same time, Gen AI further optimizes these integration processes by automating data mapping, making the integration smoother and more efficient. This combination enables seamless data exchange during modernization efforts without disrupting ongoing operations. This integration capability allows banks to gradually modernize their systems over time, reducing the operational risks often associated with core transformations.
A complete modernization of core banking systems is a costly and lengthy endeavor. Projects can last for years, even decades. It’s not uncommon for banks to spend millions in investment only to restart the entire process. The need for a better, more efficient approach is clear.
Low-code platforms help mitigate these costs by reducing the reliance on extensive custom coding and large development teams. Gen AI complements this by improving cost efficiency through automation of testing processes for new products, minimizing errors, and accelerating time to market.
Why now is the right time
As interest rates continue to decline, the competition for banks will continue to tighten. The ability to attract new customers on traditional products like high deposit rates, high-yield savings accounts, and certificates of deposit (CDs) alone will be a thing of the past – which is why traditional banks, especially outside of the top five largest banks in the U.S., need the ability to innovate quickly. However, lower rates aren’t the only reason banks need to rethink their traditional models. Consumer preferences continue to change, with younger generations demanding seamless digital experiences and tailored financial solutions.
Large banks may now be accustomed to competing with fintech companies specializing in user-friendly services like mobile payments, but they now face additional pressure from big tech players such as Apple, Amazon, and Google entering the financial services space. These tech giants are leveraging their expertise in digital ecosystems to gain market share, raising the stakes for banks to innovate and remain relevant.
The combination of these market factors shows why the benefits of a supercharged low-code technology suit are arriving at just the right time – and why we think they are ripe for rapid adoption.
