By Celonis, published in Banking Dive
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The notion that the best way for banks to modernize their core systems is to rip them out and replace them with new ones is not only outdated—it’s also inefficient.
Consider a study by IBM that found 94% of banking overhauls exceed their deadlines, resulting in delays that negatively affect the project’s ROI.
Fortunately, the rise of AI means that there are other paths to transformation. In this new era, leading financial institutions are focusing less on decommissioning outdated software and more on how new technology can help them extract data and create value from existing systems. It’s a savvy move that enables organizations to leverage what they have, while still modernizing for the future.
“We’ve been so focused on moving from legacy systems to newer systems, when the real opportunity is in how we use the data and improve the process,” says Jaymini Hirani, Financial Services Lead at Celonis.
Data is everywhere, but where’s the intelligence?
Modern banks rely on hundreds of systems and applications. For example, a single payment may come in contact with 200 different systems as it traverses from initiation and authorization to clearing and settlement.
Some of those systems represent the latest technology. But others are likely legacy software, core banking systems, or even Excel spreadsheets managed by specific employees. As a result, the related data is siloed within systems and applications, making it difficult to access.
In this environment, interactions and handoffs between teams become increasingly complex, and the risk of errors increases, resulting in delays and manual workarounds. The fragmented nature of the data also limits transparency, which can impede productivity and, more importantly, lead to regulatory concerns.