Banks are eager to modernize with AI and other new technologies, but outdated legacy systems and technical debt are holding them back. These issues make operations fragile, increase the risk of errors, and can massively hinder digital transformation efforts.
To stay competitive and meet customer expectations, banks need to extract valuable data from legacy systems and invest in modern technology that enables them to retire outdated infrastructure. Addressing technical debt proactively is essential to maintaining innovation and business growth.
What is technical debt?
Technical debt refers to the accumulated cost of suboptimal technology decisions to hit short-term goals, which later lead to extra work or rework. While it affects all industries, it poses a particular challenge for banking, where reliability, precision, and customer trust are critical. Technical debt can arise from legacy systems, quick fixes or workarounds, fragmented data or process and a general underinvestment in modernization.
Technical debt comes at a price
Technical debt carries a significant financial cost, both directly and indirectly. Inefficient legacy systems can lead to lost customers, frustrated employees, and lengthy, expensive transformation projects, with these inefficiencies draining resources and hindering progress year after year.
Banks operate in highly regulated environments, meaning any errors caused by outdated systems or rushed fixes can result in compliance breaches, fines, or legal consequences, amplifying both the financial and reputational risks of technical debt.
As an important step in tackling technical debt, banks must clearly communicate the financial impact of technical debt across all levels of the organization, ensuring everyone understands its importance and is committed to reducing it. This can help define areas of focus and ultimately the business case for change.
Leading from the top
Speaking of communication, leaders also need to be vocal about the ways in which they are tackling and prioritizing technical debt and lead by example.
Employees are often too busy managing outdated systems to focus on fixing them, especially if past transformation efforts have failed or leadership doesn’t prioritize the issue. As an example, outdated software may struggle to integrate modern AI-driven fraud detection systems, forcing employees to manually review more suspicious transactions than needed, increasing error risk and slowing response times, and leading to frustration and disengagement with staff and customers alike.
To counter this, banking leaders must actively champion optimization, listen to staff feedback, and allocate time for teams to modernize systems. Clearly explaining why certain debts are prioritized and sharing progress updates fosters shared ownership, boosts motivation, and drives a more efficient, collaborative culture.
Customer expectations are only increasing
As well as setting employees up for success, banks have to prioritize the customer experience. Modern banking customers expect nothing less than seamless digital experiences in their banking interactions. When technical debt slows innovation or causes service disruptions, it can significantly erode customer trust.
Without trust, they may then hesitate to borrow, invest, or engage with new banking products, worry their data isn’t secure or simply end up moving to a competitor they deem more trustworthy or reliable. With this, it’s crucial for banks to optimize operations in line with what their customers expect and deserve, and ensure anything getting in the way of this is dealt with as a priority.
Most importantly, reducing technical debt should be integrated into regular development cycles as a strategic, enterprise-wide, and ongoing priority, rather than something done on an ad-hoc basis. By truly understanding the financial implications associated with legacy systems, ensuring communication across the team and prioritizing a customer-centric mindset, banks will be able to better innovate, remain resilient, and meet the expectations of both customers and employees.
So, what’s different now?
The ability to analyze and re-design legacy systems has really advanced a lot in the last 12–18 months. Much of this is in applying GenAI and AI to analyze code, videos, and old process diagrams and documentation (if you have them). In the past, without documentation, you’d need lengthy current state analysis projects. Not anymore. You can upload a video of a system walkthrough or some code and have it analyzed, have documentation created, and then generate a new process workflow. The difference now is the reduced time frame to get analysis done in minutes and hours vs. days and weeks. And in what feels like a back to the future moment, carefully selecting the right solution provider, systems integrator, and hyperscaler to get it all done.