By Raj Mittal, Founder and Principal of Neural International Partners LLC; Part-Time Lecturer, ERM Program, School of Professional Studies
Despite decades of investment in digital infrastructure, many banks still grapple with slow product cycles, fragile systems, and increasing regulatory scrutiny of risks related to their technology infrastructure. Technology is now routinely listed among the top enterprise risks in the financial sector. But senior executives often misdiagnose the problem.
Yes, core systems are aging. Yes, mergers have left behind duplicative, incompatible platforms. Yes, cyber threats and outages are rising. But these are symptoms—not causes.
The deeper issue is cultural: banks still treat technology as a cost center rather than a core strategic driver. Until that mindset changes, no amount of investment will deliver the desired results. This is critical, especially now, with AI set to dramatically widen the performance gap between digital-native firms and legacy institutions.
Banks Have a Technology Problem—But Not the One You Think
Banking has a proud history of technological innovation. From ATMs to online banking to electronic trading, financial services were once at the cutting edge. Pioneers like Citibank’s Walter Wriston and JPMorgan Chase’s Jamie Dimon understood that the real competition wasn’t other banks—it was technology firms.
Yet most banks fell into a reactive stance. To keep pace with customer expectations and regulatory mandates, they piled on workarounds and integrations. Over time, this created brittle, fragmented systems—too complex to scale, too fragile to fix. Post-financial crisis mergers only worsened the tangle, as integration focused on business lines while legacy technology was left in place.
The result is a structural fragility that regulators are increasingly unwilling to tolerate. High-profile failures, such as Citibank’s $700 million mistaken loan transfer or persistent system outages, have triggered fines, consent orders, and board-level scrutiny.
Culture Is Holding Back Technology
Bank culture still sees technology as “back office”—a function to support business priorities, not shape them. Even as digital channels dominate customer interaction, core platforms remain siloed and antiquated. Many banks have modern interfaces layered on systems that pre-date the internet.
This duality is unsustainable. Customers expect seamless, data-driven experiences. Competitors like Stripe or Square are redefining what’s possible in real time. Banks may want to act like tech companies, but few are willing to truly become one.
Part of the problem is leadership. Most bank executives lack backgrounds in product or engineering. Their limited understanding of systems design and software delivery often leads to underinvestment, poor oversight, and unrealistic expectations. Technology leaders, meanwhile, are often buried a few layers down, without clear budget authority or board visibility.
How Technology Departments Perpetuate the Problem
Paradoxically, the very departments tasked with fixing the problem often perpetuate it. Over time, bank IT organizations have grown in size, budget, and influence—but many still prioritize maintenance over modernization.
Several dynamics are at play:
- Complexity protects incumbents. Fragile systems are hard to replace, which gives technology teams job security and leverage.
- Bright talent stays away. Top engineers avoid working on tangled legacy systems inside risk-averse bureaucracies. CIO roles are often filled by generalists, not hands-on technologists who can architect change.
- Incentives are misaligned. Performance metrics rarely reward simplification or system retirement—only delivery of new features.
The result is a vicious cycle: complexity leads to fragility, which leads to fear of change, which leads to even more complexity.
AI Will Widen the Divide
Artificial intelligence will not save banks from this trap—it will expose it.
While many institutions are experimenting with AI-powered chatbots, fraud detection, or risk modeling, few are ready for the next phase: AI-driven automation of core workflows and hyper-personalized financial services.
AI success requires unified, high-quality data. Yet most banks have data scattered across siloed, incompatible systems, many built decades ago. Without resolving this foundational issue, AI models will produce shallow insights or simply fail to deploy.
Talent is another constraint. Building AI systems requires engineers, data scientists, and product designers who thrive in fast-moving, empowered teams.
Unless banks invest in foundational change, AI will accelerate the divergence between tech-native firms and traditional players. The winners will be those who’ve already laid the groundwork: modular systems, clean data architecture, empowered teams.
What Bank Leaders Must Do Differently
Fixing the culture around technology is not a technical exercise—it’s a leadership one. Here’s where banks must focus:
- Elevate Technology to the Core of Strategy: Treat technology as a revenue enabler, not a support function. Tech leaders should report directly to the CEO and have a voice at the board level.
- Rewire Governance and Incentives: Align incentives around system simplification and stability—not just feature delivery. Reward teams for retiring old platforms, reducing manual interventions, and improving system resilience.
- Restructure for Product Thinking: Dissolve the business-tech divide. Create cross-functional product teams with shared ownership, budgets, and accountability for outcomes.
- Invest in Talent and Leadership: Recruit experienced engineers and architects into senior roles. Pay what it takes to compete for tech talent. Embed technology fluency into executive development.
- Prioritize Platform Modernization: Make core system renewal a board-level priority. Break down the roadmap into modular, achievable steps—but keep the long-term commitment visible.
- Communicate with Investors and Regulators: Be transparent about the modernization journey. Set realistic timelines and KPIs. Reframe technology investment as risk reduction and strategic positioning—not just expense.
The Bottom Line
Banks need to transform the thinking that made these problems inevitable.
Without a shift in culture, leadership, and incentives, even the best AI strategies and cloud migrations will fail. But for institutions willing to lead this change—not just fund it—the payoff is substantial: faster innovation, lower risk, and a future-ready organization.
Raj Mittal is the founder and managing partner of Neural International Partners, a consulting firm specializing in risk advisory and training, and a senior advisor to fintech and consulting organizations. A former Managing Director at Citi, he held leadership roles in business and risk management. He is also a part-time lecturer in Columbia’s Enterprise Risk Management program, where he has taught courses on Traditional ERM Practices, Strategic Risk Management, and Operational Risk Management.
Views and opinions expressed here are those of the authors, and do not necessarily reflect the official position of Columbia School of Professional Studies or Columbia University.
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The Master of Science in Enterprise Risk Management (ERM) program at Columbia University prepares graduates to inform better risk-reward decisions by providing a complete, robust, and integrated picture of both upside and downside volatility across an entire enterprise. For both the full-time and part-time options, students may take all their courses on Columbia’s New York City campus or choose the synchronous online class experience.
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