The Generational Shift:
Why Banks Are Replacing Their Decisioning Infrastructure
Financial institutions are ripping out decisioning infrastructure they spent two decades building. This isn’t a routine technology refresh. Banks are replacing entire systems because the architecture that powered the last generation can’t support what the market now demands.
Here’s what I see from working with major banks on this transformation: the technology decision is actually the easy part. The harder question is whether the organization is ready to use what becomes possible. At a recent AI conference in London, the dominant theme wasn’t about technology capabilities but organizational readiness.
The story of how we got here explains why this organizational challenge is so acute. Twenty years ago, banks moved from monolithic mainframes to commercial decisioning applications. The promise was flexibility and lower maintenance costs. What emerged instead was fragmentation. Today’s typical bank runs separate systems for credit, fraud, compliance, onboarding, and collections. Each line of business and geography has its own stack. This siloed architecture creates two critical problems: it delivers poor customer experiences, and it makes real AI impossible.
At Provenir, we work with tier one banks around the world, and we see firsthand which institutions move quickly and which get paralyzed by complexity. This article examines why re-platforming is happening now, what truly differentiates AI-capable infrastructure, and the timeline institutions can expect for transformation.
- Why Digital Disruptors Force the Issue
Ten years ago, Revolut raised a $2.3 million seed round. Today, they serve 65 million customers across 48 countries and hold a $75 billion valuation. Companies like Monzo, Klarna, and Stripe followed similar trajectories, resetting customer expectations for financial services entirely.
Customers now expect instant approvals, personalized offers, and seamless experiences across every touchpoint. Traditional banks lose market share because their infrastructure can’t deliver this. The technology that worked for batch processing and overnight decisions can’t support the always-on, contextually aware experiences that digital natives established as baseline.
- The AI Imperative:Why Siloed Systems Fail
AI requires two things that fragmented architectures fundamentally can’t provide: a unified view of the customer and the ability to act on insights instantly across any touchpoint.
Let me be specific about what a unified customer view actually means. Take a customer applying for a loan. You need to orchestrate their credit card transaction history, bank account behavior, biometric verification, external data signals about email validity and device fingerprinting, behavioral patterns across channels. One system might know their credit history. Another tracks fraud signals. A third manages compliance data. If these never converge into a single profile, AI has nothing comprehensive to analyze.
This is why profiling needs Machine Learning at its core. You can’t just pull data from various sources and stack it together. You need to apply analytics to networked, contextual information. A suspicious transaction pattern means something entirely different when connected to a recently created email address and a high-risk merchant code. Disconnected systems miss these connections entirely.
There’s a massive gap between running AI pilot projects and operationalizing AI at enterprise scale. Banks experiment with AI in isolated use cases all the time. Embedding these capabilities across the entire organization is fundamentally different. It requires infrastructure designed for AI from the ground up.
- Native AI Architecture vs. Bolted-On Capabilities
Moving from fragmented applications to AI-capable infrastructure requires understanding what platform architecture actually means. I’ll use a concrete analogy. Adding AI to legacy systems is like retrofitting solar panels onto a house that wasn’t designed for them. You can make it work. But you’ll have cables running down the outside of the building, connections that require extensive modifications, and an outcome that’s never as efficient as if you’d designed the house holistically from the start.
We’ve seen competitors try to build separate AI engines because it’s too difficult to evolve their existing technology. Then they attempt to connect these disparate pieces. The integration is awkward. The outcomes are less accurate. The results are harder to explain and audit. When AI capabilities are embedded natively, the entire system is engineered to make those capabilities effective. Data orchestration, model deployment, execution, and monitoring all work together seamlessly.
Speed matters enormously here. Traditional data science teams might spend months manually building and deploying a credit risk model. With a Decision Intelligence platform, you can spin up challenger models in minutes. The system can automatically generate alternatives, simulate their performance against historical data, compare results, and deploy the best option immediately.
- Agents:The Next Evolution in Decisioning
The future of AI decisioning involves autonomous agents, and platform architecture determines whether you can deploy them effectively. There are two distinct ways agents transform how institutions operate.
First, platforms can embed agents directly into workflows. During customer onboarding, an agent might recognize that additional information is needed and interact with the customer to collect it, then feed that data back into the process. The agent handles the dynamic, conversational piece while the decisioning platform orchestrates the broader workflow.
Second, and this is where it gets interesting, you can wrap decisioning workflows themselves into agents. Instead of predefined sequences where we tell the system exactly what data to call and which models to execute in what order, agents can make intelligent choices. Maybe the agent determines it doesn’t need to call all the data sources we thought were necessary. Maybe it doesn’t need to fire every model to reach a confident decision. This creates efficiency gains through reduced computing costs and intelligence gains through dynamic learning.
Think about the implications. An agent adapts its approach based on what it observes rather than following a static rulebook. Organizations that can deploy agents across credit, fraud, compliance, and customer management will operate with speed and intelligence that static workflows simply can’t match.
What Actually Changes
The transformation delivers measurable outcomes. Processing time moves from hours to milliseconds. This enables instant experiences that weren’t previously possible. Quality improves dramatically because institutions gain access to comprehensive customer profiles rather than making choices based on incomplete data.
The business impact shows up as profitable growth combined with reduced losses. Better decisions mean approving more good customers while declining more risky ones. Institutions can expand their customer base without proportionally increasing credit or fraud losses. This is the outcome that gets C-suite attention.
The Re-Platforming Challenge No One Talks About
Here’s what can be frustrating about most re-platforming initiatives. Banks want to take all the rules they’ve had, all the models they’ve built, and simply replicate them on a new, more modern system. They’ve upgraded the quality but have missed the opportunity to reimagine what’s possible.
We see this nine times out of ten. Banks want to start with what they know, even if what they know was designed for a different era with different constraints. Eventually, once they’re comfortable with the new system, they’ll try new approaches. But why not use the transition as the moment to rethink how you want to manage customers in a modern way?
The resistance we encounter falls into three categories. First, it’s genuinely difficult. Re-platforming is another project to organize and orchestrate. Banks have existing roadmaps and limited bandwidth. Second, there are upfront costs. You need technical teams to disconnect legacy systems and implement new infrastructure. Some institutions don’t have the capital or resources available right now, even if the long-term economics are compelling. Third, organizational AI maturity varies enormously. If an institution doesn’t deeply understand AI yet, they may be nervous about re-platforming until they’re convinced the new platform is transparent, auditable, and meets their requirements.
The Timeline Reality
When institutions commit to transformation, we see sales cycles ranging from four months to two years. The variance depends on whether they need to build internal consensus, run proof of value exercises, or work through procurement complexity. The implementation itself takes months, not years, but organizational readiness takes longer.
Here’s the irony about investment: moving to cloud-native platforms typically saves money. Institutions spending millions annually on on-premise licenses and infrastructure can often reduce total cost of ownership significantly. The platform provider handles infrastructure, scaling, and maintenance. The upfront investment is about organizational change and implementation services, not ongoing license costs that exceed what modern platforms charge.
Moving Forward
The third generational shift in financial services technology is underway. Organizations that treat this as a technology upgrade will miss the point. Success requires treating this as a strategic imperative that determines whether you can compete in the next decade. It requires organizational readiness alongside technical capability. It requires willingness to reimagine processes rather than simply replicating them on better infrastructure.
The institutions that move decisively to unified, AI-capable platforms will define what competitive advantage looks like in financial services. Those that hesitate will find themselves competing against organizations operating with fundamentally superior capabilities. The choice is whether your institution will lead or follow.
The Generational Shi...
