Building a Decision Intelligence platform for financial services sounds straightforward until you’re actually doing it. Provenir CPO David Mirfield joined Helen Yu on CxO Spice (Episode 133) to get into the specifics: the architectural decisions, the roadmap trade-offs, and the hard-won lessons from two decades of working with banks, fintechs, and everyone in between.

Here are the key insights from their conversation.

Financial services organizations have spent years assembling point solutions for credit risk, fraud, onboarding, and customer management. The result is fragmented data, duplicated logic, and decisions made in silos that don’t reflect how risk actually moves across the customer journey.

David’s take on why that’s such a persistent problem:

– David Mirfield | CPO, Provenir

That’s the real challenge of building a unified platform: it’s organizational as much as it’s technical. Customers can run separate teams on one platform for legitimate regulatory or logistic reasons and still get the benefit of shared data and shared logic.

And that logic overlaps more than most people realize. Credit and fraud share roughly 90% of the same data and strategic considerations. Building separate capabilities for each means solving the same problem twice and introducing blind spots at the seams.

The platform also serves many different users simultaneously:

Provenir’s approach is to maintain genuine technical depth while progressively building toward low-code and no-code interfaces, working up from a strong foundation rather than stripping the platform down.

This was one of the most quotable moments in the conversation, and Helen said she was stealing it:

– Helen Yu | CEO, Tigon Advisory Corp

Provenir hasn’t built a dedicated fraud product or a dedicated credit product. It’s built an engine flexible enough to serve both, and everything in between, without constraining how customers configure it. The platform’s breadth is a feature, not a lack of focus.

The same thinking applies to AI. The pace at which foundation model providers are moving makes it strategically unwise to commit to any single LLM or agentic framework.

Provenir’s response is to be the orchestration layer, not the AI itself. That means staying agnostic across LLMs, agentic capabilities, and frameworks, and adding support natively as they mature. The most recent example: MCP support, already integrated into the platform.

In regulated markets, there’s an additional reason to stay independent from any specific AI provider. Explainability and transparency aren’t optional. Being able to show a regulator exactly why a decision was made, and how the data supported it, matters as much as the decision itself.

If there’s one area where Provenir has built a durable competitive advantage, David pointed squarely at data. And he made the point with some feeling:

Provenir built that clean way of doing it long before David joined the business, and the flexible adapter infrastructure that came from it remains one of its clearest differentiators. The 225+ pre-integrated data sources in the marketplace are part of the story. The more important capability is that customers can build their own integrations directly within the platform, to internal databases, RESTful APIs, LLMs, and agentic services, through a low-code UI, without needing an engineering sprint.

The product decision David flagged as one of the hardest: choosing to stop building new marketplace integrations at scale, because there are higher-priority areas on the roadmap. Knowing when to stop adding and start deepening is genuinely hard, and it doesn’t happen without a clear point of view on what the platform is for.

Most institutions know that real-time decisioning is where they’re headed. Most are still running monthly or weekly batch processes because that’s what their core systems support. Provenir’s position is to bridge that transition rather than force it.

The same decisioning engine handles batch and real-time processing, with a single UI and a single configuration layer. A customer can go live on batch and switch to real time when they’re ready, without rebuilding anything. David illustrated why that matters in practice:

For use cases that don’t require external data calls at all, the engine handles 10,000 transactions per second at enterprise scale. The underlying principle across all of it: improvements to the core engine benefit every use case built on top of it, simultaneously.

Two areas are getting the most product development attention through H1 and into H2 this year.

The first is Decision Intelligence. Provenir recently launched a simulation module that lets users compare production data against historical performance before making a change. Coming next are proactive recommendations, where the platform surfaces areas within a customer’s decisioning flow that could be improved, using data and models the customer already has.

That moves the platform from answering questions to generating insight before anyone thinks to ask. Agentic interfaces make those recommendations easy to explore interactively; automated machine learning provides the statistical rigour underneath.

The second area is continued enterprise depth: regulatory controls, security, data protection, and the governance infrastructure that large tier-one banks require before trusting a platform with their most sensitive decisioning workflows. The goal, as David put it, is to be the safe pair of hands that is also the most innovative engine in the room.

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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.

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.

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.

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.

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.