One pattern comes up repeatedly when talking to lenders about their decisioning infrastructure: the gap between what they thought they were buying and what they’re actually able to do.

A platform that promises end-to-end capability across scoring, orchestration, and decisioning often comes with constraints that only become visible when you try to move quickly. Adding a new data provider requires a professional services engagement. Updating business logic means opening a ticket and waiting for the next release window. These aren’t edge cases. They’re the standard experience for a significant portion of the market.

Understanding what that dependency actually costs, in concrete terms, is a useful starting point for evaluating your current setup.

The lenders getting the most out of their decisioning programs tend to share a few operational characteristics.

Their teams own their data strategy. Credit and fraud analysts can onboard a new data provider, alternative data signal, or open banking feed without routing through vendor product roadmaps or waiting on integration queues. This matters especially when your platform provider also competes in the data space, where incentives around what gets prioritized can become complicated.

Their strategy teams control their decisioning logic. Changes to business object flows, score cutoffs, and segmentation are made by the people closest to the problem, on the timelines the business requires. When analysts need to route every change through engineering or external professional services, the speed of iteration suffers. In credit risk and fraud, iteration speed is a meaningful competitive variable.

Their platform covers the full customer lifecycle. Acquisition, account management, and collections are often managed as separate problems with separate tools. The downstream cost is fragmented data, inconsistent decisioning, and margin leakage that’s difficult to attribute. A single platform architecture means insights from origination can inform account management strategy, which can inform early intervention in collections. That continuity has real value.

These constraints are easier to evaluate when you put numbers to them.

Consider a mid-size lender processing one million applications per year, with a 60% approval rate, a 1.5% fraud rate on approved accounts, and an average balance of $5,000. That’s roughly $45 million in annual fraud exposure.

Now suppose the fraud team has identified a new detection vendor with demonstrably better signals. The business case is solid. But the current platform requires a vendor engagement to onboard a new data provider, putting the integration six months out.

A 2% improvement in fraud detection on a $45 million exposure base is worth $900,000 in recoverable losses annually. A six-month delay means $450,000 of that goes unrealized, before anyone has touched a strategy rule. Across multiple use cases and multiple cycles, the cumulative figure grows quickly.

This is why vendor dependency tends to function as a hidden operational cost. It doesn’t appear as a line item, but it shows up in fraud rates that didn’t move, approval rates that didn’t improve, and strategy cycles that ran a quarter behind.

The same dynamic applies on the revenue side, particularly for lenders looking to expand access to credit responsibly.

Using the same lender profile: 400,000 applicants are declined annually. A meaningful share of them are creditworthy but invisible to a bureau-only model. Alternative credit data such as cash flow signals, income volatility, and rent and utility payment history can surface thin-file and credit-invisible consumers that conventional scoring misses.

A conservative 1% incremental approval rate translates to 10,000 additional approved accounts, $50 million in incremental balances, and approximately $6 million in gross revenue at a 12% net yield. Accounting for the incremental risk at a 4% loss rate on the near-prime book versus a 1.5% core rate, the net revenue figure comes to around $4 million annually.

If integrating that data source takes six months because the platform requires a vendor engagement, $2 million in net revenue is deferred before the strategy team has made a single decision. That’s the cost of one integration delay, on one data source, in one cycle.

The lenders closing the financial inclusion gap, or improving fraud performance at scale, aren’t necessarily working with better data than everyone else. They’ve built or selected infrastructure that lets them act on good data when they find it.

Platform flexibility is worth evaluating on a few specific dimensions: how quickly can your team onboard a new data source independently? How much of your decisioning logic can analysts update without engineering involvement? How consistent is your data and decisioning architecture across acquisition, account management, and collections?

These aren’t abstract architectural questions. The answers have direct financial implications, measured in fraud losses, incremental revenue, and the compounding effect of faster iteration over time.

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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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