Q1. For readers less familiar with Aptitude Software, how would you describe the company and your role within it?
I’ve spent twenty years at Aptitude, working with some of the world’s most complex and highly regulated organizations, banks, insurers, telcos, building our international business and then moving to North America where I grew the business to over 50% of group revenue, before becoming CEO. Throughout that time, I’ve always stayed close to customers, and that proximity has shaped everything about how I think.
What struck me consistently, regardless of industry or geography, is that I’ve never met a finance leader who is genuinely happy with their finance architecture. That frustration is only growing because AI has arrived and exposed the problem in a way nothing else has.
Most organizations don’t have an AI problem. They have an architecture problem. Fynapse is our answer to a Finance ERP built for the AI era. And one thing I feel strongly about, real-time financial data shouldn’t be reserved for the largest enterprises. Every business, whatever its size, deserves to run on live financial insight, not last month’s report.
What makes our position unique is that Aptitude has spent more than three decades building finance-grade systems for organizations that publish numbers to boards, regulators and public markets. That heritage the controls, the auditability, the trust is built directly into Fynapse.Â
Q2. Fynapse has become a major part of Aptitude Software’s proposition. As a next-generation finance ERP platform, what attracts businesses to Fynapse?
Most CFOs don’t start out looking for a new ERP. They start frustrated. AI pilots underdelivering. Close cycles still taking two weeks. Boards demanding real-time answers while finance is working from last month’s data.
What pulls them to Fynapse is the realization that those aren’t separate problems, they’re symptoms of the same thing. The architecture under finance was built for a different era.
Fynapse is the Finance ERP built for the AI era. Real-time, event-driven, and it goes live in weeks, not years. PayByPhone processed 150 million journal lines a day and was live in six weeks. That’s not a pilot. That’s production.
Speed is the most honest signal in this market. If a vendor says modernizing finance takes 18 months, that tells you everything about the architecture they’re selling.
Q3. What problems are businesses trying to solve with platforms like Fynapse today?
Most are fixing an ERP data foundation problem they didn’t know they had until they tried to do something real with AI. Switch on an AI tool, watch it underdeliver. The real culprit is the data: batched, days late, summarized before it reaches the general ledger so the detail is already gone. 87% of CFOs say AI will be critical to finance in 2026, yet only 14% report clear ROI and only 7% have real-time transactional data flows. That gap is almost entirely an architecture problem.
But this goes far beyond fixing automation or closing the books faster. When you fix the foundation, the CFO’s entire role changes. A bank adjusts pricing while the quarter is still open. A telco spots margin leakage while revenue is still recoverable. An insurer reacts to emerging claims trends immediately rather than at month end. Finance stops explaining what happened and starts shaping what happens next. The CFO becomes a genuine co-pilot to the business.
That’s the real prize and that’s what Fynapse is built to unlock.
Q4. Why do you think Fynapse is resonating with enterprise finance teams right now?
The conversation has changed. Finance modernization used to sit on an IT roadmap with a multi-year program attached. Today it’s a board-level question with an AI deadline.
Pressure is coming from both directions. CEOs and boards want real-time answers. CFOs can see their own AI pilots under delivering and increasingly understand why the architecture underneath is the constraint.
What’s also resonating is trust. Aptitude has spent over three decades implementing finance-grade systems for some of the world’s most complex and regulated organizations. CFOs know we’ve operated at this level before the controls, the auditability, the regulatory credibility are proven. What’s new is that we’ve combined that heritage with a modern AI-native platform that delivers in weeks and months, not multi-year programs.
PayByPhone was live in six weeks, processing 150 million journal lines a day. HCSC, one of the largest health insurers in the United States, reduced their M&A integration timeline by over 50% and achieved triple-digit ROI within the first month without replacing their ERP estate.
Trust plus speed. That combination is what the market has been waiting for.
Q5. AI is reshaping every part of fintech. How do you see that evolution playing out in enterprise finance software?
The category splits and we’re going to spend the next 18 months watching it happen in real time. And the divide between the two will define who wins in this market.
On one side: AI-enabled software. A copilot bolted on, a chatbot beside the ERP, a model summarizing reports the system already produced. It can accelerate individual tasks, but it can’t change what’s fundamentally possible. Bolt AI onto a batch-era ledger and you get a faster version of the same backward-looking thing.
On the other: AI-native software. Built from the ground up so AI operates safely inside the financial workflow from day one. In Fynapse, the close runs continuously, reconciliations resolve as transactions are processed, and AI can explain why a number changed because the lineage is intact from source event through to report. As AI technology evolves you evolve with it. Plug and play with the future, not locked into the past.
This is rapidly becoming the defining selection criteria when organizations evaluate finance technology. The incumbents cannot retrofit their way from one to the other architecture decisions made decades ago compound. The vendors who recognized this early and rebuilt the core will define the next decade of finance software. The rest will be selling efficiency improvements while the world moves on.
Q6. What are enterprise customers most concerned about when adopting AI-driven finance technologies?
Trust. By some distance. Not trust in the technology itself, trust in the output. Can I sign off on what this AI just produced? Can I explain it to my board? Can I prove it to my auditor? In finance, it’s not enough for AI to be right. It must be provably right. Every number must be defensible the moment it’s produced, not reconstructed after the fact.
This is where most AI implementations in finance fall apart. The model produces an answer, but the lineage isn’t there. You can’t trace it back to the originating transaction. You can’t explain how you got from input to output. And in a world where CFOs are signing off on AI-driven decisions to boards, regulators and public markets, that’s not acceptable.
We call our approach glass-box AI. Transparent the inputs are inspectable. Explainable the decision chain is traceable and immutable. Repeatable same inputs, same outputs, every time. And that governance has to live inside the architecture, not in a policy document alongside it.
Aptitude has spent three decades operating under exactly that level of scrutiny. That experience is built directly into Fynapse. Trust isn’t a feature we’ve added. It’s the foundation we’ve always built on.
Q7. What trends do you expect to define fintech and enterprise finance technology in H2 2026?
Four things are converging and they’re all happening at once.
The AI reckoning sharpens. Boards have moved from “are you investing in AI?” to “what return are you getting?” That question is about to get very uncomfortable for organizations with AI ambition sitting on legacy architecture.
ERP inertia hits a peak and then breaks. For years, CFOs have been paralyzed between two bad options, commit to a multi-year ERP replacement nobody wants to run, or do nothing. That paralysis is unsustainable. As it becomes clear that retrofitting AI onto legacy ERP simply doesn’t work, you can’t bolt intelligence onto a batch-era ledger and expect transformation organizations will start moving. Not by replacing ERP, but by decoupling finance from it entirely and modernizing the finance layer independently.
Speed becomes the credibility test. If a vendor tells you that modernizing finance takes 18 months before you see value, that tells you everything about the architecture they’re selling. Organizations going live in weeks will make that impossible to ignore.
And finance-grade data becomes the new procurement test. AI capability is table stakes every vendor claims it. What separates them is whether the data underneath survives scrutiny. Expect “show us your data foundation” to replace “show us your AI” in every serious Finance ERP evaluation before year end.
Q8. Looking ahead, what is your broader vision for the future of finance and what excites you most personally about where the industry is headed?
The vision is bigger than software. It’s a fundamental change in what finance is.
For most of its history, finance has been backward-looking. Close the period, explain what happened, do it again. Hugely capable people spending their best hours on reconciliation and reconstruction work AI can do reliably once the foundation is right.
The future we’re building toward is finance that doesn’t just operate in real time it operates ahead of time. Imagine AI agents running entire finance functions autonomously. Not just detecting anomalies after they happen but predicting them before they do and taking action. Not waiting to be asked a question but proactively surfacing the insight the business needs before the question is even formed. Pricing decisions made before margin slips. Risk identified before it crystallizes. Capital allocated before the opportunity closes. Finance moves from historian, to co-pilot, to something closer to the navigation system of the entire business.
What excites me personally is that this isn’t theoretical anymore. PayByPhone is running 150 million journal lines a day on Fynapse from a real-time P&L. Organizations are running continuous close in production today. The architecture works, AI runs inside it safely, and the operating model is already changing.
We’re helping define a category at the exact moment finance most needs it. It’s a good job to have in 2026.
