The Oper Management team, from left to right: Christine Hick (Head of Growth), Geert Van Kerckhoven (CEO), Stephanie Ng-Fragner (Legal Counsel), Wouter Lachat (Head of CS & Co-Founder of Oper), Charlotte Mast (COO). The team also includes Gerben De Graaf (Chief Technology Officer), who has recently joined.
Welcome to Fintech Scout! This feature offers the lowdown on exciting fintechs across the globe, as told by the people behind the idea. It explores why founders created their company, what they’re trying to achieve, what investors should know about them, and who they’re looking to work with and hire.
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The Basics
Company: Oper Credits
Founders: Geert Van Kerckhoven / Wouter Lachat
Headquarters: Antwerp, Belgium
Most Recent Valuation: Undisclosed. €11m Series A raise announced in August 2022
Employees: 32
Founded: 2018
In One Sentence: “At Oper, we combine deep mortgage expertise with technology to eliminate manual verification steps and digitalise the lending journey end to end.”
Top quote: “When the journey is clear and decisions are fast, more families get into homes they can afford to keep.”
Going Deeper
Who are you building your product/service for, and what painful problem are you taking away from their day?
We're building Oper for European lenders – banks, advisors and underwriters trying to deliver fast, fair home loans on top of a stack designed for the 1990s. A typical lender spends 70% of processing time just browsing through 30+ documents per loan application, and one in two applications comes back with errors. Borrowers feel it too: nearly 40% abandon mid-process. Herman, our AI agent, reads, verifies and cross-checks supporting documents against the loan application, applies the bank's credit policy step-by-step, and assembles a decision-ready file in three minutes instead of 60. Ultimately, humans focus on analysis and judgement instead of paperwork.
If a top-tier VC is reading this, what would you want them to understand about your company in 30 seconds?
Mortgages are Europe's largest consumer credit product and are still mostly manual. Oper is the agentic AI layer banks plug into their IT legacy stack. Herman, our AI agent, automates up to 90% of underwriting (versus ~30% for legacy straight-through), hits 95%+ extraction precision, cuts time-to-decision by 81%, and lifts digital conversion 26%. We're live with 20+ lenders across six markets, process €1.5B in volume per quarter and are backed by Bessemer Venture Partners and ABN AMRO Ventures.
Geert representing Belgium in the triathlon for the 2025 European Championships, where he finished 11th.
Why has nobody else done what you’re doing?
Mortgages don’t like shortcuts. The product is regulated by BaFin, FINMA, AFM and others; the documents are country-specific, and every bank's credit policy is different. Most fintechs went consumer-facing or tried to replace the loan origination system entirely. Both are incredibly hard.
We first went down the same route, replacing existing loan origination systems with our SaaS product, but when AI matured, we saw the opportunity to build tech which works with instead of against legacy systems. Agentic AI allows you to pull data from various sources and automate the heavy manual workload performed by credit analysts.
In fact, the biggest pain for our clients/prospects is incomplete and inconsistent loan applications. Meaning, supporting documents (payslips, ID, ECP certificates, etc.) are missing or data has incorrectly been added (typos, wrong address, etc.). Therefore, using AI to make sure all files are complete and consistent when an analyst starts doing his/her work reduces time-to-yes, manual workload and costs.
For these reasons we did the opposite of our competitors. We sat inside the workflow until we understood the actual work – including local quirks like Germany's Schufa report. Then we built modular, explainable agents on top of the bank's systems.
What was the “this has to exist” moment that made you start the company?
Fifteen years inside BNP Paribas, ING and EY taught me the "unglamorous truth": truly every European bank has a Herman – the senior analyst whose undocumented judgement holds a fragmented mortgage process together. When Herman retires, the bank loses both the expertise and the invisible glue keeping it moving. I'd watched it nearly happen more than once. The "this has to exist" moment was realising nobody was building the bank's forever Herman: an agent that captures that tacit expertise, applies it under credit policy, leaves an audit trail, and stays when people rotate.
Geert at the last annual get-together in Belgium, sharing his vision for Oper and the importance of agentic AI. Once a year, the Oper Credits team meets for 2-3 days of workshops, teambuilding activities and connecting. In 2025 the main focus was the pivot from SaaS to Agentic AI: how to build the product, how to position agentic AI, how to sell it.
How do you make money? Which part of your business model are you most excited about scaling?
Lenders license the Oper platform on recurring subscriptions, with modules they switch on as they roll out – Application, Advisory, Closing, and the Herman agent underneath (collection, validation, cross-check, credit policy, and decisioning). The part we're most excited about scaling is consumption tied to volume (usage-based pricing). As Herman takes on more cases for each customer, the value he delivers compounds, and so does what we earn.
We're already processing €1.5B of mortgage volume per quarter across 20+ lenders. There are a lot of uncertainties at the moment regarding SaaS/agentic AI pricing, but we see a clear shift to usage-based pricing (per processed page/document) – very similar to tokens. The issue is that clients still budget our product as SaaS and want visibility on their future costs (aka fixed pricing). So the aim is to start with small bundles, then lock clients in and switch to "tokens" in future.
Which proof points matter most right now?
Production deployments, doing real volume in regulated markets.
Numbers come from live cases, not demos: 20+ lenders across six European markets, €1.5B of mortgage volume processed per quarter, 90% of routine steps automated (versus ~30% for legacy straight-through), 81% faster time-to-decision, 26% higher digital conversion, 95%+ extraction precision, and roughly 90% first-time-right on submitted files. Manual file review goes from 60 minutes to three minutes.
Wouter, Geert, and part of the team at a teambuilding session in Belgium.
How does your product/service make the world a better place?
A mortgage is usually the biggest, most stressful financial decision a person ever makes, and in Europe it's getting harder. German house prices rose 80% in a decade; rates added €400 a month to a typical loan, and nearly 40% of applicants abandoned mid-process.
When the journey is clear and decisions are fast, more families get into homes they can afford to keep. Oper also makes lending fairer: every credit decision is mapped to policy, explainable, and audit-ready. That means fewer borrowers turned away on the wrong grounds.
What’s been your biggest unexpected challenge so far, and what did it force you to learn quickly?
We assumed the AI would be the hard part, but in fact it wasn't: the hard part was earning the right to be inside a bank. Regulated lenders don't sign because they like your demo but because your security, governance and explainability are bulletproof. Early on we lost months chasing logos with the wrong materials. We learned to lead with ISO 27001, single-tenant deployment, audit trails, EU AI Act readiness and human-in-the-loop architecture before the AI conversation even started.
Geert and Christine at the annual get-together in Belgium.
What does success look like in 12 months? What about in 5 years?
In 12 months: Herman customers are in production in at least Belgium, Germany and Austria, with measurable lifts in time-to-decision and first-time-right. This means pivoting from a SaaS company to Agentic AI (with live clients) in < 12 months.
We first had the idea for Herman in June 2025, the first prototype in September, and the first client signed in January 2026. In May 2026 we signed our third (Belgian) client, which means that Oper is one of the very, very few European companies that is deploying agentic AI at European banks. Not customer-facing, but actually doing heavy lifting at the lender's back office. That's something we are super proud of! Our goal is now to extend Herman's capabilities, win new clients and expand into new markets.
In 5 years: Oper is the default mortgage operating layer for European banks. Lenders compete on borrower journey and credit quality rather than rate alone. "Applying for a mortgage" no longer means losing weeks to PDFs.
What type of partner or collaborator would most accelerate your journey right now? (If relevant, which roles are you hiring for?)
Three types accelerate us right now. First, regulated lenders are ready to be first-movers on agentic AI in production – willing to be a reference, not just a logo.
Second, the ecosystem partners who already sit alongside us in the bank: core banking providers in our core markets.
Third, individuals and thought leaders with deep European banking networks to build a strong community around Oper and establish our company as a trusted advisor when it comes to agentic AI in lending.
On hiring, we're looking for 1 BA/Solutions Expert and 3 engineers (for short-term projects, freelancers), both in the Customer Success Team.
Finish this sentence: “We’ll know we’ve really made it when…”
... getting a mortgage in Brussels, Vienna or München – anywhere in Europe – takes minutes, not weeks.”
Website: www.opercredits.com
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