Oper Credits blog

Building the Agentic Bank: what needs to be true

Geert Van Kerckhoven — Mon, 18 May 2026 22:00:00 GMT

The Agentic Bank isn't a destination. It's a direction

Part three of a three-part series on the agentic bank written by Oper’s CEO and Co-Founder Geert van Kerckhoven.

In the first two parts of this series, I laid out the opportunity: a front office redesigned around agent-to-agent interaction, a mid-office and back-office where AI delivers immediate ROI, and a lifecycle management layer where the cost-of-tokens thesis unlocks product categories that simply didn't exist before.

But a blueprint is only useful if you can execute on it. So let me close with what actually needs to be true.

Model selection: three capabilities that matter

Not all models are created equal for lending. When you're evaluating your AI architecture, three capabilities matter above everything else.

Multimodal data handling. Mortgage origination is inherently multimodal - scanned payslips, handwritten annotations, photographed IDs, structured XML data from registries, free-text emails from brokers. Your model stack needs to handle all of it natively, not through a patchwork of specialised tools.

Reasoning with auditability. In a regulated environment, it's not enough for a model to reach the right conclusion. You need to demonstrate how it got there. Reasoning chains must be auditable, verifiable, and non-discriminatory. This isn't a nice-to-have - it's a supervisory requirement.

Tool use and computer use. The agent needs to interact with your systems - API gateways, core banking platforms, document management systems, external databases. And sometimes it needs to use vision to navigate mainframe interfaces that were never designed for machine interaction. Models that excel at tool use are models that can actually operate within your technology landscape.

Sovereign vs. Cloud: a European question

European banks face a deployment question that their US counterparts largely don't: where does the data go?

The good news is the open-source model ecosystem has matured to the point where you have genuine choices. Models can run in private European cloud infrastructure with full data residency guarantees. You can mix and match: sovereign deployment for sensitive processing, cloud for non-sensitive tasks, with clear data governance at every boundary.

Your AI roadmap should map which models can be deployed in a sovereign way, which in a non-sovereign cloud, and which tasks require which posture. This isn't a one-time decision - it's a living architecture that evolves as models improve and regulatory requirements clarify.

The August 2026 deadline

If you're using AI for credit scoring or loan approval in the EU, you're operating a high-risk AI system under Annex III of the EU AI Act. Full obligations take effect August 2, 2026.

This means: conformity assessments, quality management systems, EU database registration, comprehensive documentation of training data, risk management procedures, human oversight mechanisms, and ongoing monitoring for accuracy, robustness, and non-discrimination. Penalties for non-compliance: up to €35 million or 7% of global turnover.

The EBA has noted that many of these requirements can be integrated into existing supervisory review and evaluation procedures. But "can be integrated" is not the same as "will be easy." Banks that haven't started this work are running out of runway.

The Digital Omnibus proposal may extend certain deadlines to December 2027 or August 2028, depending on harmonised standards availability. But planning on a deadline extension is a strategy for the optimistic.

The bank that moves first

The agentic bank isn't a destination. It's a direction - and a compounding one. Every dossier processed builds institutional knowledge. Every edge case resolved deepens the agent's expertise. Every workflow automated frees capacity for the next initiative. The banks that start now build advantages that compound, while the banks that wait will find the gap widening every quarter.

The blueprint laid out here - from borrower-facing agents through AI credit committees to dynamic loan products - isn't science fiction. The technology exists. The regulatory frameworks, while demanding, are navigable. The economics are overwhelming. What's missing, in most institutions, is the architectural vision and the organisational will to pursue it front-to-back rather than piecemeal.

We see a future where mortgage lending is faster, more flexible, more affordable, and more responsive to borrowers' lives. Where the cost of processing doesn't dictate the boundaries of product design. Where agents handle the volume, and humans handle the judgment. Where the front office, the back office, and the lifecycle are connected by a single intelligence layer that learns, adapts, and improves with every interaction.

That future isn't coming. For the institutions that are paying attention, it's already here.

Oper Credits is a pan-European mortgage origination platform. Herman is our AI agent for residential lending - built on context engineering, powered by composable domain skills, and already in production with 22+ financial institutions across 6 EU countries. To learn more, visit opercredits.com or reach out directly.

The engine room is changing next - and the ROI will surprise you

Geert Van Kerckhoven — Mon, 11 May 2026 22:00:00 GMT

Part two of a three-part series on the agentic bank written by Oper’s CEO and Co-Founder Geert van Kerckhoven.

The front office gets the headlines. But if you ask me where AI delivers the most immediate, measurable return in mortgage lending right now, the answer is the mid-office and back-office. And if you ask where the most transformative long-term opportunity lies, the answer surprises most people: it's in what happens after the loan is booked.

Document Intelligence at scale

The core bottleneck in mortgage origination hasn't changed in decades: documents. Collecting them, reading them, validating them, cross-referencing them against policy, sending them back when something's missing, waiting for resubmission, repeating the cycle.

Agentic AI fundamentally breaks this bottleneck. Agents parse PDFs, bank statements, ID proofs, property documents, and income records. They classify files, detect missing pages, and extract entities - names, employer details, cashflows, account balances - across multilingual, multiformat inputs. Not the brittle OCR of five years ago. Real comprehension: understanding what a document means, not just what it says.

The numbers are striking. AI-enabled underwriting can process 70–85% of credit applications without human intervention. Agentic workflows reduce per-loan processing costs by 35–50%. Freddie Mac estimates that lenders fully utilising AI automation can save up to $1,500 per loan and shorten production cycles by five days on average. Automation Anywhere reports that agentic process automation slashes processing time by 88%.

And this isn't pilot-stage. According to Celent, 83% of lenders plan to increase their generative AI budgets in 2026. Two-thirds have already completed or will implement GenAI strategies this year. The market has moved decisively past experimentation.

For both conventional and non-conventional lending - where document complexity and edge cases multiply - we'll see the most dramatic progress. Tool use makes it trivial for agents to query external databases, verify employment records, cross-reference tax filings, and check regulatory registries. The mid-office and back-office analyst who previously spent hours assembling a dossier will spend minutes reviewing one.

The AI credit committee

Here's a concept that sounds radical until you think about it for five minutes: multi-agent credit deliberation.

Today, a credit committee is a group of experienced professionals who review a dossier and debate whether a borrower is creditworthy. They bring different perspectives - risk appetite, regulatory awareness, portfolio balance, relationship context. The process works, but it's slow, expensive, and constrained by human availability.

Now imagine multiple agents - potentially running on different models, with different specialisations - conducting that same deliberation. One agent analyses financial risk. Another evaluates the business model. A third checks regulatory compliance. They surface their findings, flag disagreements, and produce a structured recommendation. McKinsey describes these as "multiagentic squads" that facilitate full credit review workflows, and reports that one financial institution is already using a multiagent system to draft financial-risk assessments for corporate clients.

The human doesn't disappear. The human becomes the final reviewer - ensuring guardrails have been respected, checking for discrimination, exercising judgment on edge cases. But the preparation that previously took days now happens in near real time. McKinsey estimates 40–80% productivity uplift per use case, with approximately 30% faster decision-making.

The compliance dimension is critical. AI systems now analyse hundreds or thousands of variables compared to traditional models that use fewer than 20 data points, achieving 25–50% uplift in loan approvals without additional risk. But with that power comes responsibility. The $89 million in penalties levied against Apple and Goldman Sachs for algorithmic discrimination concerns in 2024 underscore why the human-in-the-loop guardrail isn't optional - it's existential. Best practice: establish a Model Risk Committee with authority over approvals, performance reviews, exception handling, and sunset decisions.

Collateral through a new lens

For collateralised lending - mortgages above all - multimodal AI also transforms how you evaluate the collateral itself.

Computer vision can now assess property conditions from imagery at scale. Restb.ai's partnership with HomeVision, announced March 2025, uses computer vision to detect inconsistencies between appraisal data and property imagery, automatically analysing property conditions and unlocking visual insights. Over 70% of real estate firms now use AI-driven models for valuation. Fannie Mae and Freddie Mac approve automated valuation models for specific mortgage products, with Fannie Mae reporting that standardised data collection saves consumers $350-$400 over traditional appraisals.

The CFPB approved new rules in June 2024 on AI/algorithmic use for home appraisals, establishing quality standards for automated valuation models. The regulatory framework for AI-driven property valuation exists. The technology is mature. And for lenders processing thousands of mortgage applications, the combination of automated document analysis and automated property assessment creates a fully agent-mediated path from application to credit decision.

Beyond RPA: Lifecycle management

Most conversations about AI in lending stop at origination. That's a mistake. The most transformative implications may be in what happens after the loan is booked.

Today, lifecycle management is a world of forms. A borrower wants to swap collateral - form. A property gets split because of a divorce - form. Someone passes away and the estate needs to restructure - form. Each form triggers a registration on the core banking system. RPA has already automated many of these procedural tasks, and credit where it's due: it helped.

But RPA is brittle. It follows scripts. It breaks when the process changes. It can't handle ambiguity, exceptions, or tasks that require understanding context.

Agentic AI is different. An agent that understands the lifecycle event, knows the procedures, has access to the tools - the core banking system, the document management platform, the regulatory databases - and can reason through the specifics of each case. A borrower sends an email requesting a modification. The agent picks up the email, interprets the request, verifies eligibility, executes the task across systems, sends confirmation, schedules follow-ups. Not a predefined workflow - an adaptive process that handles the variability of real life.

AI-driven post-close audits can now review 100% of loans versus the previous 10% sample, reducing repurchase exposure and audit preparation by approximately 50%. Fannie Mae reports a 29% average decrease in operational costs for lenders using AI, and a 50% reduction in mortgage fraud cases through ML-based detection.

The cost-of-tokens thesis

Here's the argument that I think changes everything in lending - and it has nothing to do with origination.

When the cost of a loan modification is measured in tokens, not hours, the entire product design space opens up.

Today, loan modifications are expensive. They require human analysts to review, process, and register changes. So lenders minimise them. Many tasks that would genuinely improve borrower outcomes are simply deleted from the product because the operational cost doesn'tjustify the benefit. Post-renovation rate adjustments? Too expensive to process manually. Dynamic amortisation based on life events? Operationally impractical. Continuous energy-efficiency monitoring that triggers rate improvements? Nobody has the staff for that.

But when the marginal cost of processing a modification drops to fractions of what it costs today - because agents work 24/7, scale elastically, and run at token prices that decline year over year - those deleted tasks come back. And with them, entirely new product categories.

Dynamic amortisation tables

Imagine a mortgage where the repayment schedule adjusts automatically based on life events - job loss, salary increase, parental leave, retirement. The agent monitors, verifies, and modifies. No forms. No waiting.

Energy-efficiency repricing

A borrower renovates their property, improving its energy rating. The agent detects the change through utility data, renovation permits, or updated energy certificates, verifies it, and adjusts the interest rate accordingly. A task that was impossible when it required human processing becomes a standard product feature.

Continuous post-close monitoring

After-checks that were cut for cost reasons - income verification, property condition, insurance compliance - become standard when they cost tokens instead of hours. The lender's risk management improves, and the borrower gets better terms because the lender has betterinformation.

Forrester research confirms the direction: 70% of financial services respondents anticipate using agentic AI to deliver tailored experiences previously available only to high-net-worth individuals - including automatic refinancing when rates decrease and proactive portfolio management. One financial services organisation already has 60 agentic agents in production, with plans for over 200 by 2026.

The implications for affordability are significant. More flexible products, more responsive servicing, and lower operational costs all feed into a mortgage landscape where lending can be more affordable, more personalised, and more adaptive to the realities of borrowers' lives. That's not incremental. That's a different kind of product entirely.

Next in this series: What actually needs to be true for all of this to work - model selection, sovereign deployment, and the EU AI Act deadline that's closer than most banks think.

Geert is CEO of Oper Credits, a pan-European mortgage origination platform. Herman is Oper's AI agent for residential lending, already in production with 22+ financial institutions across 6 EU countries.

The Mortgage Front Office Has Already Changed - Here's What's Coming Next

Geert Van Kerckhoven — Tue, 14 Apr 2026 22:00:00 GMT

“I wrote "A Blueprint for the AI-Native Lender" as a report earlier this year. But reports sit in inboxes. This series is my attempt to make the same argument in a format that travels further - broken into three parts, written directly to the people running mortgage operations across Europe. The ideas are the same. The urgency hasn't changed.” - Geert van Kerckhoven

Every few years, someone declares that AI will transform banking. Most of the time, it doesn't - not because the vision was wrong, but because the technology wasn't ready.

This time is different. And if you're running a mortgage operation in 2026, you already feel it.

The models have crossed a threshold. Not in one dimension - in all of them simultaneously. Reasoning, vision, multimodality, tool use, code generation, legal text interpretation. Each capability was interesting in isolation. Together, they're transformative. Because for the first time, we can build AI systems that don't just answer questions or classify documents - they deliver outcomes. They reason through edge cases. They know your processes. They adapt to circumstances rather than following predefined workflows.

That's what we mean by agentic. Not a chatbot. Not an automation script. Not a thousand if-then rules stitched together. An agent is more like a thousand workflows distilled into a single intelligence - one that handles the variability, the exceptions, the judgment calls that previously required a human sitting at a desk.

The implications for mortgage origination, credit decisioning, and loan lifecycle management are profound. Over this three-part series, I'll lay out a front-to-back blueprint: where the opportunities are, what patterns are already emerging, and how a truly agentic lender should think about redesigning its value chain. We'll start where disruption will be most visible - the front office.

The AI-Assisted Advisor

The most immediate application is augmenting human loan advisors. Picture an advisor sitting with a borrower, backed by an agent that has already ingested the lender's full risk policy framework, current rate structures, and regulatory requirements. The advisor asks questions; the agent surfaces tailored, policy-compliant recommendations in real time.

This is already happening. In the US, platforms like Addy AI train on lender-specific guidelines - Fannie Mae, Freddie Mac, non-QM - and provide real-time analysis during borrower conversations. Frost Bank, which re-entered mortgage lending with technology at its core, ended 2025 with $595 million in unpaid mortgage balance, exceeding its goals by 19%. One credit union reported its AI assistant resolved over 90% of customer inquiries instantly, cutting support calls by 20%.

But here's the thing: if the agent can deliver that advice in real time, calibrated to policy, across every product - do you need the advisor in the same form at all?

The Borrower-Facing Agent

That question leads directly to putting the agent in front of the borrower. An AI model - available 24/7, in any language, infinitely patient - that guides a borrower from initial inquiry through affordability assessment, document collection, and all the way to pre-approval. Not a form with a chatbot bolted on. A genuine conversational experience that understands context, answers specific questions, and moves the process forward.

Infosys has described this as a "Conversation Agent" - an always-on assistant that serves as the borrower's single point of contact from rate shopping through post-close. ServisBOT already deploys AI borrower assistants that manage servicing interactions via voice, chat, or email, assisting through pre-qualification and application. In March 2026, Mortgage Automation Technologies launched BIG AI - a ChatGPT-like interface purpose-built for mortgage, supporting natural language queries, amortization schedules, and client proposals, powered by the lender's own origination data.

Only 9% of American consumers now prefer branch visits. 55% use mobile banking apps as their primary channel. We don't think Europeans are far off. The direction is clear: borrowers want digital-first, and agents can deliver an experience that's not just digital, but genuinely intelligent.

When Borrowers Bring Their Own Agents

Now here's where it gets really interesting - and where most banks stop thinking too early.

People are already using ChatGPT and Claude to advise them on mortgages. This isn't speculation. A LendingTree survey found 51% of respondents turn to AI for financial advice, with 30% saying AI influenced their decision to open or close a financial account or loan. A Veterans United poll showed 32% of homebuyers use AI tools, and 22% specifically use them to compare mortgage lenders. Among Gen Z, 74% have used an AI chatbot at least once.

And it's not just consumers. 82% of mortgage brokers have used ChatGPT or other AI tools within the past three months. Nearly half use AI daily.

We're not a year away from consumers using agents to shop for mortgages. We're already there.

Now extrapolate. Today, a consumer asks ChatGPT for general mortgage advice. Tomorrow - and "tomorrow" may be months away, not years - the consumer's agent contacts your bank directly. It queries your rates. It submits a pre-qualification request with the borrower's financial profile. It compares your offer against three competitors. It negotiates. All machine-to-machine.

As a bank, how do you respond to that?

The MCP Moment: Building Machine-Readable Interfaces

We believe banks need to start thinking about programmatic interfaces for external agents - the equivalent of what APIs did for open banking, but for AI-to-AI interaction.

The infrastructure is emerging. Anthropic's Model Context Protocol (MCP), introduced in late 2024 and donated to the Linux Foundation's Agentic AI Foundation in December 2025, provides an open standard for connecting AI agents to external systems. Google's Agent-to-Agent (A2A) protocol offers another pathway. Both are seeing rapid adoption - hundreds of tool providers and major platforms have integrated MCP within its first year.

In banking specifically, Block (Square) has integrated MCP into internal payment workflows. Research shows banks implementing MCP-like protocols can give AI agents real-time access to transaction data, enable dynamic risk assessment, and integrate with existing systems - all while maintaining OAuth 2.1 security compatible with PSD2, BSA/AML, GLBA, and EU DORA requirements.

And the regulatory enablement is coming. PSD3, which reached provisional political agreement in November 2025, standardizes Open Banking APIs as the main access point for data exchange, with stronger governance and customer dashboards for permission management. Targeted applicability: Q2/Q3 2028. The regulatory infrastructure for agent-to-bank communication is being built in parallel with the technology.

What we expect to see - and what forward-thinking banks should prepare for:

Agentic mortgage brokers. Not human brokers using AI tools, but AI-native brokerage platforms that contact banks for quotes programmatically, compare offers across dozens of lenders in seconds, and present borrowers with optimised recommendations. The pattern is clear: 82% of brokers already use AI, adoption is accelerating, and the economic logic of replacing human intermediation with agent-mediated comparison is overwhelming. Whether this emerges as standalone "agentic broker" platforms, as features within consumer-facing AI assistants, or as specialist marketplaces remains to be seen - but the direction is unmistakable.

Consumer agents negotiating directly with bank agents. This creates a new interaction paradigm: what information do you want to disclose to an external agent? What don't you? How do you price competitively when every offer is compared algorithmically in real time? Banks that build these interfaces early - with appropriate compliance safeguards - will have a structural advantage in a world where the borrower's first touchpoint isn't a website or a branch, but an agent acting on their behalf.

The banks that dismiss this as futuristic will find themselves scrambling when a significant share of mortgage inquiries arrives not from browsers, but from agents.

Next in this series: I'll look at where AI delivers the most immediate ROI - the mid-office and back-office - and why the most transformative implications in lending may have nothing to do with origination at all.

Onesto Launches Herman: Faster, Compliant Mortgage Decisions

Geert Van Kerckhoven — Tue, 17 Mar 2026 23:00:00 GMT

New Client Announcement

Onesto, one of Belgium's leading social mortgage and renovation lenders, has deployed Herman to transform how its team processes loan applications. With a mission centered on long-term, affordable homeownership for families, Onesto needed a solution that could handle the administrative complexity of mortgage processing - without losing the human attention each applicant deserves.

Herman now automates document validation, data extraction, fact-checking, and credit policy checks across Onesto's mortgage applications. The result: analysts spend less time navigating paperwork and more time understanding the person behind each file - assessing financial resilience and connecting borrowers to renovation advice through Onesto's One-Stop Shop model.

"At Onesto, our mission goes beyond granting loans. We aim to ensure that families can not only buy a home, but continue to live in it affordably in the long term. By integrating AI into our document analysis process, we free up valuable time for our experts to focus on each applicant's specific situation. Herman supports our team in handling administrative complexity, so we can invest more attention in responsible lending and in connecting borrowers to renovation advice through our One-Stop Shop model." - Hans Vermeulen, CEO, Onesto.

"Herman automates the full mortgage underwriting workflow - document validation, data extraction, fact-checking, policy application - in minutes rather than hours. What makes Onesto's case particularly compelling is the complexity involved: renovation financing, many document types, and detailed policy requirements that would typically demand significant analyst time on every single file. Onesto is a strong example of what responsible AI adoption in lending looks like in practice.” - Geert Van Kerckhoven, CEO Oper Credits.

Forbes Belgium: Fixing Europe’s Mortgage “Time-to-Yes” Crisis | Oper

Geert Van Kerckhoven — Thu, 26 Feb 2026 23:00:00 GMT

In a recent profile in Forbes Belgium, we share how we’re tackling one of the biggest bottlenecks in European mortgage lending: the slow, largely manual credit decision. While the front end of the mortgage journey is now highly digital – simulators, online onboarding, document upload portals – the real “time-to-yes” moment often still relies on people opening PDFs, visually comparing data, retyping information and sending incomplete files back and forth. From our base in Antwerp and across six European markets, we’ve built Oper to address exactly that gap in the process.

The article highlights how our software focuses on the “decision core” of mortgage origination: underwriting, document processing and policy checks. Our AI agent, Herman, acts like a junior credit analyst – recognising documents, checking completeness, structuring data and surfacing signals for a senior underwriter, without ever taking the final decision. We work strictly within each bank’s own data sources and credit policies, and our algorithms are designed to reason, not guess: when Herman encounters something he doesn’t understand, he stops and hands it over to a human. Drawing on insights from our “Mortgage Trends 2026: A New Era of Intelligent Mortgage Origination” report, our CEO and co-founder Geert Van Kerckhoven explains how rising regulatory requirements, more complex files (often 20–50 documents per case) and a shortage of credit analysts have created a structural “time-to-yes crisis” just as banks want to grow their mortgage books without proportionally increasing headcount.

Looking ahead, we share an ambition that’s simple to describe but challenging to achieve: moving the industry towards near real-time, safe and compliant mortgage decisions for the vast majority of cases, fundamentally improving the customer experience while making underwriting more scalable. The profile also touches on broader questions around pricing, segmentation and profitability in mortgage lending, underlining the need for better data-driven decisioning.

Read the article in French

Finance Derivative on AI in European Mortgage Underwriting | Oper

Geert Van Kerckhoven — Thu, 26 Feb 2026 23:00:00 GMT

In this thought leadership piece for Finance Derivative, our CEO, Geert Van Kerckhoven, explores how Europe’s mortgage market has rebounded since the 2022–2023 rate shock—and why lenders now face a new kind of pressure. With 2025 marking a return to lending growth and improving borrower confidence, we see a clear shift: having a digital front end is no longer enough. While many banks have invested in apps, chatbots, affordability calculators, and digital ID checks, the real bottleneck remains hidden in the core of the mortgage process.

We highlight research showing that the biggest drop-off in digitalisation sits between document upload and credit decision, where underwriting still relies heavily on manual checks, fragmented workflows, and long email chains. In markets like Germany, most lenders still take more than a week to reach a final decision, and roughly a third take over two weeks. Our position is that specialised, well-governed AI in the back office is now a decisive competitive advantage: it can automate up to 90% of routine steps and improve time to decision by around 81%, turning documents into reliable data, clearing backlogs, and supporting underwriters on complex cases. Crucially, we stress that this doesn’t replace people—borrowers still want human contact. Instead, it frees advisors to focus on nuanced situations such as non-standard income, where human judgement and advice are essential.

Looking ahead, we argue that the European mortgage industry is moving from “digital” to truly “intelligent” operations, and that purpose-built AI in underwriting and documentation will become the new baseline for lenders who want to stay ahead of the curve. This perspective reinforces Oper’s role in shaping the future of mortgages across Europe: combining deep mortgage expertise with specialised AI that keeps human decision-makers firmly in control.

Read the full article here

Oper Credits launches digital mortgage simulator for a Belgian cooperative bank

Geert Van Kerckhoven — Tue, 17 Feb 2026 23:00:00 GMT

Oper Credits is proud to welcome a leading Belgian bank as a new customer. The bank is taking an important step in strengthening its digital mortgage experience by launching a new public mortgage simulator on its website, powered by Oper.

Serving private individuals, self-employed professionals and SMEs across Belgium, the institution combines strong local roots with a long-term vision. With a focus on proximity, trust and personal advice, it continues to invest in digital solutions that complement its relationship-driven model.

Enhancing the online mortgage journey

With the introduction of a public simulator, the bank aims to improve how customers explore mortgage options online. The simulator is designed to:

Accelerate time-to-market, leveraging Oper’s out-of-the-box white-label technology
Increase conversion, by encouraging website visitors to book an appointment with an advisor
Deliver a seamless experience, fully embedded within the bank’s website and aligned with its brand

By guiding users through key mortgage scenarios in an intuitive way, the simulator acts as both an information tool and a lead generator.

Built for flexibility and continuous improvement

Oper’s simulator gives the bank the flexibility to adapt its digital mortgage journey over time. The solution is fully configurable, integrates directly with the banks' analytics and conversion tracking tools, and is built on secure cloud infrastructure.

This enables continuous refinement of the experience based on user behaviour, while ensuring a robust and compliant technical setup from day one.

Looking ahead

This collaboration reflects a shared focus on customer-centric digital innovation. With the new mortgage simulator, the bank strengthens its online presence while maintaining the personal advisory approach at the heart of its service model.

We are excited to support this customer on its journey and look forward to seeing the impact of this new digital experience on future homebuyers.

Welcome to Oper.

Oper Targets Luxembourg with AI-Assisted Mortgage Underwriting

Geert Van Kerckhoven — Wed, 14 Jan 2026 23:00:00 GMT

Paperjam highlights our expansion into Luxembourg as a priority market for our AI-assisted mortgage underwriting platform. In the interview, our co-founder and CEO, Geert Van Kerckhoven, explains how we aim to support local lenders as the housing loan market recovers, combining end-to-end digital origination with agentic AI. He describes how our AI agent “Herman” helps credit teams verify data and apply policy checks in a transparent, auditable way—reducing manual review that can take hours down to under three minutes—and notes that, across more than 20 European lenders, this has helped cut processing costs, speed up decisions and improve conversion by 27%.

The coverage underlines what differentiates our approach: we focus on the full mortgage funnel—from lead generation through to a qualified electronic signature—while keeping regulation and auditability at the core of our product design and aligning our AI work with the EU AI Act. We’re portrayed as mortgage specialists rather than generalist lenders’ tools, with our platform already compatible with Luxembourg’s regulatory, digital identity and signing frameworks. The piece also connects our partnership with DocuSign and the strong adoption of LuxTrust to the potential for faster, more compliant digital closings in Luxembourg, and notes that while our commercial focus is on banks’ cost-to-serve, speed and conversion, homebuyers ultimately benefit from clearer, more predictable journeys and quicker decisions.

Looking ahead, we set out three strategic ambitions: to become the leading digital origination and operations platform for European lenders, to scale agentic AI across underwriting with full auditability, and to deliver step-change improvements in cost, conversion and time-to-decision. Within that roadmap, Luxembourg is presented as a priority expansion market where a concentrated lender base, strong digital ID infrastructure and recovering mortgage volumes create ideal conditions for fast, measurable impact.

Read the full article here

InFinance: Decision Speed Becomes Decisive in Dutch Mortgages | Oper

Geert Van Kerckhoven — Wed, 14 Jan 2026 23:00:00 GMT

In its coverage of our Mortgage Trends 2026 report, InFinance highlights a structural turning point in the Dutch mortgage market that aligns closely with our vision: decision speed is becoming the decisive competitive factor. We show that in an intermediary-driven, highly competitive landscape, the lender that can deliver a clear “yes” or “no” fastest increasingly wins the customer—regardless of a sharp rate or product proposition.

Our analysis reveals that many lenders still need 9 to 14 working days to reach a final approval. While the digital front end of the journey (orientation, onboarding, document upload) is largely in good shape, the real bottleneck sits at the heart of the credit process: acceptance. Manual document checks, repeated rekeying of data, and human interpretation of complex credit policies remain the norm. This becomes especially painful in more complex cases—such as multiple income streams, sustainability measures, or policy exceptions—where roughly 20–30% of applications drive a disproportionate share of workload and lead time, leaving acceptance teams under sustained pressure. We also point out that AI could significantly improve efficiency, but its broader deployment is being held back not by technology, but by governance and regulatory questions around the EU AI Act, explainability, data quality, and human oversight.

The coverage underscores our central message: lenders that fail to structurally improve decision speed risk losing competitiveness, no matter how attractive their price or product. For stakeholders across the mortgage value chain, this is a clear call to move from front-end digitisation to deep process transformation in credit decisioning.

Read the article in Dutch

AI Implementation in Mortgage Origination: Challenges and a Practical Approach

Geert Van Kerckhoven — Thu, 08 Jan 2026 23:00:00 GMT

The AI opportunity in mortgage origination today

“What are the blockers stopping AI adoption in mortgages at the moment?”

It’s a deceptively simple question, because the easy answers of “models aren’t good enough” or “documents are too messy.” aren’t correct. Instead, Oper Founder and CEO Geert van Kerckhoven argues:

“The only hurdle today is that, from a compliance and a risk point of view, there is a limited visibility on regulations… If it's not clear, the answer is: let's not do it.”

Technically, the building blocks for AI-enabled mortgages are already here:

OCR and layout‑aware vision–language models can reliably extract fields from payslips, tax statements, and property appraisals in controlled conditions.
Open-banking and national data rails (like the Dutch HDN and UWV income verification) can validate income and property attributes in minutes.
Large language models (LLMs) can read underwriting manuals, explain policies, and even assemble decision rationales.

What governs the pace in Europe is not model capability, but three things:

Regulatory clarity – The EU AI Act explicitly classifies AI used to evaluate creditworthiness as high-risk, with heavy obligations.
Governance and auditability – Supervisors expect documented data lineage, model oversight, and human control, following frameworks like the EBA’s Loan Origination and Monitoring (LOM) Guidelines.
Data reality – In some markets, everything’s on APIs; in others, income still arrives as scanned PDFs or even faxes.

The thesis: phased, compliance‑first operationalization

The temptation is to aim straight for “one-click mortgage approval”; walking into a house and getting an offer in real time.

“I think there is no real hurdle there technically… But if you immediately want to do that, then there's a lot of compliance work that needs to come first.”

Instead, Oper argues for a different route:

Start with low‑risk, high‑ROI, narrow use cases that do not themselves make or materially influence the credit decision (e.g., document classification, key‑field extraction with human review, policy parsing, product pre‑screening).
Chain these automations over time, building the governance, audit trails, and comfort that will eventually support end‑to‑end underwriting.
Design everything compliance‑first, aligned with the EU AI Act, EBA LOM, DORA, GDPR, and national supervisory expectations.

You can create meaningful efficiency gains in 12–24 months, and quietly prepare your organization for fully automated decisions when regulation and internal governance catch up.

What’s Actually Blocking AI in Mortgages

Regulatory ambiguity and model‑risk realities

Under the final text of the EU AI Act, AI systems used “to evaluate the creditworthiness of natural persons or establish their credit score” are explicitly listed as high‑risk. That classification brings with it stringent obligations:

Risk management and control frameworks
Data governance and quality processes
Detailed technical documentation and logging
Transparency to users and clear human oversight
Accuracy, robustness, and cybersecurity standards

At the same time, Article 6(3) clarifies that narrow, procedural systems that do not materially influence a decision - for example, classifying documents or transforming a PDF into structured data with human validation - can remain outside the high‑risk category.

The challenge is that most banks don’t yet have a robust internal playbook for deciding what is, and isn’t, “material influence.” So the safest answer from compliance and risk teams has often been: “Let’s not do it yet.”

Post‑crisis risk posture in European banks

That caution didn’t emerge in a vacuum. After the 2008 financial crisis, European supervisors doubled down on governance, documentation, and model risk management. The EBA’s Loan Origination and Monitoring Guidelines codified this for credit processes: banks must show that each loan decision is based on sufficient, verified data, and that any models used are well understood and controlled.

Or, more directly put:

“If it's not clear, the answer is let's not do it.”

With DORA now in force, ICT risk, logging, and third‑party oversight requirements are even tighter. That’s another reason why AI pilots that touch input data (classification, extraction) are simply easier to defend than those that directly make or override credit decisions.

Not a tech problem: LLMs are capable, but governance lags

From a pure technology perspective, we’re much closer to automated underwriting than many assume:

Layout‑aware document models achieve high F1 scores on public key‑information‑extraction benchmarks like SROIE and FUNSD.
LLMs can interpret long, messy policy texts and output machine‑readable rules plus human‑friendly explanations.
Orchestration tools can chain these steps into full workflows.

That technology capability gets us much of the way there:

“To get to a fully automated underwriting, you need to be able to create a profile of a client, read the documents… A lot of these steps can already be automated with AI.”

The blockers, then, are:

Governance maturity (model inventory, testing, monitoring)
Documentation and explainability at a regulator‑grade standard
Comfort in risk and compliance that AI is controlled, not a black box

Where AI Can Create Value Now (Low‑Risk, High‑ROI Use Cases)

Document classification and key‑field extraction

“The Achilles heel today is very easy. It's the extraction out of paper documents.”

In API‑rich markets, much of the mortgage payload is machine‑readable. In others (such as Germany, parts of Benelux, and Southern Europe) income and property evidence still arrives as PDFs or scans. That’s precisely where AI can create immediate value without stepping into high‑risk territory.

What to do now:

Automatically classify incoming documents (payslip vs. bank statement vs. appraisal).
Extract key fields—names, addresses, gross/net income, dates, loan amounts—with confidence scores.
Route low‑confidence or critical fields to human reviewers.

Because this is data transformation with human‑in‑the‑loop, and not an autonomous credit decision, it can be structured to remain outside the AI Act’s high‑risk category - provided humans validate before the data is used for underwriting.

Policy extraction and rules application

Most underwriting manuals are dense PDFs that live in shared drives and underwriters’ heads. LLMs can:

Read these manuals and extract eligibility rules (e.g., LTV caps, minimum tenure, income stability conditions).
Convert them into machine‑readable checks (if/then rules or decision tables).
Attach plain‑language rationales to those checks.

Underwriters remain in control: AI prepares the rule set and applies it, but humans approve both the rules and any exceptions. In that form, it’s support and pre‑analysis, not automated decision‑making.

Product fit and eligibility screening

One of the easiest wins is intelligent pre‑screening:

Given a borrower profile and extracted data, AI filters the product set to “likely eligible” options.
It can propose required condition based on the rules above.
An underwriter or advisor confirms the recommendation.

This doesn’t replace your credit policy; it reduces noise and rework, so specialists spend their time on real edge cases.

Data prefill from public and partner APIs

In more digital markets, AI and APIs reinforce each other:

In the Netherlands, the HDN standard and UWV’s Inkomensbepaling Loondienst (IBL) allow lenders to verify salaried income directly from social‑insurance records—now used in hundreds of thousands of cases per year.
Property and address data can be pulled from Kadaster/PDOK APIs to prefill collateral details.
In Estonia and parts of the Baltics, the X‑Road infrastructure enables rapid, secure sharing of identity and registry data.

These aren’t “AI use cases” in isolation, but AI orchestration (e.g., an intake assistant that knows which APIs to call and when) can turn them into a fully guided, low‑friction origination experience.

Market Dynamics Creating Urgency

Consolidation and scale without headcount

Across Europe, banks are consolidating mortgage books and seeking scale—but they’re not planning to double headcount as volumes grow.

“I do not hear the narrative anymore of ‘if I use technology, I can slash my headcount by X.’ … It's more like, I'm not finding the people. Volumes are back, we would love to double production and we don't want to double analysts.”

External benchmarks back the opportunity: European “average” mortgage time‑to‑cash sits around 40 working days, while top performers are closer to 18 days. Reducing touches per file and cutting rework loops is not a nice-to-have; it’s the only way to hit those numbers without burning out staff.

Talent scarcity and backlog pressure

AI, in this context, is not a headcount‑reduction tool; it’s a throughput multiplier:

Fewer manual data entry tasks for underwriters.
Fewer back‑and‑forths with customers and brokers for missing documents.
Cleaner files arriving at the credit desk, with pre‑assembled rationales.

That’s why we’re seeing a genuine inflection in operational seriousness: banks are finally treating their back office as a competitive differentiator, not just a cost center.

What to do next

If you’re a Head of Mortgage Operations, a Chief Risk Officer, or an AI transformation lead, the path forward is surprisingly concrete:

Pick two low‑risk use cases to start:
- document classification + key‑field extraction, and
- policy extraction + eligibility pre‑checks.
Bring risk and compliance in from day one. Map use cases to the EU AI Act, EBA LOM, DORA, and national guidance. Document why they are preparatory and non‑high‑risk.
Stand up a basic QA and lineage framework. Dual extraction for critical fields, sampling, clear thresholds, and full data lineage.
Measure what matters. Time‑to‑offer, touches per file, extraction accuracy, rework rates, and customer/broker satisfaction.
Iterate and chain. Once the first steps are stable, add policy checks, product screening, and richer decision support—always with human oversight and clear governance.

Fully automated underwriting is not science fiction anymore; it’s technically within reach. The question is not if you get there, but how—and whether you choose a path that builds trust, withstands regulatory scrutiny, and delivers real operational value at every step along the way.