Repodo raises €8.2M - AI does the repetition, auditors keep the liability. In its August 25 funding announcement, Copenhagen-based Repodo disclosed an €8.2 million pre-seed round led by Hedosophia and Seed Capital to launch an AI-native authorised audit firm in Denmark. The initial market is Danish small and medium-sized businesses, with European expansion presented as a later, market-by-market plan. Former Lunar executives Ken Villum Klausen, Peter Andreasen and Joachim Strøjer Hansen founded Repodo with auditor Anders Houmann. Its proposed division of responsibility is explicit: software processes repetitive audit work, but qualified auditors retain review, risk assessment, professional judgement, oversight and final sign-off. The financing supports a defined Danish launch. The new capital is intended to develop Repodo's technology platform, build its team and support the launch in Denmark. It does not constitute financing for a simultaneous rollout across Europe, and the company has not identified its next national market. Klausen, Andreasen and Hansen previously held the CEO, CFO and CPO roles at Nordic digital bank Lunar. Houmann supplies specialist audit experience to a founding team attempting to operate an authorised audit firm rather than sell a standalone software tool to established firms. Sifted's account of the launch says Repodo had grown to a team of 20 and become an authorised audit firm, while also confirming that the platform covers data collection, reconciliations, documentation and transaction analysis. Those details indicate that the funding supports both software development and the professional organisation required to deliver audits. "AI-native" therefore describes how Repodo is designing its technology, workflows and customer experience from the outset. It does not establish that the firm has already made audits faster or cheaper: no public pricing, completed-audit volumes or comparative performance figures accompanied the financing announcement. Automation prepares evidence but does not settle the opinion. Repodo assigns automation to evidence-heavy stages where records are collected, entries reconciled, documentation organised and transactions analysed. Routine matches can be processed consistently, while exceptions and complex issues are reserved for professional attention. That division is narrower than the claim that AI performs an audit autonomously. A reconciliation may show that two supplied datasets agree, but agreement alone does not determine whether the records are complete, whether the procedure addresses the relevant risk or whether conflicting evidence changes the conclusion. The same boundary applies to transaction analysis. Software can classify records and surface unusual items, but significance remains a matter for an auditor who understands the engagement, evaluates the available evidence and decides what additional work is required. Repodo has not publicly detailed its integrations, exception thresholds, model-validation methods or escalation rules. "Liability" remains attached to the professional decision. In the headline, liability is shorthand for the professional accountability Repodo assigns to qualified auditors; it is not a claim that the company has published contractual liability terms. The disclosed model leaves judgement, oversight and the signature behind the audit opinion with licensed professionals rather than transferring those functions to software. This makes the final sign-off more than an administrative click. The auditor must decide whether the work performed and evidence obtained support the conclusion, including how unresolved, contradictory or incomplete information affects the opinion. AI can organise the material or flag an exception, but it does not become the professional approving that conclusion. The allocation also sets a limit on the meaning of "AI-native." Repodo is proposing a technology-assisted statutory-audit process, not an unsupervised service. The company has yet to publish how auditors will test automated outputs, document their review, sample processed items or respond when the system produces an uncertain result. European ambition extends beyond the funded operation. Denmark is more than a test market: it is the only funded launch that Repodo has described in operational terms. A FinTech Futures report on the company's registration says Repodo was approved and registered with the Danish Business Authority on August 21, 2026, and was preparing to hire and onboard its first customers. The wider European ambition remains less specific. Repodo has not named the country that would follow Denmark, provided a timetable for another launch or disclosed how much funding would be allocated to expansion outside its home market. That distinction matters because a portable technology platform does not by itself create an operational audit firm in every jurisdiction. National authorisation, reporting practices and market-specific requirements still have to be addressed, and Repodo has not disclosed approvals outside Denmark. The operating model now has to produce evidence. The confirmed event is a substantial pre-seed financing tied to a Danish launch, not proof that Repodo has outperformed conventional audit firms. The founders, lead investors, first market and high-level allocation between automation and professionals are known; customer numbers, completed audits, quality measures and pricing are not. Early engagements will show how reliably records enter the system, how exceptions reach auditors and how professional review is documented. Until Repodo publishes those operating details and measurable results, its central proposition remains clear but unproven: AI handles more repetitive processing, while qualified auditors keep the judgement, oversight and sign-off that give the audit opinion its professional consequence.

Lunar bank co-founders have secured €8.2 million ($9.6 million) in funding for their new venture: an artificial intelligence-powered audit firm. The Danish entrepreneurs are aiming to disrupt the auditing industry, which remains heavily dependent on manual processes. The startup plans to use AI technology to transform traditional auditing practices. The founders believe there is significant opportunity to modernise an sector that has been slow to adopt automation and digital tools. This marks a new chapter for the Lunar co-founders as they shift focus from digital banking to professional services. Their bet is that AI can bring efficiency and innovation to auditing work that has long been done manually.

AI becomes the banker: Decision-making and the new testing burden. This three-part series explores how AI is reshaping banking end-to-end, and the growing testing, risk and assurance challenges facing QA teams as deployment moves from pilot to production. Today Part III. Please click here for the first instalment and part II. If the first phase of AI in banking transformed interfaces, and the second embedded intelligence into operational control, the third is pushing AI to the very edge of decision-making, where money moves, credit is granted and customer intent is interpreted in real time. This is the most consequential stage of AI adoption. Here, systems are no longer assisting processes; they are directly shaping financial outcomes. Voice assistants are replacing menus, messaging apps are becoming payment rails and AI models are determining who gets access to credit and at what cost. For QA and software testing teams, this represents a step-change in responsibility. Testing is no longer about validating workflows or even complex system interactions, it is about assuring outcomes in environments where inputs are unstructured, behaviour is probabilistic and errors carry financial, regulatory and reputational consequences. The challenge is compounded by scale. These systems are designed to operate continuously, across millions of users, often without human intervention. That makes observability, explainability and fail-safe design critical components of any testing strategy. "2024 was a pivotal year for AI in banking, as institutions worldwide moved from pilot projects to real deployments that tangibly improved the digital customer experience," stressed Alex Kreger, founder and CEO of UXDA Financial UX Design. He added that banks observed AI could "boost both customer satisfaction and operational performance - a true win-win." Voice and conversational banking go mainstream. Lunar, the bank that launched Europe's first GenAI-powered voice assistant for banking, uses a voice-native GPT-4 model to hold natural conversations and can handle both simple and complex requests, including interruptions and follow-ups. This type of interface introduces entirely new testing requirements, according to Kreger. QA teams must validate not just functional outcomes, but conversational flow, intent recognition across accents and languages, and the system's ability to recover gracefully when misunderstandings occur. Meanwhile at Nubank, AI has been embedded across operations through the use of OpenAI models. Kreger said the bank is enhancing "both customer experience and operational efficiency," with AI handling 55% of Tier 1 inquiries and reducing response times by 70%. The same bank also pushed AI into payments through Pix integration. Kreger said customers can send payments using "voice, text or even images through WhatsApp, with AI handling the interpretation and execution." For QA teams, this is a fundamental shift. Payment initiation is no longer tied to structured input fields but to natural language and even visual cues, requiring entirely new validation models to ensure accuracy, consent and security. "Institutions worldwide move from pilot projects to real deployments." - Alex Kreger At Upstart, AI-driven underwriting is redefining how credit decisions are made. Kreger said its models enabled "44% more loan approvals at 36% lower average APRs for the same risk level," compared with traditional approaches. He added that many loans are now approved instantly with no human intervention, demonstrating how AI can simultaneously increase efficiency and expand access to credit. However, this also introduces one of the most complex testing challenges in financial services. AI-driven credit decisions must be tested not only for performance, but for fairness, bias, transparency and regulatory compliance. Models must be explainable, auditable and resilient to drift over time. Predictive and proactive systems. At Commonwealth Bank of Australia, AI is being used to reduce fraud and scam losses through tools like NameCheck and CallerCheck. Kreger said the bank achieved "a 50% reduction in scam losses, a 30% drop in fraud cases and a 40% decrease in call center wait times." At Capital One, the Eno assistant has expanded into voice and chat-based banking, handling routine requests while providing proactive insights. Kreger said it reduced "call center contact volumes by 50%" while improving response speed and consistency. These cases highlight a key trend: AI is no longer reacting to fraud, it is anticipating it. That requires QA teams to test systems against evolving threat patterns, adversarial behaviour and complex real-world scenarios that cannot be fully replicated in controlled environments. ""Across the global banking industry, AI has shifted from pilot to profit generator." - Alex Kreger As AI moves into payments, credit and real-time decisioning, the testing burden increases sharply. Systems must not only perform correctly, they must behave responsibly under uncertainty. Kreger acknowledged the challenges, noting that "banks had to address customer trust and security concerns around AI," and that "incidents of AI errors, or 'hallucinations', were taken seriously, and institutions implemented guardrails and testing to maintain accuracy." He added that the human element remains essential, with leading banks combining AI with human oversight to ensure that customers can escalate when needed. A new QA mandate. The final set of case studies makes one thing clear: AI is no longer a feature within banking systems, it is becoming the system itself. From how customers interact with their bank, to how decisions are made and risks are managed, AI is now embedded across the entire value chain. "Across the global banking industry, AI has shifted from pilot to profit generator," Kreger said. "Intelligent tools now accelerate back-office work, raising employee productivity and shrinking response times, while smarter analytics empower advisors to deliver faster, better-informed guidance," he added. Kreger also pointed to the scale of impact, noting that instantaneous self-service resolves most issues in under two minutes, trims wait times by 40 percent and cuts repetitive contact by 55 percent, while AI-driven systems improve fraud detection and expand access to credit. The implication for QA and testing teams is profound. Assurance can no longer be retrospective. It must be continuous, embedded and aligned with both technological and regulatory expectations. As Kreger concluded: "The 'age of AI' in banking has truly begun," and the institutions that succeed will be those that can not only deploy AI at scale, but prove, consistently and rigorously, that it works as intended. WHY not become a QA Financial subscriber? It's entirely FREE Watch now. May 15, 2026 May 14, 2026 May 13, 2026 May 12, 2026