RegTech's data problem: why structured data is key to automation

Regulation is accelerating. AI is advancing. But the missing layer in #RegTech isn’t another platform or dashboard, it’s structured, machine-readable regulatory data. At #RegGenome, we believe compliance automation won’t scale until regulation itself is transformed into an infrastructure layer: 🔎Granular, obligation-level data consistently tagged and versioned 🔗 Interoperable with RegTech, GRC, and AI pipelines 🧩 Built for integration, not locked inside proprietary platforms This is why AI-ready data matters. Without it, AI outputs are inconsistent, untraceable, and can’t be trusted in compliance. With it, #RegTech providers can deliver defensible, scalable, enterprise-grade automation. We’ve outlined the case - and the ROI for solution providers - in our latest piece: Why AI-Ready Data Is Critical Now: https://lnkd.in/gBBwrPPw 👉 Swipe through the carousel for 4 reasons structured data unlocks RegTech growth. #RegTech #Compliance #AI #GenerativeAI #FutureOfCompliance #AIinFinance #RegulatoryData #ComplianceAutomation

The regulatory data ecosystem doesn’t have a tooling problem. It has a data problem. Unstructured regulation makes AI outputs unreliable. Structured, machine-readable data makes them scalable and defensible. That’s why RegGenome exists.   Why AI-Ready Data Is Critical Now: https://lnkd.in/eapXgv7d

When we talk to RegTech teams, the same issue comes up again and again: before you can build AI features, you’re stuck dealing with fragmented, unstructured regulation. Scraping regulator sites. Manually tagging obligations. Stitching together content from inconsistent sources. It slows roadmaps, blocks scale, and erodes client trust. This is the bottleneck. And it’s why AI-ready regulatory data matters. At RegGenome, we transform raw regulation into:  • Standardised, machine-readable data – consistent across jurisdictions  • Obligation-level tagging & versioning – fully traceable back to source  • Integration-ready formats – built for APIs, AI pipelines, and solution platforms The result?  ⚡️ Faster delivery  🌍 Easier scale 🔒 Defensible outputs  🤖 AI features that actually work in production We’ve laid out the case in our latest blog: Why AI-Ready Data Is Critical Now. https://lnkd.in/e9gzCYfq

Our applied AI is making serious progress towards machine readable rulebooks. if you are interested in financial regulations this is worth taking a look at...

As AI takes on critical decisions, accuracy is no longer enough. Trust is. Enterprises need systems that follow policy, adapt to regulation, and withstand scrutiny without slowing operations. That is the role of RegOps (Regulatory Operations): embedding compliance into workflows so governance becomes automatic, auditable, and scalable. The signals are clear: -$130B+ TAM for Governance, Risk and Compliance software by 2030 -AI governance spend rising from less than $1B today to $5 to 15B by 2030 -EU AI Act, US executive orders, UK and Asia frameworks setting global standards -Fortune 500s appointing Chief AI Governance Officers and building compliance teams One of the most compelling examples of RegOps in practice is ndaOK, a Venture Forward Capital portfolio company. ndaOK transforms contract workflows, enabling enterprises to make faster decisions without sacrificing trust. As ndaOK CEO James Weir told us: “Speed and trust don’t have to be in conflict. With the right infrastructure, they reinforce each other.” The implications go far beyond compliance. RegOps expands margins by replacing manual review with automation. It strengthens defensibility, turning trust into a strategic moat. This is why RegOps is more than compliance tooling. It is the trust layer of the AI economy. Mike Schatzman, Ohad Tzur, Pankaj Kedia, Julie Bevacqua and the team at Venture Forward Capital believe RegOps is one of the most important infrastructure shifts of the decade. #AIInfrastructure #RegOps #TrustAtScale #AI #VerticalAI

As AI takes on critical decisions, compliance can’t be an afterthought. That’s the role of RegOps: embedding governance directly into workflows so enterprises can move fast and stay within policy. ndaOK, led by CEO James Weir, is showing how RegOps works in practice—transforming contract workflows so speed and trust reinforce each other. Kasra Davar shares more on why RegOps is set to become one of the most important layers of AI infrastructure: https://lnkd.in/gR9gBAdi

You Can’t Build the Future Without Trust. In a world shaped by artificial intelligence, one thing matters more than innovation: responsibility. At Finally Free Productions, we believe secure and compliant AI isn’t optional — it’s foundational. Our latest insights explore how we design AI applications in 2025 that are not just powerful, but ethical, private, and regulation-ready from day one. 🔐 Here’s how we build trust into every line of code: Security-first systems: From zero-trust architecture to MFA and API hardening Global compliance readiness: GDPR, CCPA, HIPAA, and the EU AI Act — built in Explainable + ethical AI: Human-in-the-loop, audit trails, and bias checks Integrated DevSecOps: Secure CI/CD, threat monitoring, rollback protections Transparent data governance: Clear lineage, synthetic data, responsible models Because trust isn’t a feature. It’s the future. If you’re developing AI solutions in healthcare, finance, government, or any high-impact sector — this is your blueprint for building smarter, safer, and more human-centered technology. 👉 Dive into the full article: https://zurl.co/GWtsg #SecureAI #AICompliance #EthicalTech #FutureReady #FinallyFreeProductions #FFPInsights

Free Webinar: Building Trust in AI: The Role of Synthetic Data in Privacy & Risk Compliance As AI adoption accelerates across the public and private sectors, the challenge of using real data responsibly and legally is greater than ever. Join us for this exclusive session to learn how synthetic data is enabling banks, insurers, regulators, and enterprises to explore data-driven innovation while maintaining privacy and compliance. In this webinar, you’ll discover: * What synthetic data really is and how it differs from anonymized or masked data * Real-world use cases in banking, insurance, investments, and the public sector * A demo of Betterdata: a leading privacy-enhancing synthetic data platform * Why this matters for AI readiness, regulatory sandbox projects, and internal innovation This session is ideal for: * Data Privacy Officers * Risk and Compliance Heads * Technology and Innovation Leaders * Regulatory and Supervisory Teams Seats are limited. Register now to explore how to build trust in AI—starting with safer data. Presented by i-Metrics Asia-Pacific Corporation, a distributor of Betterdata in the Philippines. Register here https://lnkd.in/g5Xef37a

Last year, our teams at Intuit asked a tough question: Can we move faster because of governance—not despite it? Turns out the answer is yes. 🚀 Here’s what unlocked our velocity: •GenSRF Controls → safeguards designed-in from development phase. •Risk-Based Routing → low-risk AI moves fast; deeper reviews only where needed. •Platform→ A platform connecting these services and for tracking . •Evals & Dashboards → drift caught early with clear ownership. With the EU AI Act’s official regulatory framework and recent Reuters guidance on systemic-risk AI compliance, plus today’s FTC case against “Air AI”, the message is clear: speed belongs to teams who make governance programmable. 🔎 On standards, NIST’s Control Overlays for Securing AI Systems offer a blueprint for building-in measurable AI safeguards. We are taking steps towards Responsible AI as a system of practices, tools, and culture that makes trust the natural outcome of how we build instead of a final check. 👉 what’s the one practice that’s helped your org ship responsibly without slowing down? #ResponsibleAI #AIGovernance #Intuit#EUAIAct #NIST

We often assess whether our systems are robust enough for scale and forget that resilience isn’t only a system question but also a governance one. Control and accountability at the data layer are vital. The techUK article Robust AI Governance as a Path to Organisational Resilience gets right to the point: governance is a business-critical function that frames how organisations manage risk, comply with emerging AI and data regulations, and ensure operational continuity. For CISOs and their peers, the key question is shifting from “Is the data secured?” to “Can we prove how it’s being accessed, by whom, and why, across all environments, including AI pipelines?” This is where Self-Sovereign Data™ becomes foundational. By keeping data at source (zero-copy), embedding access policy logic directly into the data asset, and enforcing access through portable permissions, governance becomes continuous, tamper-proof, and scalable across jurisdictions. It’s a model built for the governance mandates of 2025 and beyond: continuous, provable control, even outside the perimeter. #SelfSovereignData #datagovernance #datasecurity #confidios