Italy is making a bigger move in payments, and fintech founders should pay attention.

Italy’s state investor, CDP Equity, plans to increase its stake in Nexi to 29.9%, stopping just short of the threshold that would trigger a mandatory takeover bid under Italian rules. On paper, it looks like a shareholder story. In reality, it feels closer to a strategic infrastructure decision.

A Payments Company Under Pressure

Nexi is one of Europe’s largest payments companies, processing around €1.8 trillion in digital transactions across 25 countries. It expanded quickly after its 2019 IPO through acquisitions and became one of the continent’s major payment players.

But scale has not insulated the business from pressure.

Nexi’s share price has fallen sharply from post-pandemic highs. Investors have become more cautious about the long-term outlook for payment companies as technology shifts faster, competition increases, and margins face pressure. The company has also dealt with leadership changes and repeated private equity interest, including reported attention from investors looking at taking it private.

There is also a shareholder story happening in the background. Private equity firms that helped shape Nexi’s growth have gradually exited. Bain and Advent have already left, while Hellman & Friedman, one of Nexi’s major investors, appears set to lose its position as the company’s biggest shareholder.

Why CDP Equity Is Stepping In

CDP Equity’s move looks like an attempt to stabilise Nexi’s shareholder base at a difficult moment.

When a company becomes strategically important, governments and state-backed investors tend to think differently. They stop looking only at quarterly performance and start asking bigger questions. Who controls the infrastructure? What happens if ownership becomes unstable? Does this asset matter for national or regional competitiveness?

Payments increasingly fall into that category.

We often talk about roads, energy grids, or telecom networks as infrastructure. Digital payments are starting to sit in the same conversation. They are deeply connected to commerce, banking, and increasingly to Europe’s ambitions around financial independence.

CDP Equity has already described Nexi as important to Europe’s future development of digital money. That wording matters. It suggests Italy sees Nexi as more than a listed payments company. It sees it as part of long-term financial infrastructure.

What Fintech Founders Should Watch

For fintech startups, this story is less about Nexi itself and more about the direction of travel.

Large incumbents backed by stable ownership can become more reliable partners. At the same time, they can become slower, more process-heavy, and less willing to take risks. That creates room for startups solving highly specific problems around payments, merchant operations, fraud prevention, reconciliation, compliance, or customer experience.

There is another lesson hiding in plain sight.

Payments no longer behave like a pure software market. Regulation, politics, shareholder pressure, and infrastructure priorities increasingly shape the sector. Founders building in payments need to think about all of those forces, not just product and growth.

Here are a few practical things worth keeping in mind:

Key takeaways for fintech startups

• Payments infrastructure is becoming more strategic in Europe, which may create more stable but slower-moving incumbents.
  
  
• Weak public valuations and shareholder pressure can create partnership and acquisition opportunities.
  
  
• Founders in payments should think beyond technology and consider regulation, ownership structures, and infrastructure priorities.

If your fintech startup is trying to make sense of industry shifts like this, Your Fintech Story can help turn market signals into practical growth decisions. Contact us to discuss your strategy.

Flexprice has raised a $1.5 million seed round led by Shastra VC, with participation from TDV Partners and Anupam Mittal. The company is betting that billing, once a quiet back-office function, is becoming one of the most fragile parts of the AI stack. The new capital will go toward US and Europe expansion, alongside product development in metering, revenue recognition, and AI-native financial workflows.

Billing stopped being simple when AI pricing arrived

SaaS billing used to be straightforward. Monthly subscriptions, predictable tiers, and the occasional usage add-on gave finance teams something stable to model. AI broke that rhythm. Pricing is now tied to tokens, API calls, GPU usage, compute time, and sometimes even outcomes. A single product can carry multiple pricing dimensions depending on how it is used, which turns billing into something closer to a real-time system than an accounting function. For product teams this is flexibility. For finance teams it quickly becomes operational friction.

Why Flexprice is building infrastructure, not billing tools

Flexprice is an open-source billing and metering platform designed for AI and API-first companies. Instead of treating billing as a layer on top of the product, it moves closer to the infrastructure that captures and processes usage data. The company says it already processes more than 20 billion events per month and grew revenue six-fold in the last quarter, serving companies working with GPU usage, token consumption, and hybrid pricing models. The direction is clear: billing is no longer a back-office concern but part of the core system that defines how AI companies actually generate revenue.

The harder problem behind pricing models

Investors and founders describe the same structural issue. Traditional billing systems were built for predictable subscription logic, while AI companies operate on continuous, high-volume usage events that need to be priced and reconciled in real time. Flexprice’s broader ambition, described as full revenue automation, is to connect the full chain from usage event to revenue recognition. That includes tracking, pricing, invoicing, and accounting logic in one flow, instead of stitching together multiple disconnected systems.

Key takeaways

• AI pricing has shifted from fixed subscriptions to usage-heavy, multi-variable models
  
  
• Legacy billing systems struggle with real-time, event-based usage data
  
  
• Flexprice is building open-source infrastructure for billing and metering in AI companies
  
  
• The platform processes 20B+ events per month and grew revenue 6x last quarter
  
  
• Billing is moving from back-office tooling to core product infrastructure
  
  
• The real challenge is aligning usage data with accurate revenue recognition

If you want to position your product, messaging, or growth strategy around these trends, Your Fintech Story helps startups turn complex ideas into clear market narratives and traction. Contact us.

Scapia has raised $63 million in a round led by General Catalyst, with continued participation from Peak XV Partners and Z47. The company plans to use the capital to expand its product suite, scale across India, and accelerate its shift toward building AI-native products and teams.

The round comes at a moment when Scapia is operating at the intersection of two sustained trends in India: rising discretionary travel among younger consumers and rapid adoption of embedded financial products. The company is positioning itself as a travel-first financial platform rather than a card or payments product with travel add-ons.

Building around travel as a default behaviour

Scapia’s core positioning has remained consistent since launch. The product is built around the assumption that travel is no longer an occasional purchase for a segment of users, but a regular consumption pattern for a younger demographic.

This shows up in how the platform is structured. Instead of treating travel as a booking flow attached to payments, Scapia integrates financial incentives, rewards, and travel discovery into a single loop. The credit card sits at the centre of that loop, with travel benefits designed to influence day-to-day spending behaviour.

The company reports strong growth across its core categories, with flight bookings increasing 5–6x and stays growing 8x compared to the previous year. A growing share of this demand is coming from Tier-2 and Tier-3 cities, where digital financial adoption and travel intent are expanding in parallel.

Expanding from payments into a broader travel stack

Over the past year, Scapia has steadily expanded beyond its initial credit card use case. The platform now includes Scapia Pay, a rewards-led UPI experience, add-on cards, BBPS-based credit card bill payments, and a set of travel and lifestyle features including Scapia Store and Scapia Experiences.

At the centre of this expansion is Scapia Coins, a unified rewards system that can be earned across domestic spending and redeemed across multiple travel categories including flights, trains, buses, stays, and visas. The aim is to reduce fragmentation in how users accumulate and use travel value across the ecosystem.

The company has also pushed into airport-linked experiences beyond traditional lounge access. Its Airport Privileges offering extends into dining, retail, and duty-free benefits. According to the company, one in three users now prefer these alternatives over standard lounge access, indicating a shift in how premium travel benefits are being consumed.

Capital allocation toward AI and product scaling

A significant portion of the new funding will be directed toward building what Scapia describes as an AI-first organisation. This includes hiring across engineering, product, data science, and design, alongside integrating AI into core product development and internal workflows.

India’s large Gen Z population is central to this strategy. The company sees this group as both a primary customer base and a talent pipeline that is already familiar with AI-native tools and workflows. The goal is to reflect that behaviour in how the product is built and personalised over time.

General Catalyst’s Neeraj Arora noted that Scapia identified early how travel is becoming a baseline expectation for younger consumers rather than an aspirational category. Peak XV Partners highlighted the company’s execution across payments, rewards, and travel benefits as a key driver of continued conviction. Z47 pointed to the pace of product development, including dual-network cards and airport-linked features, as evidence of strong execution discipline.

Scaling a travel-fintech model in India

Scapia operates co-branded credit cards in partnership with Federal Bank and BOBCARD, including India’s first dual-network co-branded card spanning Visa and RuPay. It also maintains a zero forex markup structure for international spending, positioning itself in a niche where travel and financial utility overlap directly.

The company’s distribution footprint now spans users across 17,500+ pincodes and acceptance across millions of merchants globally. This scale reflects the broader trajectory of travel-fintech in India, where financial products are increasingly being designed around lifestyle categories rather than standalone transactions.

With this round, Scapia is betting on continued convergence between travel behaviour and financial products, and on its ability to expand that model into a larger, AI-enabled consumer platform.

Key takeaways

• Scapia is shifting from a travel credit card product into a broader travel-fintech ecosystem with multiple consumer entry points
  
  
• The product is structured around a closed loop: spending → rewards → travel redemption → re-engagement
  
  
• Expansion beyond cards (UPI, experiences, bill pay, airport privileges) signals push toward a full travel stack
  
  
• AI is being positioned as a core org and product layer, not just a feature set
  
  
• Distribution scale across pincodes and global acceptance is becoming a key moat layer alongside product depth

If you’re building in fintech, reach out. We can help you shine.

Moment raised 78 million dollars in a Series C led by Index Ventures, less than a year after its Series B.

On the surface, it looks like fast fundraising momentum. The more important signal is where adoption is happening.

Large wealth firms including Edward Jones, LPL Financial, and Hightower Advisors are now building on Moment. Together, these institutions manage more than 10 trillion dollars in client assets on the platform.

That level of concentration is not typical for early AI infrastructure companies. It suggests the product is moving beyond experimentation and into core operational usage inside large financial institutions.

Investment management software was built as a patchwork

Investment management systems have historically evolved as disconnected layers rather than unified platforms.

Portfolio construction tools were built separately from trading systems. Tax optimization was handled in different software. Compliance operated in parallel. Reporting and reconciliation were added on top as additional layers.

None of these systems were designed to operate together in real time. The result was a fragmented architecture where human workflows became the integration layer between tools.

That structure worked when investment processes were slower and more manual. It becomes increasingly fragile when firms try to introduce automation and AI into production workflows that span multiple systems.

Moment is attempting to replace the system boundary itself

Moment positions itself as an AI operating system for investment management rather than a point solution within it.

The goal is to unify trading, portfolio management, compliance, and execution inside a single environment where AI agents can operate across the full workflow.

Within that system, firms are deploying portfolio construction agents that generate investment proposals from natural language inputs in seconds. They are also using multi asset optimization engines that run portfolios across equities, fixed income, and currencies with tax aware constraints.

Other applications include surveillance systems that scan large account sets for risk, tax, and transition opportunities, compliance agents that evaluate transactions in real time against firm defined rules, and execution systems that coordinate orders across asset classes with routing logic embedded directly into the platform.

The common thread across these capabilities is not the individual features. It is the shared infrastructure layer that allows them to operate in a regulated environment.

The constraint is governance, not intelligence

Large financial institutions already have access to advanced models and AI systems. The limiting factor is not capability, but control.

Investment management requires strict constraints. Every decision must be auditable, explainable, and compliant with firm specific rules. It must also remain consistent across systems that touch trading, portfolio construction, tax logic, and reporting.

Without a unified foundation, AI systems remain isolated in advisory or experimental layers. They cannot safely operate in core workflows where capital is actually deployed.

Moment’s architecture is built around this constraint. It emphasizes a unified data model and regulatory grade controls that allow AI agents to operate inside production environments with governance built in from the start.

This is what separates AI as a feature from AI as infrastructure.

Procurement behavior is changing in large wealth firms

The most important shift is not technical but organizational. Large wealth managers are consolidating vendors instead of expanding fragmented toolchains.

Rather than maintaining multiple overlapping systems, firms are increasingly standardizing on fewer platforms that can support broader end to end workflows.

The growth from 300 billion dollars to more than 10 trillion dollars in assets connected to Moment reflects this consolidation trend. Once trading, compliance, and portfolio construction are centralized in a single system, switching costs increase significantly.

At that point, the relationship between vendor and institution shifts from software usage to infrastructure dependency.

Conclusion

Moment operates in a category that is still forming. It is not competing primarily on features or interfaces. It is competing on system architecture.

If AI becomes embedded in how portfolios are constructed, optimized, and executed at scale, the infrastructure layer becomes the primary control point in investment management software.

Key Takeaways

• Moment raised 78 million dollars shortly after its Series B, driven by rapid adoption inside large institutions
  
  
• Major wealth firms including Edward Jones, LPL Financial, and Hightower Advisors are already building on the platform
  
  
• Investment management software is shifting from fragmented point solutions to unified infrastructure
  
  
• Moment positions itself as an AI operating system for core investment workflows across trading, portfolio management, compliance, and execution
  
  
• The main constraint to AI adoption in wealth management is governance and regulatory control, not model capability
  
  
• Large institutions are consolidating vendors and standardizing on fewer core systems
  
  
• Infrastructure platforms are becoming more central than standalone tools in modern investment management

If you are building or scaling a fintech product, Your Fintech Story can support your strategy, positioning, and growth roadmap. Reach out to explore how to turn market opportunity into execution.

Banking has stayed oddly consistent while everything around it has moved on. You still deposit money, move it around, and rely on external tools to understand what it all means. For founders running modern companies, that gap shows up in the small moments that slow everything down: reconciling transactions, exporting spreadsheets, and trying to connect financial data that never quite sits in one place.

It’s not that banks don’t work. It’s that they don’t really help you think.

Why Mercury started in 2017

Mercury was founded in 2017 with a simple frustration at its core. If money sits at the center of every business decision, why does banking stop at storage and transfers? The goal was to build something that understands context, not just balances. Something that behaves less like infrastructure you occasionally visit and more like a system that actively helps you run a company.

That idea has become more relevant as companies themselves have changed shape, especially with AI lowering the barrier to starting something new.

The $200M Series D and what it signals

Mercury is now announcing a $200 million Series D at a $5.2 billion valuation, led by TCV, alongside returning investors including Andreessen Horowitz, Coatue, CRV, Sapphire Ventures, Sequoia Capital, and Spark Capital.

The timing matters. Company formation is accelerating again, driven by AI tools that make it easier to go from idea to product. Mercury reports a significant increase in applications, alongside a broader shift in who is starting companies and how quickly they move from concept to incorporation.

A customer base that no longer fits one category

Mercury now serves more than 300,000 customers, including roughly one in three U.S. startups. But the more interesting shift is outside the startup world.

A majority of new customers now come from outside traditional tech. Ecommerce businesses, service firms, solo operators, and hybrid income profiles are all using the same infrastructure. Banking, in that sense, is no longer a “startup tool.” It’s becoming general-purpose financial operating infrastructure.

From account to operating layer

Over the past year, Mercury has started to evolve beyond being a place where money sits.

Mercury Insights brings real-time financial visibility directly into the product, removing the need for external reporting tools. Developer-focused capabilities like MCP and CLI extend banking into environments where technical teams already work. Payroll is being integrated through the acquisition of Central, and Mercury Personal expands the same experience into individual finances for qualifying users.

The direction is subtle but important. The product is shifting from passive visibility to active participation.

AI changes the interface, not just the tooling

The next step is Mercury Command, an AI-native interface designed to reduce the friction between intent and execution. Instead of navigating dashboards or stitching together financial workflows manually, users will be able to describe what they want in natural language and have it executed inside their account.

Check cash position. Move funds. Categorize transactions. Send invoices. The key difference is that everything remains grounded in real account data, and every action still requires explicit user approval. AI becomes an interface layer, not an autonomous operator.

Moving closer to becoming a full bank

Alongside product expansion, Mercury is progressing on a more structural shift. The company has received conditional approval from the OCC to establish Mercury Bank, N.A., moving it closer to becoming a fully regulated national bank.

Further approvals from the FDIC and Federal Reserve are still required, but the direction is clear: deeper ownership of the financial infrastructure behind the product. That unlocks capabilities like expanded lending, payments, and integrations that are harder to build on top of legacy banking systems.

Key takeaways

• Mercury raises $200M Series D at a $5.2B valuation led by TCV
  
  
• AI is accelerating company formation and expanding the pool of founders
  
  
• Banking is shifting from passive storage to active financial operating infrastructure
  
  
• Mercury is broadening beyond startups into a general business banking platform
  
  
• The company is moving toward full bank status with OCC conditional approval

If you are building in payments or scaling a financial product, this shift is worth paying attention to. Reach out if you want to explore how infrastructure choices shape growth.

A few days ago, OpenAI announced a new personal finance experience inside ChatGPT powered by Plaid. U.S. Pro users can now connect their financial accounts and ask questions based on their actual financial situation. Not generic budgeting advice. Not “5 tips to save money.” Real-time answers grounded in someone’s own accounts, spending habits, debt, cash flow, and financial goals.

That changes the conversation quite a bit.

For years, AI in personal finance mostly lived in the “assistant” category. Budget reminders. Spending alerts. Predictive charts nobody looked at after week two. This feels closer to AI becoming the interface itself.

Instead of opening five banking apps and manually piecing together what is happening financially, users can simply ask: “Can I realistically afford a house next year?” or “What is the fastest way for me to pay off debt without killing my savings?” That sounds small on paper, but in reality it changes user expectations completely.

The Most Important Part Is Not The Chatbot

The real story here is infrastructure.

Financial data is messy. Extremely messy. Transaction descriptions are inconsistent, categories are often wrong, and most raw banking data is borderline unreadable unless heavily processed. One transaction says Starbucks. Another says SQ*TST12345. Another looks like someone smashed their keyboard.

Plaid is trying to position itself as the layer that translates all of that chaos into something AI can actually understand. And honestly, this is where the announcement becomes strategically interesting.

A lot of companies can build AI interfaces now. Far fewer companies have trusted access to financial data, institution connectivity, transaction intelligence, and consumer permission systems all working together. That combination is much harder to replicate than a chatbot UI.

Banking UX Suddenly Looks Old

Traditional banking apps already felt clunky before this. Now they risk feeling prehistoric.

Most banks still rely on dashboards designed around navigation. Tabs. Menus. Filters. Static charts pretending to be insights. But consumers are quickly getting used to conversational software that gives direct answers instead of forcing them to hunt for information.

Nobody wants to spend twenty minutes analyzing spending trends manually if they can ask one question and get a contextual explanation instantly. The last decade of fintech focused heavily on convenience. The next phase probably focuses on interpretation. Helping users understand what is actually happening with their money and what actions make sense next.

That is a much harder problem.

The Pressure On Fintech Just Increased

This also quietly raises the bar for every fintech product in the market. Once users experience financial tools that understand context deeply, generic experiences start feeling shallow very quickly.

Consumers will increasingly expect products to understand their full financial picture, personalize recommendations in real time, explain tradeoffs clearly, proactively surface useful actions, and adapt to changing financial behavior automatically.

And importantly, they will expect this everywhere. Inside neobanks. Inside lenders. Inside investment apps. Inside accounting platforms. Inside payroll tools.

The companies that win from this shift probably will not be the loudest AI brands. They will be the ones that make financial complexity feel simpler, calmer, and genuinely useful without crossing the line into creepy automation.

Key Takeaways

• AI in fintech is moving from generic guidance to context-aware financial intelligence
  
  
• Plaid is evolving from connectivity provider into AI infrastructure layer
  
  
• Conversational finance may replace traditional dashboard-heavy banking UX
  
  
• Consumer expectations around personalization are about to rise fast
  
  
• Fintechs now face pressure to make products smarter, not just prettier

If you’re building a fintech startup and want help refining positioning, messaging, or growth strategy, reach out.

Healthcare payments are still negotiated in the dark. Most providers feel it every day.

Anomaly just raised $17M in new funding, bringing total capital raised to $34M. The round was led by Sound Ventures, with participation from Alumni Ventures and existing investors including Link Ventures, Redesign Health, and RRE Ventures. The company is focused on one problem: providers do not actually see how payers behave at scale.

A system built on blind spots

Healthcare providers lose billions every year through denials, underpayments, downgrades, and delayed reimbursements. On paper, contracts define what should be paid. In reality, payments often deviate due to policy updates, interpretation shifts, and layered review systems.

Most health systems only see the outcome of a claim, not the pattern behind thousands of similar decisions. Payers, meanwhile, operate with far more structured intelligence infrastructure. That creates a persistent imbalance in visibility.

What Anomaly actually changes

Anomaly analyzes billions of healthcare transactions in real time. The goal is not just to flag denied claims, but to connect them into behavior patterns across payers.

It tracks how reimbursement decisions shift across claims activity, contract terms, policy changes, and adjudication behavior over time. Instead of reacting to denials one by one, providers can start seeing how those denials are generated in the first place.

The same dataset is used in two places: day-to-day revenue cycle operations and higher-level managed care negotiations.

From fixing claims to understanding payers

Early results suggest the impact goes beyond operational cleanup. Anomaly has helped recover tens of millions in revenue and contributed to measurable changes in payer behavior across health systems, labs, and RCM organizations.

More than 20 health systems are now using the platform, including large providers with over $4B in annual net patient revenue.

The shift is subtle but important: from managing denials to understanding payer behavior as a system.

Why this matters in practice

For many provider organizations, payer negotiations and claim operations still live in separate worlds. That separation limits what teams can see and act on.

When patterns become visible, conversations with payers also change. Discussions move away from individual claims and toward repeated behavior.

Internally, it also changes how teams prioritize work. Not everything looks like an isolated issue anymore.

Key takeaways

• The core shift is from reacting to denials to understanding payer behavior in real time
  
  
• Healthcare reimbursement is driven by payer behavior patterns, not just contract terms
  
  
• Providers still operate with partial visibility into how decisions are made at scale
  
  
• Anomaly turns transaction data into payer behavior intelligence
  
  
• The platform connects revenue cycle execution with managed care strategy

If you are building or scaling a fintech product, Your Fintech Story can support your strategy, positioning, and growth roadmap. Reach out to explore how to turn market opportunity into execution.

Financial institutions have spent years talking about digital transformation. Yet many still rely on fragmented systems that make product launches slow, integrations expensive, and operational upgrades risky. That gap is becoming harder to ignore as AI adoption accelerates across financial services.

Stitch, a company building cloud-native infrastructure for financial institutions, announced a $25 million Series A round led by Andreessen Horowitz. The investment is notable not only because of the size of the round, but because it represents Andreessen Horowitz’s first investment in the GCC region.

The round also included participation from existing investors Arbor Ventures, COTU Ventures, Raed Ventures, and SVC, bringing Stitch’s total funding to $35 million.

A modern operating system for financial institutions

Stitch positions itself as an operating system for modern financial institutions. Instead of forcing banks and fintechs into costly “rip and replace” migrations, the company offers a modular infrastructure stack covering lending, cards, payments, and ledgers. Institutions can adopt components gradually while continuing to operate existing systems.

That approach matters. Many financial institutions operate across disconnected platforms built over decades. Adding new products often requires complex middleware layers, manual reconciliation processes, and long implementation cycles.

Stitch is targeting that operational complexity directly by becoming the system of record underneath financial products and workflows. The company was founded by operators with experience at organizations including NPCI, FIS, Barclays, Santander, and Azentio. That operational background is visible in the positioning: infrastructure first, AI second.

AI adoption depends on clean infrastructure

One of the more important themes in Stitch’s announcement is the connection between infrastructure modernization and AI readiness.

The AI conversation in fintech often focuses on interfaces, copilots, or automation layers. But financial institutions still struggle with fragmented data environments and legacy cores that were never designed for real-time intelligence.

AI systems are only as useful as the infrastructure feeding them. Without centralized, reliable systems of record, financial institutions face limitations around data quality, compliance visibility, and operational consistency. That creates a ceiling for meaningful AI deployment.

Stitch is betting that infrastructure modernization will become a prerequisite for the next phase of financial services transformation.

Growth signals across emerging markets

The company says more than $5 billion has been transacted on the platform in the last six months alone. Stitch also reported 10x customer growth and 20x revenue growth in 2025.

Its footprint already spans the GCC, Africa, including Egypt and Kenya, and Southeast Asia. Customers include Raya Financing, LuLu Exchange, Noqodi, and Foodics.

The new capital will be used to accelerate product development, deepen regional expansion across GCC and MENA markets, and scale global go-to-market operations.

For investors, the opportunity appears tied to a broader trend: financial institutions globally are searching for infrastructure that can support faster product development, regulatory resilience, and AI adoption simultaneously.

Before wrapping up, here are a few key takeaways fintech startups should pay attention to:

Key takeaways for fintech startups

• AI adoption in financial services increasingly depends on infrastructure quality, not just AI tooling.
  
  
• Modular modernization approaches are gaining traction over full core replacement strategies.
  
  
• Infrastructure startups solving operational pain points continue to attract significant investor attention.
  
  
• Emerging markets are producing globally relevant fintech infrastructure companies.
  
  
• Systems of record remain one of the most strategic layers in financial services technology.

Your Fintech Story helps fintech startups grow through strategy, positioning, consulting, and marketing support. We work with founders and operators building the next generation of financial services infrastructure and products. Reach out.

Elliptic has raised $120 million in a Series D round led by One Peak, with participation from Nasdaq Ventures, Deutsche Bank and the British Business Bank. The round values the company at $670 million.

The announcement matters because of who participated. Nasdaq Ventures and Deutsche Bank are not early-stage crypto-native investors experimenting at the edge of financial infrastructure. These are institutions operating at global scale, with deep exposure to regulated financial markets and enterprise risk management.

Their involvement reflects a broader shift happening across financial services. Digital assets are increasingly being treated as operational infrastructure rather than a standalone speculative category. As stablecoins, tokenized assets and on-chain settlement systems expand, compliance and transaction monitoring become core infrastructure requirements.

Elliptic positions itself directly inside that layer.

Compliance is becoming the critical scaling layer for on-chain finance

According to Elliptic, stablecoins processed $33 trillion in transactions during 2025. At that scale, manual compliance processes are no longer commercially viable for exchanges, fintechs or institutions operating on-chain.

The company says it currently screens more than 1 billion transactions per week across 700+ customers in 30 countries. Two thirds of global crypto volume is transacted on exchanges already using Elliptic’s systems.

That scale explains why enterprise-grade compliance tooling is attracting institutional attention.

Financial institutions entering digital assets face a practical challenge. They need real-time visibility into transaction flows, counterparties and risk exposure across multiple blockchains while also meeting regulatory expectations. The operational burden increases further as transaction volumes grow.

Elliptic’s proposition is that AI-native compliance systems, supported by large proprietary datasets, can reduce investigation costs while improving decision speed.

Data depth is becoming a competitive advantage

One of the strongest signals in the announcement is the emphasis on proprietary data.

Elliptic states that it has spent more than a decade building datasets across 65+ blockchains, with continuous asset and entity labelling. The company argues this allows its platform to process more contextual information per transaction than competitors.

That distinction matters because AI systems in compliance environments depend heavily on data quality and historical coverage. Faster automation is only useful if institutions trust the underlying intelligence layer.

The company says its platform enables alerts to be resolved in minutes instead of hours, reserving human investigation capacity for higher-risk cases.

For large institutions, reducing compliance friction while maintaining oversight is increasingly important as digital asset volumes rise.

Stablecoins are accelerating the infrastructure conversation

The announcement also reflects how stablecoins are changing institutional priorities.

What was previously viewed as crypto market infrastructure is increasingly becoming payments infrastructure. Treasury operations, exchanges, fintech platforms and banks are all evaluating how value moves across on-chain systems.

That creates demand for monitoring systems capable of operating continuously and at enterprise scale.

Elliptic’s funding round suggests investors believe compliance infrastructure will become one of the defining control layers of the on-chain financial system.

Before digital assets can scale further inside regulated finance, institutions need systems that allow them to monitor risk in real time and operate within evolving regulatory frameworks.

Key takeaways for fintech startups

For fintech founders building in digital assets, several signals stand out:

• Institutional adoption increasingly depends on compliance infrastructure
  
  
• Proprietary data assets are becoming defensible competitive advantages
  
  
• Stablecoin growth is driving enterprise demand for real-time monitoring
  
  
• Large financial institutions are investing deeper into operational blockchain infrastructure
  
  
• AI adoption in compliance depends heavily on data quality and historical coverage

Your Fintech Story helps fintech startups turn complex industry developments into clear positioning, content and growth narratives that resonate with customers, investors and partners. Contact us.

Adfin has raised an $18 million Series A, bringing total investment in the company to more than $30 million in under two years. Index Ventures led the round again, with Visionaries Club participating alongside new investors Stéphane Kurgan and Miro founder Andrey Khusid.

The funding itself is notable, but the bigger story is how Adfin sees the future of finance operations. The company started with a narrow but painful problem: businesses getting paid late. According to Adfin, 63% of businesses are paid late, which still feels absurd when invoices are tied to legally binding agreements. Yet most finance teams still spend hours chasing payments, retrying collections, sending reminders, and fixing disconnected systems manually.

That operational mess became the foundation for Adfin’s product strategy.

Infrastructure First, AI Second

Adfin’s first focus was not AI agents or workflow automation. It was payments infrastructure. The company built systems aimed at making money move faster and with fewer points of failure. That includes direct debit flows, retry logic, integrations with accounting platforms, and faster settlement timing.

Some of the improvements are fairly simple, which is probably why they work. Adfin says mandate requests are signed three times faster because requests come from a customer’s own email address instead of a generic mailbox. Failed direct debits trigger alternative payment links immediately rather than waiting for another collection attempt. Customers also receive daily settlement instead of weekly.

Those operational tweaks appear to be producing meaningful results. Adfin says its customers now see only 9% of invoices paid late compared to the broader UK figure of 63%.

For finance teams, that difference is not cosmetic. Cash flow issues usually begin with operational friction. A delayed invoice here, a failed collection there, and suddenly teams are spending more time managing processes than managing the business itself.

Where Adfin Is Heading Next

The company now wants to expand beyond collections into broader finance automation. Adfin describes its vision as “money that moves itself,” combining payments infrastructure with AI-driven workflows across finance operations.

The interesting part is the tone of the announcement. Unlike many AI-heavy fintech pitches right now, Adfin repeatedly stresses human control. Their argument is not that finance teams disappear. It is that repetitive operational work disappears while people keep ownership over judgement calls and exceptions.

That feels far more realistic than the fully autonomous finance narrative floating around the industry.

The first example will be Adfin’s upcoming “customer agents,” designed for credit control workflows. The agents will automate tasks like reminder sequences and late fee calculations while finance teams decide how much automation they actually want. That balance between automation and oversight will probably matter more than raw AI capability for most finance leaders.

Key Takeaways for Fintech Startups

A few themes stand out from Adfin’s announcement.

• Infrastructure still matters more than flashy AI layers.
  
  
• Finance teams want automation without losing visibility or control.
  
  
• Small workflow fixes can have massive cash flow impact over time.
  
  
• Late payments remain one of the least solved operational problems in business finance.
  
  
• Fintech companies increasingly win by combining infrastructure with workflow automation instead of treating them separately.

If you’re building a fintech startup and want help refining positioning, messaging, or growth strategy, reach out.