How Venture Builders Reduce Startup Failure Risks

Startups often feel like walking a tightrope in a storm: one wrong step, one misstep in timing, market, or team, and everything falls. It’s no surprise that about 90% of startups fail overall. But in 2025, a different model is proving it can lower those odds: the venture builder. These are organizations that don’t just invest - they build. They nurture ideas, assemble teams, offer infrastructure, and walk alongside founders through early storms.

Here’s how venture builders are reducing failure risks - and what data and case studies show about their effectiveness.

The Stakes: Understanding Startup Risk

The numbers are stark. Many reports show failure rates over time are steep: roughly 10% of new startups fail within their first year, and between years two through five, majority of failures happen. By year ten, few survive. These aren’t just abstract stats, they represent teams who ran out of runway, misread market demand, or couldn’t piece together strong execution. That’s the baseline. Venture builders aim to shift those odds by intervening early on the common failure triggers.

What Venture Builders Do Differently

Venture builders provide what many startups struggle to assemble quickly: clarity of idea, team strength, operational support, and effective validation.

You can think of it this way: instead of solo founders trying to juggle everything - product, user-feedback, hiring, legal, finances - the builder supplies scaffolding. They often supply shared services (legal, HR, strategy), access to domain experts, and a process for iterating ideas before major investment. This means startups born inside builder models often avoid big, early mistakes.

There are multiple pieces to this, but one that researchers call out often is the capacity to test product-market fit before “going big.” Because builders usually demand early user feedback, safe prototyping, proof of concept. That early feedback loop weeds out ideas with weak demand.

Data & Case Studies: Proof in Practice

• Venture Studio Survival & Alive Ratios

A study called Big Venture Studio Research 2024 looked at hundreds of venture studios, hybrid builders, and corporate builders. They found that hybrid venture studios (those that combine venture studio activities with things like corporate building, accelerator, VC fund) have much higher survival rates: for every studio that closes, there are ~10.86 that remain alive. Corporate builders had ~9.3:1. Pure venture studios had lower survivorship: ~4.73:1.(That means builders which diversify or bring in hybrid functions tend to reduce risk further.

• Experienced Builders vs Novice Ones

McKinsey recently published findings in “The Three Building Blocks of a Successful Venture Factory” that more experienced venture builders are about twice as likely to achieve success compared to newcomer studios. Over time, with repeat efforts, their output (in revenue in fifth year) can be 12 times higher than that of novice studios. That suggests that venture builders don’t just reduce risk by the model - they get better at reducing risk as they build more companies.

• Corporate Venture Building vs Traditional Startup Paths

An article by CreativeDock noted that corporations using venture building (internally creating new startups or spin-outs) report success rates around 66% for their ventures, far above the 20-30% or so typical for venture capital backed startups or corporate ventures without structured building. They also say that venture building-born startups achieve better IRRs (~44% higher on average) compared to traditional startups, faster transitions from seed to Series A, and earlier exits (on average under 4 years) compared to 6-7 years typical elsewhere.

Human Stories Behind the Data

Consider a venture builder that continuously launches several projects per year. With the builder model, a given project might start not with a blank page, but with a research phase. Founders test assumptions: Is there demand? Can the technology or product be built affordably? Who is competition? These early experiments expose flaws early - low demand, wrong features - so adjustments are made before major investment.

Another important case is around the “business-building muscle.” McKinsey points out that entities that build many ventures develop repeated systems: standard ways to onboard teams, validate ideas, launch MVPs, spin-outs. Over time, they make fewer rookie mistakes - less duplicated effort, fewer misfires - so each new project starts from a stronger foundation.

What Failures Are Reduced

By virtue of these mechanisms, venture builders tend to reduce risk in several specific ways:

• Team risk: builders often match people with complementary skills rather than solo founders. They bring in domain experts early.

• Market risk: they test demand, refine product-market fit before big spends.

• Execution risk: shared infrastructure and expertise mean better supply chain, legal, hiring, finance practices early.

• Timing & capital risk: because builders tend to pace investment, control burn, and have staged funding, they avoid over-extension before product is solid.

These interventions don’t eliminate risk entirely. But they shift the risk curve substantially.

Broader Trends & What Investors Are Saying

Investors in 2025 say they want a higher floor - some guarantee of minimal failure, clearer paths from concept to growth. They like models where founders aren’t isolated. Where you can see how an idea was validated, how the team was assembled. Where overhead is shared and costs are lean early.

Corporations also find benefit: many large firms are adopting corporate venture building to create new growth engines. In one survey by EY-Parthenon, nearly 45% of executives from surveyed companies reported they have launched ventures in the last five years that now generate $100 million+ in annual revenue. Venture building gives them structure to do that.

Looking Ahead: What Makes a Builder Even More Robust

The data suggests certain traits make some venture builders better at reducing risk:

• Repetition: builders who launch many ventures learn faster.

• Hybrid or diversified models: studios that also do corporate venturing, VC funds, accelerators tend to have higher survival of their ventures.

• Strong validation early: demand testing before full build.

• Deep domain or technical competence: where builders understand industry/technology well, they avoid mis-positioning or under-estimating costs.

The Next Chapter

Startups will always carry risk. That’s part of what gives them upside. But a model growing in legitimacy in 2025 is one that doesn’t treat failure as inevitable, but as something to manage. Venture builders are showing how structured support, domain expertise, shared infrastructure, and repeated experience can tilt the odds in favor of survival.

For founders thinking of starting under a builder, the message is hopeful: you don’t have to brace for failure alone. For investors, it means better early signals, stronger teams, and less wasted cost.

In a world where capital is tighter and demands are higher, venture builders are proving to be more than trend - they might be the most reliable path through the startup storm.

Funding the Future: The Role of VCs and Sovereign Funds in Singapore’s Venture Studios

The venture studio model has redefined how startups are born. Instead of betting on lone founders, venture studios build companies from the ground up, pairing entrepreneurial talent with capital, infrastructure, and networks. But behind this model lies a critical question: who funds the future?

In Singapore, the answer increasingly comes from two powerful sources - venture capital firms and sovereign wealth funds. Together, they are shaping not just the trajectory of venture studios but the kinds of companies that will define Asia’s innovation landscape over the next decade.

Why Funding Matters in Venture Building

Traditional startups often begin with a small seed round, testing ideas with limited resources. Venture studios flip that dynamic. They require upfront investment to design infrastructure, hire operational teams, and support multiple ventures simultaneously. The model is capital-intensive, but it also increases the odds of producing sustainable startups.

This is why the involvement of venture capital (VCs) and sovereign wealth funds is so significant. They provide not only the capital but also the long-term vision needed to sustain venture studios through the uncertain early stages of building science-driven or industry-specific companies.

The Numbers Speak

In 2022, Singapore attracted more than US$11 billion in startup funding, according to Enterprise Singapore, with a growing share flowing into venture-building initiatives. The global venture studio market itself is projected to reach US$42 billion by 2027, up from around US$20 billion today, as reported by Global Startup Studio Network.

Within Singapore, sovereign wealth funds play a particularly influential role. Temasek Holdings, with assets exceeding US$287 billion, has been steadily increasing its exposure to early-stage innovation through vehicles like Xora Innovation, its venture-building arm. Meanwhile, GIC, with more than US$770 billion in assets under management, has also stepped up its participation in deeptech and sustainability-focused ventures, often co-investing alongside studios and VCs.

Case Study: Temasek and Xora Innovation

Temasek’s launch of Xora Innovation in 2019 was a milestone for Singapore’s venture building ecosystem. Xora focuses on commercializing breakthrough scientific research in fields like climate tech, healthcare, and advanced manufacturing. Unlike traditional VC, Xora doesn’t just invest - it co-builds, bringing together teams of scientists, operators, and entrepreneurs to create companies from scratch.
One example is its investment in Eavor, a geothermal technology startup developing closed-loop systems for renewable energy. By backing such ventures, Temasek shows how sovereign funds can align financial returns with global sustainability goals while anchoring these efforts in Singapore.

The Role of Venture Capital Firms

Venture capital firms, too, are leaning into the venture studio model. Global firms like Sequoia Capital and Vertex Ventures (the latter headquartered in Singapore) have backed startups emerging from studios, drawn by the de-risked nature of ventures that already have structured support and validation.

Antler, one of the world’s most prominent venture builders with a major base in Singapore, has partnered with VCs to scale its portfolio. Since its launch, Antler Singapore has created more than 100 startups, many of which have raised follow-on capital from leading VCs. This collaboration demonstrates a virtuous cycle: studios generate investable companies, while VCs provide the growth capital to scale them globally.

Why Sovereign Funds Matter More in Singapore

Singapore’s sovereign wealth funds bring something that private VCs alone cannot - patient capital. DeepTech, climate, and biotech startups often take years to become commercially viable. Sovereign funds like Temasek and GIC are uniquely positioned to absorb these long timelines while maintaining conviction in long-term returns.

Moreover, their participation signals confidence to the market. When a sovereign fund co-invests in a venture, it often catalyzes additional investment from global VCs, corporates, and even governments. This multiplier effect strengthens the ecosystem and accelerates the scale-up of ventures born in Singapore’s studios.

Singapore as a Regional Magnet for Capital

The presence of sovereign wealth funds also amplifies Singapore’s role as a capital hub for Southeast Asia. With over 650 million people, Southeast Asia represents one of the world’s fastest-growing digital economies, projected by Google and Temasek to reach US$330 billion by 2025. By anchoring venture studios in Singapore and funding them with sovereign-backed capital, the city-state effectively positions itself as the launchpad for ventures targeting this massive market.

The Challenges Ahead

While the alignment of VCs and sovereign funds has fueled the rise of Singapore’s venture studios, challenges remain. Venture building is resource-heavy, and not all studios will survive. There is also the question of focus: should capital prioritize moonshot DeepTech ventures with global ambitions, or scalable consumer-tech plays better suited for regional adoption?

Striking the right balance will be key. Too much emphasis on short-term gains risks diluting the transformative potential of venture building. Too much focus on moonshots without market validation risks creating science projects that never scale.

Looking Ahead: Funding the Next Decade

What’s clear is that the combination of VCs and sovereign wealth funds gives Singapore’s venture studios a uniquely powerful advantage. Venture capital brings agility and global networks, while sovereign funds provide stability and patience. Together, they create an ecosystem capable of nurturing bold ideas through the long road from concept to commercial success.

In the next decade, expect to see more sovereign-VC partnerships in Singapore’s venture building space, particularly in fields like climate tech, AI, and advanced manufacturing. These are areas where global challenges intersect with Singapore’s ambition to lead in innovation.

For founders, the message is clear: in Singapore, you don’t just get access to capital - you get access to aligned capital, designed to see you through the toughest years of building. For investors, the takeaway is equally strong: if you want exposure to the next generation of high-impact ventures in Asia, Singapore’s venture studios are where the story begins.

Singapore’s Role in Shaping the Next Wave of DeepTech through Venture Building

DeepTech refers to technologies rooted in scientific discoveries and engineering breakthroughs and it is increasingly seen as the foundation for solving humanity’s toughest problems. From climate resilience and quantum computing to advanced healthcare and space exploration, the promise of DeepTech extends far beyond incremental innovation. But building DeepTech startups is notoriously hard: they require long development cycles, heavy capital investment, and multidisciplinary expertise.

This is where Singapore is quietly taking center stage. Over the last decade, the city-state has positioned itself as one of Asia’s most compelling hubs for DeepTech innovation, not through chance but through a deliberate embrace of venture building. By pairing research talent with structured startup creation, Singapore is charting a path that could make it a global leader in translating science into scalable businesses.

The DeepTech Imperative

DeepTech is not just hype. According to Boston Consulting Group, DeepTech startups globally attracted more than US$60 billion in funding in 2023, double the levels seen in 2016. Yet the barriers to entry remain high. Unlike software startups, where a minimum viable product can be built in weeks, DeepTech ventures often require years of research before commercial viability.

Singapore has recognized both the challenge and the opportunity. With limited natural resources, the country has long invested in knowledge as its most strategic asset. Today, that strategy is paying off as its universities and research institutions - such as the National University of Singapore (NUS) and A-STAR - are increasingly integrated into venture-building pipelines.

A Government-Backed Ecosystem

The Singapore government has been one of the most active global backers of DeepTech venture building. Through initiatives like the SGInnovate Deep Tech Nexus Strategy, launched in 2017, the country committed more than US$150 million to support the translation of science into companies. SGInnovate itself has directly invested in over 100 DeepTech startups spanning fields like autonomous robotics, medtech, and agritech.

This model is designed not just to fund startups but to systematically derisk them. By offering labs, pilot facilities, and structured venture building programs, Singapore reduces the “valley of death” between academic research and commercial application.

Case Study: A-STAR Spinouts

One of the best examples of Singapore’s DeepTech venture building comes from A-STAR, the Agency for Science, Technology and Research. Over the past five years, A-STAR has spun out dozens of startups in biotech, advanced materials, and AI. Companies like Nanoveu, which develops nanotechnology-based films for optics and antiviral protection, have scaled regionally thanks to early support from A-STAR’s venture co-creation efforts.

Another case is RWDC Industries, a biodegradable plastics startup that originated in Singapore and has since raised more than US$135 million in growth funding. RWDC’s success underscores how research-driven ventures can become globally relevant with the right support structure.

Temasek and the Long-Term View

DeepTech requires patient capital, and few institutions embody patience better than Singapore’s sovereign wealth fund, Temasek. Through its venture-building arm Xora Innovation, Temasek partners with scientists and entrepreneurs at the very earliest stages, often before a commercial application is fully proven.

This long-term approach is critical. Traditional VCs often shy away from DeepTech because of long timelines, but venture studios like Xora de-risk the process by building operational capacity around founders. This allows breakthroughs in quantum computing or synthetic biology to be pursued without the pressure of unrealistic short-term returns.

Singapore as Asia’s Testbed

Another advantage lies in Singapore’s role as a testbed for emerging technologies. With its compact size, advanced infrastructure, and supportive regulators, the city-state often serves as a “living laboratory” for pilots.

For example, autonomous vehicle trials, drone delivery pilots, and next-gen biotech therapies have all been deployed in Singapore earlier than in most regional markets. This testbed status makes the country an attractive base for DeepTech venture builders: startups can validate complex technologies locally before scaling across Asia’s 650 million-strong Southeast Asian market.

Talent at the Core

DeepTech thrives on talent density, and Singapore has invested heavily in building a global research workforce. The country attracts scientists and engineers through initiatives like the Research, Innovation and Enterprise (RIE) 2025 Plan, which allocated US$25 billion to science and innovation over five years.

What sets Singapore apart is how this talent is integrated into venture building. Instead of leaving researchers isolated in academia, programs connect them with entrepreneurs, operators, and investors who can help translate breakthroughs into market-ready companies. This culture of collaboration is one reason why Singapore consistently ranks among the top 10 in the Global Innovation Index.

Looking Forward: Singapore’s DeepTech Ambition

The next decade will determine whether Singapore’s DeepTech bets pay off. The foundations are strong: government backing, venture studios, sovereign wealth participation, and global research talent. The challenge lies in scaling beyond local pilots into global leaders.

If Singapore’s studios can consistently produce DeepTech unicorns - companies solving real-world problems in energy, healthcare, and materials - it will cement its place not just as Asia’s DeepTech hub but as one of the world’s great innovation ecosystems.

For founders, Singapore offers a rare combination: scientific depth, supportive policy, and venture-building structures that reduce the odds of failure. For investors, it provides a gateway to high-potential DeepTech startups in Asia with the added security of government and sovereign fund alignment.

The message is clear: while Silicon Valley may dominate software, the next generation of world-changing science-driven startups could well be born in Singapore’s venture studios.

Why Singapore is Emerging as Asia’s Hub for Venture Building

Singapore has long been known as a global financial center, but in recent years, its ambitions have expanded beyond banking and trade. Today, it is carving out a reputation as Asia’s leading hub for venture building, the model where ideas are not just funded but systematically transformed into startups through the structured support of venture studios. While Silicon Valley remains the gold standard for startup culture, Singapore is demonstrating that the future of innovation in Asia might follow a different playbook.

The rise of venture building in Singapore is not accidental. It is the result of a deliberate strategy combining government foresight, investor appetite, and the city-state’s unique position as a connector between East and West. For founders and investors alike, Singapore is increasingly where the region’s most ambitious ideas are being tested, scaled, and launched into the world.

The Numbers Behind the Story

Singapore’s startup ecosystem has grown at a remarkable pace. According to Enterprise Singapore, the number of tech startups in the country jumped from around 2,800 in 2003 to more than 4,500 in 2023, employing tens of thousands of people and contributing significantly to GDP. In 2022 alone, venture funding in Singapore reached US$11 billion, accounting for more than 50% of all funding across Southeast Asia, according to DealStreetAsia.

But what’s most striking is not just the raw funding numbers. It is the structural shift toward venture building. More than 30 venture studios now operate in Singapore, ranging from independent builders like Antler, which has a strong base in the city, to corporate-backed and government-supported studios that focus on deeptech, fintech, and sustainability. This density is unmatched anywhere else in Asia, positioning Singapore as the natural hub for the model.

A Supportive Government Framework

One of Singapore’s most powerful advantages is the role of government policy. Agencies such as Enterprise Singapore and EDB (Economic Development Board) have actively fostered venture building by co-investing in studios, providing grants, and streamlining regulatory pathways for new businesses.

For instance, in 2020, the government launched the Startup SG Founder Venture Building Program, a scheme designed specifically to support venture builders in co-developing startups with entrepreneurs. This move signaled not only recognition of the venture building model but also a willingness to bet national resources on it.

The regulatory environment also plays a role. With a reputation for clarity, efficiency, and fairness, Singapore provides a rare sense of stability in a region where startups often grapple with red tape. For deeptech or highly regulated sectors like fintech and biotech, this regulatory clarity can make the difference between stagnation and scale.

Case Studies: Successful Venture Builders

The global venture builder Antler made Singapore its launchpad in Asia, running its residency program for founders and producing startups that have since expanded globally. In just five years, Antler Singapore has backed more than 500 founders and created over 100 startups, several of which have gone on to raise significant Series A and B rounds.

Another standout is Xora Innovation, the venture building arm of Temasek, Singapore’s sovereign wealth fund. Unlike traditional venture capital, Xora works directly with scientists and entrepreneurs to transform advanced research into scalable deeptech ventures. This model reflects Singapore’s ambition not just to create more startups but to anchor globally relevant ones in high-tech, defensible fields. These examples show how Singapore is positioning venture building not as a fringe experiment but as a central pillar of its innovation economy.

Location as a Strategic Advantage

Geography has always been part of Singapore’s success story, and venture building is no different. Situated at the crossroads of Southeast Asia, the city-state offers immediate access to a consumer market of over 650 million people, a young, digital-native population hungry for innovation. At the same time, Singapore remains deeply connected to Western capital markets, making it a natural bridge for global investors seeking exposure to Asia.

This dual access - emerging market scale on one side, developed-world capital on the other - is a rare combination. For venture studios looking to create startups that can expand regionally and scale globally, Singapore offers the perfect launchpad.

Why Founders Are Choosing Singapore

It’s not only investors and policymakers driving this momentum. Founders themselves increasingly see Singapore as the best place to build. The city offers one of the most connected startup communities in Asia, access to a deep pool of talent, and a cosmopolitan culture that values experimentation.

Entrepreneurs also appreciate the reduced risk profile that venture building offers. Instead of going it alone, they join studios that provide initial capital, expert support, and access to networks, dramatically improving their odds of success. For many, especially in capital-intensive sectors like biotech or climate tech, this support is the difference between a promising idea and a real company.

Looking Ahead

As venture building matures globally, Singapore is uniquely positioned to lead its adoption in Asia. With strong government support, growing investor participation, and an ecosystem of studios producing measurable results, the city-state has built the foundations of a venture building hub that rivals the best in the world.

The next chapter will depend on whether these studios can consistently produce companies that scale to unicorn status or become regional champions. If they do, Singapore won’t just be a hub for venture building - it will be the place where Asia’s most important startups of the next decade are born.

For founders, the message is clear: if you want to test bold ideas in Asia with a higher chance of survival, Singapore is the place to start. For investors, the message is equally strong: the most interesting stories in venture building are not just being written in Silicon Valley - they are unfolding right here, at the crossroads of the East.

Investing in Artificial Intelligence: Key Trends for Funds

Methodology: A Fund-Focused View on AI Investment Dynamics

This article draws from market reports, fund manager insights, and AI ecosystem analyses to outline the main trends shaping how venture, growth, and corporate funds are investing in artificial intelligence today. We look at deal activity, sector focus, and strategic themes guiding capital allocation.

In Brief: What Funds Need to Know

• AI deal volume remains strong, with funds focusing on core infrastructure, applied AI, and ethical frameworks.

• Large funds and corporate VCs are increasingly backing AI tools that reshape entire industries.

• Geopolitics, regulation, and responsible AI principles are playing a bigger role in diligence.

• The next wave of winners may emerge from vertical AI not general-purpose models.

AI Investment Is Maturing But the Opportunity Remains Huge

Over the past decade, funds have steadily increased their exposure to artificial intelligence. From early bets on core machine learning platforms to today’s more refined focus on vertical applications (healthcare AI, legal tech AI, climate AI), the landscape has evolved.

AI deal activity remains resilient even in cautious markets, as funds seek companies offering real, scalable applications rather than AI hype.

According to PitchBook, AI and machine learning startups captured over $50 billion in venture funding globally in 2024, with enterprise AI infrastructure and applied AI solutions leading the way.

Key Trend 1: From General AI to Vertical AI

• Fund managers are shifting attention from general-purpose AI tools to sector-specific solutions. Why?

• Vertical AI startups typically show faster paths to product-market fit.

• Customers value AI embedded in their existing workflows (e.g., legal document review, clinical trial analysis).

• Regulatory clarity is stronger in narrow-use cases.

Funds investing in AI are looking for companies that deeply understand their end markets, not just ones building horizontal tools.

Key Trend 2: Responsible AI Moves Front and Center

Ethical AI isn’t just a discussion point anymore, it's a diligence priority.

LPs increasingly expect funds to assess AI safety, bias mitigation, and explainability during investment screening. Startups offering transparency features (e.g., model audits, bias dashboards) are gaining an edge in fundraising.

Funds that position themselves as champions of responsible AI will not only de-risk portfolios but also build brand credibility with partners and regulators.

Key Trend 3: Corporate Venture Capital Is Leading in AI Scaling

Corporate funds are playing a growing role in AI funding rounds especially at the growth stage. Why?

• AI solutions often require integration with large enterprise systems.

• Corporate VCs provide go-to-market pathways AI startups need to scale.

• Strategic investors are focused on AI that directly augments their core business lines.

We see funds co-investing alongside corporates in areas like AI-driven cybersecurity, supply chain optimization, and predictive analytics.

Final Thought: What’s Next for AI-Focused Funds?

The AI gold rush is shifting from model-building to real-world deployment. Funds that succeed will:

• Back founders solving specific industry problems.

• Prioritize responsible, explainable AI.

• Align with partners who can accelerate adoption at scale.

For investors, artificial intelligence isn’t just a theme, it's becoming an essential part of any modern portfolio.

How AI is Changing the Underwriting Process in B2B Insurance

The B2B insurance landscape is experiencing a seismic transformation. Traditional underwriting, once dominated by manual processes and lengthy decision cycles, is giving way to a new era powered by artificial intelligence. This shift isn't just evolutionary, it's revolutionary, fundamentally changing how insurers assess risk, price policies, and serve their commercial clients.

The Numbers Don't Lie: A Market in Rapid Transformation

The statistics paint a compelling picture of AI's meteoric rise in insurance. The global AI in the insurance market, valued at $8.13 billion in 2024, is projected to explode to $141.44 billion by 2034, representing a staggering 33.06% compound annual growth rate. This isn't just growth; it's a complete market reimagining. What makes this transformation even more remarkable is its pace of adoption. Recent industry surveys reveal that 77% of insurance companies are now in some stage of AI adoption across their value chain, a dramatic leap from just 61% in 2023. Among life and annuity insurers, the adoption rate soars even higher, with 82% having implemented generative AI in one or more business functions.

For underwriting specifically, the impact is particularly pronounced. AI-assisted underwriting has emerged as one of the largest use case segments for AI in insurance, with insurers reporting up to 40% improvement in underwriting efficiency when deploying AI tools.

Beyond Speed: The Multifaceted Revolution

The transformation extends far beyond simple automation. Modern AI systems are reshaping every aspect of the B2B underwriting process, creating value that compounds across multiple dimensions.

Risk Assessment Precision: Traditional underwriting relied heavily on historical data and underwriter intuition. Today's AI systems analyze vast datasets from connected devices, satellite imagery, social media, and IoT sensors. With experts estimating one trillion connected devices by 2025, the data available for risk assessment is expanding exponentially. This data deluge enables insurers to understand their commercial clients more deeply than ever before, resulting in pricing accuracy that was previously impossible.

Fraud Detection and Pattern Recognition: AI's pattern recognition capabilities have revolutionized fraud detection in commercial lines. By identifying irregular patterns and reducing subjective biases, AI systems can spot potential fraud that human underwriters might miss. This enhanced detection capability translates directly to improved loss ratios, with some insurers reporting decreases of 1-3% through intelligent recommendations on optimal application approval and quoting decisions.

Real-Time Decision Making: The traditional underwriting process often stretched across weeks or months for complex commercial risks. AI has compressed this timeline dramatically, enabling real-time analysis of applications and instant decision-making for many types of coverage. This speed advantage is particularly crucial in B2B markets where businesses need coverage quickly to support their operations.

The Technology Stack Driving Change

The AI revolution in B2B insurance underwriting isn't powered by a single technology but rather by a sophisticated ecosystem of interconnected tools and platforms. Machine learning algorithms process historical claims data to identify risk patterns, while natural language processing systems extract insights from unstructured documents like financial statements and business plans.

Computer vision technology analyzes satellite imagery and drone footage to assess property risks, while predictive analytics models forecast potential claims scenarios. Integration platforms connect these AI tools with existing underwriting systems, creating seamless workflows that enhance rather than replace human expertise.

The sophistication of these systems continues to evolve rapidly. Today's AI underwriting platforms can process multiple data sources simultaneously, cross-referencing business registration information, financial health indicators, industry risk factors, and real-time market conditions to generate comprehensive risk profiles within minutes.

Industry Leaders Driving Innovation

The competitive landscape is being reshaped by companies that successfully harness AI's potential. Planck, for example, raised $71 million in funding to develop its underwriting AI product, which now operates globally. Their platform demonstrates how specialized AI solutions can transform traditional underwriting approaches.

Similarly, major data companies like Experian are developing AI-powered solutions specifically for commercial insurance. Their "Hazard Tags" system provides comprehensive profiles of five million UK businesses, enabling insurers to make more informed underwriting decisions at scale.

The Path Forward: Challenges and Opportunities

Despite the remarkable progress, the journey toward AI-driven underwriting isn't without obstacles. Data quality remains a persistent challenge, as AI systems are only as good as the information they process. Regulatory compliance adds another layer of complexity, particularly in jurisdictions with strict data protection laws.

The human element remains crucial. While AI excels at processing vast amounts of data and identifying patterns, human underwriters bring contextual understanding and relationship management skills that complement AI capabilities. The most successful implementations combine AI's analytical power with human expertise and judgment.

Looking ahead, the integration of AI in B2B insurance underwriting will likely deepen rather than simply expand. As AI systems become more sophisticated and data sources multiply, underwriters will gain unprecedented insights into commercial risks. The question isn't whether AI will transform B2B insurance underwriting; it's how quickly and comprehensively this transformation will occur.

Final Thought

The transformation of B2B insurance underwriting through AI represents more than technological advancement, it's a fundamental shift toward data-driven, precise, and efficient risk assessment. With 36% of insurance technology experts identifying AI as their top innovation priority for 2025, the momentum behind this transformation continues to build.

For B2B insurers, the choice is clear: embrace AI-driven underwriting or risk being left behind by competitors who have harnessed its power. The insurers who successfully integrate AI into their underwriting processes won't just survive this transformation, they'll thrive in the new landscape of precision, speed, and insight that defines the future of commercial insurance.

The numbers, the technology, and the market momentum all point in the same direction. AI isn't just changing B2B insurance underwriting, it's revolutionizing it, one algorithm at a time.

From Payment Rails to Embedded Finance: What VCs Are Betting on in Fintech

The fintech revolution has evolved far beyond simple payment apps and digital wallets. As we advance through 2025, venture capitalists are recalibrating their strategies, moving away from traditional fintech plays toward sophisticated infrastructure and embedded financial services that promise to reshape how businesses and consumers interact with money.

The Great Fintech Reset: Where the Smart Money Is Going

The numbers tell a compelling story of transformation. While overall VC investment in fintech remains near six-year lows, strategic investors are doubling down on specific segments that demonstrate exceptional growth potential. The embedded finance market, valued at $104.8 billion in 2024, is projected to explode to $690.39 billion by 2030, a staggering 36.4% compound annual growth rate that has captured the attention of sophisticated investors worldwide.

This isn't just another tech trend. It represents a fundamental shift in how financial services are delivered, consumed, and integrated into daily life. Smart VCs recognize that the future belongs to companies that can seamlessly weave financial functionality into existing platforms rather than building standalone financial products

Payment Rails: The Infrastructure Play That's Paying Off

The backbone of modern finance is undergoing a radical transformation, and investors are taking notice. FedNow, the Federal Reserve's instant payment system, is processing $190 million in payments daily, while Real-Time Payments (RTP) networks reported a remarkable 94% increase in transaction volume throughout 2024. This explosive growth has tripled participation in instant payment rails over the past year, with over 1,200 financial institutions now connected to these systems.

For VCs, this represents more than just impressive statistics, it signals a massive opportunity in payment infrastructure. Companies building the pipes that enable instant, seamless transactions are attracting significant investment because they're positioned to capture value from every transaction flowing through their systems. The shift from traditional payment processing to instant settlement creates entirely new revenue streams and business models that savvy investors are eager to fund.

Embedded Finance: The Trillion-Dollar Opportunity

The embedded finance sector is where VCs are placing their biggest bets, and the data supports their enthusiasm. Multiple market research firms project the sector will reach between $570.9 billion and $1.73 trillion by 2033, depending on adoption rates and regulatory environments. These aren't just optimistic projections, they're backed by real market momentum.

Consider the rapid expansion beyond traditional sectors. Healthcare, construction, and hospitality, industries previously slow to adopt financial technology, are now integrating tailored financial services directly into their platforms. This expansion is driving what investors call the "invisible finance" trend, where financial services become so seamlessly integrated that users barely notice they're engaging with sophisticated financial products.

The retail sector alone demonstrates the power of this shift. Fintech companies have grown from handling 22% of personal loan originations in 2019 to approximately 39% in 2024. This isn't just market share displacement, it's evidence of a fundamental change in how consumers prefer to access financial services: embedded within the platforms and services they already use.

The AI Wild Card: Intelligent Financial Services

Artificial intelligence has emerged as a bright spot in an otherwise cautious investment environment. VCs are particularly excited about AI applications that enhance embedded finance platforms, enabling real-time credit decisions, personalized financial products, and predictive analytics that can anticipate user needs before they're explicitly expressed.

The convergence of AI and embedded finance is creating opportunities for companies to offer hyper-personalized financial services at scale. For investors, this represents the holy grail of fintech: technology that can increase conversion rates, reduce risk, and create sticky customer relationships simultaneously.

Geographic Hotspots: Where the Action Is

The global nature of fintech investment is creating interesting regional dynamics. China's embedded finance market is expected to grow at a remarkable 32.8% CAGR through 2030, driven by tech giants like Alibaba and Tencent integrating financial services into their ecosystems. Meanwhile, India is witnessing significant growth with a 19.5% CAGR, fueled by a massive underbanked population and supportive regulatory environment.

These geographic variations are creating opportunities for VCs to invest in region-specific solutions that can later be adapted for global markets. The most successful fintech companies are those that can navigate diverse regulatory environments while maintaining their core value propositions.

The Regulatory Reality Check

Smart investors are also paying close attention to the regulatory landscape. Increased regulation, predicted as one of the top fintech trends for 2025, isn't necessarily a headwind, it's an opportunity for well-positioned companies to create competitive moats. Firms that can navigate complex compliance requirements while maintaining user experience advantages are attracting premium valuations.

The regulatory environment is actually accelerating the embedded finance trend, as companies seek to partner with established financial institutions rather than navigate licensing requirements independently. This creates opportunities for B2B fintech companies that can serve as bridges between traditional financial institutions and technology platforms.

The Investment Thesis: Infrastructure Over Apps

The most successful fintech VCs are shifting their focus from consumer-facing applications to the infrastructure that powers them. The companies receiving the largest funding rounds are those building the rails, APIs, and platforms that enable other businesses to offer financial services seamlessly.

This infrastructure-first approach reflects a mature understanding of the fintech ecosystem. While consumer apps can achieve viral growth, infrastructure companies build sustainable, defensible businesses with predictable revenue streams and strong network effects.

Looking Forward: The Next Wave

As we move deeper into 2025, the fintech landscape is being reshaped by three key forces: the maturation of instant payment rails, the explosive growth of embedded finance, and the intelligent application of AI to financial services. VCs who understand these dynamics and invest accordingly are positioning themselves to capture outsized returns in what promises to be the most transformative period in financial services history.

The message is clear: the future of fintech isn't about building better banking apps, it's about making finance invisible, instant, and intelligent. The companies and investors who embrace this reality will define the next decade of financial innovation.

Final Thoughts

The fintech evolution we're witnessing today represents more than just technological advancement, it's a fundamental reimagining of how financial services integrate into human and business experiences. For venture capitalists, this moment presents both unprecedented opportunity and significant risk. 

The data overwhelmingly supports one conclusion: the age of standalone fintech products is ending, and the era of invisible, embedded financial services has begun. The question isn't whether this transformation will happen, it's whether investors will have the vision to back the companies that make it reality.

Pourquoi les insurtechs attirent autant les investisseurs ?

L’assurance n’a jamais été un secteur synonyme d’innovation rapide. Pourtant, depuis quelques années, les startups de l’insurtech bouleversent ce paysage traditionnel avec des approches digitales, agiles et centrées sur l’expérience utilisateur. Résultat : elles attirent des milliards d’euros d’investissement à travers le monde, et l’Europe n’est pas en reste.

Mais qu’est-ce qui rend les insurtechs si séduisantes aux yeux des investisseurs ? Voici les raisons clés.

Un marché colossal en attente de disruption

Le secteur de l’assurance représente des milliers de milliards d’euros de primes chaque année, avec une forte concentration d’acteurs historiques. C’est un marché immense, mais souvent lent, opaque et peu centré sur le client. Pourtant, l’insurtech connaît une croissance rapide : en 2023, les investissements mondiaux dans ce secteur ont dépassé les 4 milliards de dollars, malgré un contexte macroéconomique difficile. 

Cette dynamique s’est poursuivie en 2024, avec plus de 1,4 milliard de dollars levés au premier semestre, signe d’un intérêt soutenu des investisseurs pour des acteurs capables de digitaliser et transformer un marché encore largement traditionnel. Comme la fintech avant elle, l’insurtech promet d’ouvrir un secteur longtemps verrouillé à l’innovation, attirant ainsi des capitaux à la recherche de nouvelles opportunités de croissance.

Une transformation digitale enfin lancée

Les consommateurs veulent désormais souscrire, gérer et résilier leurs contrats d’assurance en ligne, en quelques clics. Les insurtechs répondent à cette attente avec des interfaces intuitives, des tarifs transparents, et parfois même une personnalisation en temps réel.

En automatisant les processus, en utilisant l’intelligence artificielle pour l’évaluation des risques ou le traitement des sinistres, ces startups réduisent drastiquement les coûts d’exploitation. Un levier très attractif pour les investisseurs en quête de rentabilité.

Des modèles hybrides et scalables

Les insurtechs n’ont pas toutes le même modèle. Certaines créent leurs propres produits d’assurance en tant que porteurs de risque, d’autres s’associent à des assureurs traditionnels pour distribuer des offres sous marque blanche, ou encore proposent des infrastructures tech en marque grise (B2B).

Ce niveau de flexibilité permet d’adapter le modèle économique à chaque marché local, tout en gardant une ambition d’expansion rapide à l’international. Les investisseurs apprécient ces modèles scalables, capables de croître sans exploser les coûts.

Un alignement avec les nouvelles attentes sociétales

Les jeunes consommateurs veulent des services simples, accessibles, et plus transparents, mais aussi des entreprises qui partagent leurs valeurs. De nombreuses insurtechs proposent des assurances à impact : mobilité douce, assurance santé mentale, couverture pour freelances, micro-assurance pour les populations exclues…

Ces approches rendent l’assurance plus inclusive et plus moderne, ce qui séduit non seulement les clients finaux mais aussi les fonds à impact ou les family offices sensibles aux enjeux sociétaux.

Des exemples de succès qui rassurent le marché

Des startups comme Alan (France), Wefox (Allemagne) ou Zego (Royaume-Uni) ont levé des centaines de millions d’euros ces dernières années. Elles prouvent que le modèle fonctionne, et qu’il est possible de combiner croissance rapide et innovation réglementée.

Ces success stories créent un effet d’entraînement : en voyant d’autres fonds entrer au capital, de nouveaux investisseurs veulent aussi prendre position tôt dans les prochaines pépites du secteur.

Une réglementation de plus en plus ouverte à l’innovation

Les régulateurs européens sont de plus en plus ouverts à l’expérimentation, notamment via des "sandboxes réglementaires" qui permettent aux insurtechs de tester de nouveaux produits en conditions réelles tout en restant encadrées.

Cela réduit le risque juridique pour les investisseurs et accélère la mise sur le marché des nouvelles offres. Un cadre qui rend l’investissement plus sûr et plus prévisible.

Conclusion : une vague structurelle, pas un effet de mode

L’insurtech n’est pas une simple tendance. C’est une réponse stratégique à un besoin profond de transformation dans un secteur clé de l’économie. Elle combine les ingrédients que les investisseurs recherchent : taille de marché, inefficience à corriger, technologies différenciantes, scalabilité, impact social, et exemples concrets de croissance.

Dans les années à venir, les insurtechs les plus solides pourraient bien devenir les nouveaux géants de l’assurance. Et pour les investisseurs, c’est maintenant qu’il faut être à bord.

The Most VC-Funded Sectors in Europe

The Most VC-Funded Sectors in Europe

Europe’s startup ecosystem has grown rapidly over the past decade, attracting billions in venture capital (VC) from both local and global investors. While overall funding levels fluctuate with macroeconomic conditions, certain sectors consistently draw strong VC attention. From climate tech and deep tech to fintech, health innovations, and enterprise software, the continent’s innovation landscape is increasingly diverse and resilient.

Climate Tech Leads the Pack

Climate tech and energy transition startups are now Europe’s single largest VC-funded sector, accounting for approximately 27–30% of total venture capital investment in 2023 (Dealroom/Sifted). This surge reflects the EU’s ambitious carbon neutrality targets, supportive policy frameworks, and the growing appetite among investors for climate-positive solutions.

Startups tackling renewable energy, sustainable mobility, carbon capture, and circular economy solutions are driving the trend. Countries like the Netherlands, Germany, and the Nordics are at the forefront, combining strong cleantech ecosystems with dedicated climate funds.

AI & Deep Tech Keep Rising

AI and deep tech (which includes frontier technologies like advanced hardware, quantum computing, and automation) accounted for about 17% of Europe’s VC funding in 2023. The rise of generative AI and automation tools is accelerating investor interest, with large rounds for companies like Mistral AI, DeepL, and Aleph Alpha showing the strength of the ecosystem.

Key hubs for AI and deep tech include Berlin, Paris, and London, all benefiting from talent density and supportive research institutions.

Fintech Remains a Pillar

Fintech remains a major draw, attracting roughly 15–19% of total VC investment, down slightly from its peak but still firmly in the top three sectors. From digital banks to blockchain platforms and payments solutions, European fintech leaders like Revolut (UK), N26 (Germany), and Lydia (France) continue to scale, supported by consumers’ shift away from traditional banking.

London remains Europe’s fintech capital, thanks to its mature regulatory environment and deep investor pools.

Healthtech & Enterprise Software Stay Solid

While harder to split precisely, healthtech and enterprise software together continue to be pillars of European VC activity. Over the last 20 years, they have consistently accounted for a combined ~40% of total VC funding (Dealroom). Healthtech startups in telemedicine, AI diagnostics, and mental health are still seeing healthy long-term growth, while B2B SaaS and cloud solutions remain attractive bets for their scalability and recurring revenues.

Companies like Doctolib (France), Kry (Sweden), and Celonis (Germany) show how Europe’s healthtech and enterprise software scenes remain globally competitive.

Emerging Sectors to Watch

Beyond these leading sectors, several emerging areas are gaining momentum:

• AgriTech: With food security and regenerative farming in focus, AgriTech is steadily attracting more funding.

• Cybersecurity: Increasing digital threats are driving larger rounds for European security startups.

• Edtech: While post-pandemic growth slowed, niches like corporate training and AI-driven learning are evolving.

• Space Tech: Once US-dominated, Europe’s space tech sector is quietly expanding, supported by national programs and private capital.

How Does Europe Compare Globally?

According to Dealroom’s 2023 and early 2024 data, Europe’s top-funded sectors now mirror global trends in the US and East Asia. In the US, the top sectors by VC investment are:

1. Health & Biotech

2. Enterprise Software / AI

3. Fintech

East Asia follows a similar pattern, with deep tech, fintech, and industrial tech attracting the biggest rounds. Notably, Europe’s climate tech stands out: its share of total VC funding is higher than in North America or Asia, thanks to EU policy incentives and investor demand for sustainable growth.

Final Thoughts

The European VC landscape is dynamic, but certain sectors continue to stand out for their scale, impact, and resilience. Climate tech, deep tech, fintech, healthtech, and enterprise software together account for the lion’s share of VC investment, while emerging verticals like AgriTech and space tech hint at the next wave of innovation.

For founders, understanding which sectors attract capital and why can shape how you position your startup. For investors, the current trends reflect where both opportunity and responsibility intersect in the next era of European innovation.

Predictive Insurance Through AI: Myth or Reality?

Methodology: Exploring AI’s Role in Insurance Forecasting

This article draws on industry reports, case studies, and current use cases from insurers applying artificial intelligence to predictive modeling. It examines how AI is reshaping underwriting, claims management, and customer engagement and where the promise may be ahead of the current reality.

In Brief: Where We Stand Today

• AI is already delivering predictive insights in claims detection, risk scoring, and fraud prevention.

• Adoption remains uneven, with most impact in large carriers and insurtech startups.

• Challenges include data privacy concerns, regulatory hurdles, and model transparency.

• The future of predictive insurance lies in combining AI with human judgment and ethical frameworks.

How AI Is Changing Insurance Predictions

The idea of predictive insurance is no longer science fiction. With vast amounts of customer data, real-time IoT inputs (such as from vehicles or smart homes), and advanced machine learning models, insurers can now:

• Identify high-risk customers or properties proactively

• Anticipate claim likelihood based on behavioral and environmental factors

• Tailor pricing more accurately for individual policyholders

• Detect fraud before payouts are made

AI models can process data at a scale and speed that human teams simply can’t match, making predictive insurance a growing reality in modern underwriting.

The Challenges That Make It Feel Like a Myth

Despite these advancements, predictive insurance powered by AI isn’t universal yet. Key challenges include:

Data Quality and Access

Insurers need clean, consistent, and ethically sourced data. Many legacy systems were not built for this level of data integration, slowing adoption.

Regulatory Landscape

Predictive pricing and claims forecasting raise complex compliance questions. Regulators demand transparency on how models make decisions, particularly in sensitive areas like health or auto insurance.

Trust and Transparency

Consumers and sometimes underwriters want to understand why a price or decision was made. AI models can feel like a “black box,” making it harder to build trust without proper explainability measures.

Where AI Predictive Models Are Already Delivering

Despite these challenges, AI-driven prediction is already reshaping certain insurance segments: 

• Auto insurance: Predictive telematics models score driver safety in real time, impacting pricing.

• Property insurance: Climate and weather models anticipate loss patterns to adjust coverage proactively.

• Health insurance: Behavioral data informs wellness incentives and early intervention programs.

• Fraud detection: AI flags anomalies faster than traditional methods, cutting loss ratios.

These are no longer pilot projects; they're live tools helping insurers reduce costs, improve customer experience, and drive smarter decisions.

Final Thought: Myth or Reality? A Bit of Both For Now

Predictive insurance through AI is very real but it’s not evenly applied across the industry. For many carriers, true AI-driven prediction is still aspirational, hampered by legacy infrastructure, governance complexity, and talent gaps.

However, where AI is deployed well, it’s transforming how risk is assessed, priced, and managed. The future will belong to insurers who combine AI’s predictive power with human insight, transparency, and a focus on ethical impact.

Comment l’IA personnalise l’expérience bancaire ?

Les attentes des clients envers leur banque ont profondément changé. Ils recherchent aujourd’hui simplicité, rapidité et services sur mesure. L’intelligence artificielle permet désormais aux banques d’offrir une expérience client personnalisée, comparable à celle des géants du numérique.

Mais comment cette transformation se concrétise-t-elle ? Voici un tour d’horizon de l’impact de l’IA sur l’expérience bancaire.

Des conseils financiers adaptés à chaque profil

L’IA peut analyser en temps réel les habitudes de dépenses, les revenus et les comportements financiers pour proposer des recommandations personnalisées : conseils d’épargne, alertes sur les dépenses, suggestions de budget, ou encore anticipation des découverts.

La banque devient ainsi un véritable assistant personnel, disponible à toute heure pour aider les clients à mieux gérer leur argent.

Une segmentation plus fine et plus pertinente

Grâce à l’IA, les banques dépassent les segmentations traditionnelles (âge, revenu, statut) et s’appuient sur des données comportementales. Elles peuvent ainsi proposer des offres adaptées au style de vie de chaque utilisateur, qu’il s’agisse de produits de crédit, d’assurance ou d’investissement.

Cette personnalisation améliore la pertinence des services et renforce la fidélité des clients.

Un service client plus fluide avec des assistants virtuels

Les chatbots intelligents permettent aux clients de poser des questions, consulter leurs comptes ou effectuer des opérations simples, sans passer par un conseiller. Ces assistants virtuels évoluent avec l’usage, comprennent les préférences des utilisateurs, et savent transférer la demande à un humain si nécessaire.

Le résultat : un service plus rapide et moins contraignant.

Une anticipation proactive des besoins

L’intelligence artificielle peut détecter de nouvelles habitudes (comme un changement de statut professionnel) et proposer des solutions financières adaptées : compte professionnel, assurance dédiée, options d’épargne ou d’investissement.

Cette capacité à anticiper renforce la position de la banque comme partenaire de confiance, au-delà de son rôle traditionnel.

Une sécurité renforcée, personnalisée pour chaque client

L’IA apprend à connaître les comportements habituels des utilisateurs et peut détecter immédiatement une activité suspecte. Ce niveau de vigilance personnalisé réduit les risques de fraude tout en évitant les alertes inutiles.

Les clients bénéficient ainsi d’une sécurité renforcée, sans perte de fluidité dans leur expérience.

Vers une relation bancaire plus empathique

Certaines banques testent des technologies d’analyse d’émotions dans les interactions client, pour adapter leur ton et mieux répondre aux situations sensibles. Même si ces approches sont encore en développement, elles ouvrent la voie à une relation plus humaine, même à distance.

Conclusion

L’intelligence artificielle transforme en profondeur la manière dont les banques interagissent avec leurs clients. Elle rend les services plus personnalisés, plus efficaces et plus sûrs.

Pour les établissements bancaires, cette transformation est une opportunité stratégique. Pour les clients, c’est la promesse d’une expérience plus fluide, plus utile, et centrée sur leurs besoins réels. 

Les tendances Insurtech à suivre en 2025

Le secteur de l’assurance vit une transformation profonde portée par la technologie. En 2025, les insurtechs ne se contenteront plus de numériser les processus existants : elles réinventeront l’ensemble de la chaîne de valeur, de la souscription à la gestion des sinistres.

Voici les 6 grandes tendances à surveiller pour comprendre où va l’innovation dans l’assurance.

1. L’intelligence artificielle devient le moteur principal de l’automatisation

L’IA est désormais au cœur des modèles insurtech. En 2025, elle sera utilisée à toutes les étapes : évaluation des risques, détection de fraude, gestion des sinistres, tarification dynamique.

Par exemple, AXA France a développé, avec Microsoft, une plateforme interne baptisée AXA Secure GPT. Basée sur l’IA générative, elle permet d’ajuster les offres aux antécédents médicaux ou au mode de vie : services de prévention personnalisés ou offres santé ciblées.

De leur côté, des startups comme Shift Technology utilisent l’IA pour détecter des fraudes en analysant des millions de transactions.

Les assistants conversationnels intelligents réduisent aussi les délais de traitement, offrent un support 24h/24, et améliorent l’expérience client de bout en bout.

2. L’assurance embarquée s’impose comme nouveau standard

L’assurance ne se vend plus, elle s’intègre. En 2025, le modèle de l’assurance embarquée ("embedded insurance") devient la norme, proposée automatiquement au bon moment — lors de l’achat d’un téléphone, d’un voyage ou d’un service.

Exemples notables :

• AppleCare propose une couverture dès l’achat d’un appareil Apple.

• Booking.com intègre des options d’assurance voyage au moment du paiement.

• Stripe propose aux commerçants d’offrir une assurance à leurs clients via son API.

Cette intégration contextuelle permet d’atteindre des clients qui n’auraient pas souscrit à une assurance classique.

3. Des modèles d’abonnement plus flexibles pour répondre aux nouveaux usages

Avec l’évolution des modes de vie (freelance, mobilité, économie à la demande), les clients attendent plus de flexibilité. En 2025, les insurtechs proposent des formules à la carte ou par abonnement, facilement activables ou désactivables via une app.

La startup française Luko permet par exemple de suspendre son assurance habitation quand on est en déplacement prolongé.

Ces offres s’inspirent des standards de consommation modernes (Netflix, Spotify), et séduisent particulièrement les générations Z et milléniales.

4. Une assurance plus inclusive, portée par la donnée alternative

Les données alternatives issues des objets connectés, réseaux sociaux ou plateformes de mobilité ouvrent de nouvelles perspectives.

La startup Pula, active en Afrique, utilise les données météo et agricoles satellitaires pour assurer les petits agriculteurs, jusque-là exclus des produits classiques.

Autre exemple : Zego, au Royaume-Uni, propose des assurances flexibles pour les livreurs ou chauffeurs VTC, en s’appuyant sur des données d’usage en temps réel (heures de conduite, distance parcourue).

Ces modèles permettent une inclusion assurantielle plus large, notamment dans les économies émergentes.

5. Un écosystème de partenariats plus stratégique

Les insurtechs ne visent plus à remplacer les assureurs traditionnels, mais à collaborer avec eux. En 2025, les partenariats stratégiques deviennent un levier clé : startups agiles + acteurs établis + géants technologiques.

Par exemple :

• Swiss Re collabore avec des insurtechs pour co-développer des produits.

• Generali noue des alliances avec des acteurs de la healthtech pour enrichir ses offres santé.

• Des acteurs cloud comme AWS ou Azure fournissent l’infrastructure sécurisée des nouvelles plateformes insurtech.

Ces synergies favorisent l’innovation tout en garantissant la solidité réglementaire et financière.

6. La blockchain gagne du terrain dans la gestion des sinistres

En matière de transparence et d’automatisation, la blockchain apporte des solutions puissantes.

Des startups comme Etherisc ou Chainlink expérimentent les smart contracts pour des assurances paramétriques : retard de vol, aléa météo, hospitalisation… L’indemnisation est automatique dès qu’un événement validé est détecté.

Bien que cette technologie reste marginale, elle se développe notamment dans les micro-assurances et les marchés émergents, où la rapidité et la confiance sont essentielles.

Conclusion : une assurance plus intégrée, intelligente et centrée sur l’utilisateur

En 2025, les insurtechs redéfinissent les règles du jeu. IA, personnalisation, intégration fluide, inclusion… l’assurance devient proactive, flexible et contextuelle.

Les compagnies traditionnelles devront s’adapter à ces standards ou risquer de perdre en compétitivité. Car plus que la technologie elle-même, c’est l’expérience utilisateur qui devient le nouvel avantage concurrentiel.

AI in WealthTech: Where the Next Wave of Innovation Lies

Artificial intelligence is not just a feature in WealthTech—it’s the foundation of the next generation of solutions. 

Our methodology involved gathering numerous venture maps from around the world to identify recurring categories and sources of innovation in AI. From this extensive research, we developed the Mandalore AI in WealthTech Venture Map 2025, which captures the current state of the art in AI technology and innovation. Using these insights, we analyzed how innovation is driven across different sectors and crafted this article to highlight the key trends and opportunities shaping the future of AI.

AI enables dynamic portfolio optimization

AI is redefining portfolio construction through hyper-personalization and continuous optimization. Algorithms can ingest investor goals, risk tolerance, and real-time market data to dynamically rebalance portfolios. This enables scalable, advisor-like services delivered automatically, with less human intervention and greater adaptability.

While unlocking private markets through automated sourcing and valuation

Access to private assets is being democratized and de-risked through AI-powered deal sourcing, valuation modeling, and scenario simulation. Machine learning models uncover hidden opportunities and automate diligence processes, creating a competitive edge in an opaque and fragmented landscape.

And turning financial planning into adaptive guidance

AI transforms static financial plans into living, breathing systems that adjust to life changes in real time. By integrating behavioral data and predictive analytics, platforms can guide users proactively—recommending decisions, anticipating shortfalls, and making planning feel less like a spreadsheet and more like a conversation.

As well as modernizing compliance with intelligent monitoring

Legacy compliance processes are being replaced by intelligent monitoring systems that learn from data and flag risks before they materialize. AI enhances transparency and reduces manual workloads, making it possible for firms to scale governance and stay ahead of evolving regulations with minimal friction.

While also enhancing market insight through unstructured data analysis

AI mines unstructured data—from news to social feeds—to generate real-time insights and sentiment indicators. This empowers investors to make faster, more informed decisions and unlocks new alpha from sources that traditional models overlook.

And finally personalizing client experience with predictive interfaces

AI personalizes the advisor-client relationship at scale. From conversational interfaces to predictive nudges, AI enables firms to deliver tailored advice, anticipate needs, and build trust—making digital wealth platforms feel human, even when no one is on the other end.

How is AI reshaping InsurTech ?

AI unlocks unprecedented underwriting value through non-traditional data processing, while simultaneously enabling substantial margin improvements via automated claims handling and fraud detection. Furthermore, behavioral prediction engines dramatically reduce acquisition costs, just as sector-specific applications improve loss ratios and create new premium pools. Finally, dynamic pricing optimization delivers defensible advantages through improved ratios and conversion rates.

Our methodology involved gathering numerous venture maps from around the world to identify recurring categories and sources of innovation in AI. From this extensive research, we developed the Mandalore AI in InsurTech Venture Map 2025, which captures the current state of the art in AI technology and innovation. Using these insights, we analyzed how innovation is driven across different sectors and crafted this article to highlight the key trends and opportunities shaping the future of AI.

AI unlocks unprecedented underwriting value through non-traditional data processing

The most promising AI investments in underwriting target the opportunity in reducing mispriced risk. Algorithms now process thousands of non-traditional variables that traditional actuarial models miss completely. The emerging gold rush is in proprietary data acquisition strategies that feed these models with unique signals beyond standard industry datasets. We're particularly bullish on computer vision applications that can extract property characteristics remotely, eliminating the need for costly physical inspections while dramatically improving accuracy of risk assessment.

While enabling margin improvements via automated claims handling and fraud detection

Claims processing represents perhaps the largest near-term ROI opportunity in insurtech, with potential margin improvements through AI automation. The value creation formula is straightforward: each percentage point of fraud detection improvement could translate to annual savings industry-wide. We see immediate traction for solutions combining computer vision for damage assessment with natural language processing for claims documentation analysis. The most investable opportunities are emerging at the intersection of these technologies, where end-to-end claims automation platforms can deliver increasing processing rates.

Behavioral prediction engines dramatically reduce acquisition costs

With customer acquisition costs in insurance being high, AI-powered distribution efficiency represents a massive opportunity. The most compelling investment cases are platforms that leverage behavioral prediction engines to identify high-conversion prospects before competitors. The next frontier will be conversational AI that can handle complex insurance consultations with human-like understanding of coverage nuances, effectively democratizing expert-level insurance guidance.

Just as sector-specific applications improve loss ratios and create new premium pools

Sector-specific AI applications are producing the fastest path to market leadership. In auto insurance, companies deploying telematics with behavioral analysis algorithms are decreasing loss ratios below industry averages. Life insurers leveraging continuous underwriting models through wearable data are expanding their addressable market by making coverage accessible to previously uninsurable populations. The cyber insurance sector presents the most asymmetric return profile, where AI that can quantify previously unmodeled risks creates entirely new premium pools..

Finally, dynamic pricing optimization delivers defensible advantages

AI-driven pricing represents the most defensible competitive advantage in insurance. The investment opportunity lies in platforms that balance pricing optimization with regulatory compliance through explainable AI. Dynamic pricing engines that can continuously adjust to market conditions without human intervention are a big opportunity. The next wave of innovation will come from causal inference algorithms that simulate customer responses to price changes, allowing insurers to optimize elasticity at the individual level.

The Future of AI: Key Technologies and Breakthrough Opportunities Transforming Industries

This article explores the major AI technology categories reshaping industries today. From foundational language models to generative content creation, computer vision, robotics, and cybersecurity, it highlights the core innovations driving new use cases and efficiencies. It also emphasizes the growing importance of ethical AI governance to ensure responsible adoption across sectors.

Our methodology involved gathering numerous venture maps from around the world to identify recurring categories and sources of innovation in AI. From this extensive research, we developed the Mandalore AI Techno Venture Map 2025, which captures the current state of the art in AI technology and innovation. Using these insights, we analyzed how innovation is driven across different sectors and crafted this article to highlight the key trends and opportunities shaping the future of AI.

Foundation models and LLMs are transforming language understanding

Foundation models and large language models (LLMs) are revolutionizing machine understanding and generation of natural language. These models serve as the backbone of modern AI, capable of performing a wide range of tasks with minimal supervision. Innovation is happening at multiple levels: from developing new, more efficient architectures, to fine-tuning models for domain-specific applications. Open-source ecosystems and infrastructure tools are expanding access, while autonomous agents and AI copilots are beginning to act independently across productivity tools and enterprise workflows.

Meanwhile, generative AI powers content creation

Generative AI enables machines to create original content across text, images, video, code, audio, and even 3D models. In creative industries, this means automated content production, real-time media editing, and synthetic design. For developers, new AI coding assistants accelerate software development and testing. Audio and music generation platforms provide personalized media experiences, while generative 3D tools transform asset creation in gaming, digital twins, and immersive environments.

At the same time, computer vision interprets visual data

Computer vision allows machines to interpret and act on visual information, unlocking a broad range of applications. In industrial contexts, AI can detect manufacturing defects, monitor quality, and optimize production lines. In healthcare, it assists in analyzing medical imaging to support diagnostics. Vision-based surveillance systems are transforming security operations, while autonomous driving relies on real-time image processing to navigate dynamic environments. Facial recognition and biometrics further extend vision’s reach into authentication and identity verification.

While NLP drives language recognition

NLP technologies extract meaning from unstructured language data. Machine translation tools bridge language barriers across global organizations. Text summarization and information extraction streamline document processing, legal analysis, and research. Augmented search capabilities combine retrieval and generation to provide accurate, context-aware responses in enterprise knowledge systems. In voice, real-time transcription and synthetic voice cloning enable more natural and scalable human-machine interaction.

Additionally, robotics and automation enhance efficiency

AI-driven robotics is reshaping physical work across sectors. Humanoid and task-specific robots are being deployed in manufacturing, retail, and service industries. Warehouses are increasingly automated through intelligent systems that move, sort, and package goods with minimal human input. Edge computing enables real-time decision-making in low-latency environments like vehicles or sensors. Smart city infrastructure leverages AI to manage traffic flow, safety, and urban logistics.

Also, AI in science speeds up drug discovery and materials innovation.

AI is fast becoming a core tool in scientific discovery. In life sciences, it accelerates drug discovery by modeling molecule interactions and predicting treatment outcomes. In material science and chemistry, AI models generate new compounds with specific properties, drastically reducing the time required for R&D. These technologies not only enhance research productivity but also open new possibilities across medicine, energy, and sustainability.

Meanwhile, AI for cybersecurity improves threat detection and protection

Cybersecurity is evolving with AI on both sides of the threat landscape. Security operations are becoming more autonomous, with AI systems detecting and responding to incidents in real time. Deepfake detection and malicious content identification help combat new forms of digital fraud. AI-specific guardrails are emerging to monitor prompt injection, data leakage, and model misuse—ensuring safer deployment of large-scale AI systems.

Still promoting AI Ethics & Governance

As AI becomes more powerful and pervasive, governance frameworks are essential to ensure transparency, fairness, and accountability. Tools that audit model behavior, track data provenance, and enforce compliance standards are being embedded across industries. AI monitoring systems detect drift, bias, and anomalies, while governance platforms help organizations align model development with ethical principles and regulatory requirements.

Corporate Venture Building : un levier stratégique pour les conseils d’administration

À l’ère du digital, la survie des grandes entreprises dépend de leur capacité à innover rapidement. Alors que l'espérance de vie moyenne d'une entreprise est passée de 90 ans en 1935 à un peu plus de 10 ans aujourd’hui, les conseils d’administration doivent désormais jouer un rôle actif dans la transformation de leurs organisations.

Le Corporate Venture Building : une réponse stratégique

Le Corporate Venture Building (CVB) s’impose comme un levier stratégique puissant pour créer de nouvelles sources de revenus tout en renforçant la résilience de l’entreprise. Ce modèle hybride permet de :

• Tirer parti des actifs internes (clients, données, expertise sectorielle…)

• Reproduire l’agilité des start-ups

• Réduire les risques tout en accélérant l’innovation

Selon les experts, les entreprises qui adoptent ce modèle peuvent multiplier par 14 leurs chances de bâtir un business à forte croissance par rapport aux start-ups classiques.

🎯 4 leviers clés pour réussir un programme de Corporate Venture Building

Fixer des objectifs clairs et ambitieux

Définir une vision long terme, des axes de développement prioritaires, et des indicateurs de performance (OKR) est essentiel. Le conseil d’administration doit aussi statuer tôt sur la stratégie : spin-in (intégration au cœur de l’entreprise) ou spin-out (filiale autonome).

Adopter une logique de portefeuille et de financement progressif

Plutôt que de miser sur un seul projet, les entreprises les plus performantes adoptent une approche portefeuille, avec des décisions d’investissement basées sur des étapes clés (stage-gates). Cela permet d'optimiser le capital investi tout en réduisant les risques.

Mettre en place une gouvernance agile

L’un des freins majeurs à l’innovation est la lenteur des processus décisionnels. Pour réussir, il faut donner aux équipes une réelle autonomie, instaurer un cadre clair, et s’inspirer des meilleures pratiques des fonds de capital-risque.

Attirer et fidéliser les meilleurs talents entrepreneuriaux

Le succès d’un corporate venture repose sur ses fondateurs. Il faut savoir attirer des profils entrepreneurs/intrapreneurs et mettre en place des systèmes d’incentives inspirés des start-ups (participations, phantom shares, autonomie stratégique…).

📌 Leçons du terrain : le cas Axiata Digital

Le groupe télécom Axiata a lancé son programme de CVB en 2014. Résultat : des filiales comme Boost (wallet), ADA (data & marketing) ou Aspirasi (micro-financement) ont levé plus de 100 millions de dollars et généré des relais de croissance majeurs.

Leur recette du succès ?
👉 Une gouvernance claire, un capital dédié, des équipes autonomes, et une approche rigoureuse du portefeuille.

✅Pourquoi les conseils d’administration doivent s’impliquer dès aujourd’hui

Dans un monde où l’innovation est une question de survie, les conseils d’administration doivent :

• Challenger la vision long terme

• Soutenir l’investissement dans des projets disruptifs

• Créer un environnement favorable à l’expérimentation et à la prise de risque contrôlée

Le Corporate Venture Building est bien plus qu’un buzzword. C’est une stratégie d’innovation structurée, mesurable et scalable, capable de transformer en profondeur les modèles économiques.