Vice President

Global fintech sector revenues grow more than traditional banking. Link to Leaders August 19, 2026 The Global Fintech report concludes that the sector is recovering, with $504 billion in revenues and growth of 22%. The study "Global Fintech Report 2026: From Recovery to Resurgence" developed by Boston Consulting Group (BCG) in partnership with FT Partners, assesses the current state of the fintech sector and identifies the trends that will shape the next phase of fintech development. And one of its first conclusions is that, globally, the sector has entered a new phase of consolidation and growth, marked by greater profitability, operational discipline, and an increasingly relevant role in the transformation of financial services. It notes that in 2025, global fintech revenues exceeded $504 billion, a 22% increase year-over-year and an expansion rate more than four times higher than that of traditional financial institutions. According to the study, fintechs currently account for about 4% of global financial services revenues, a figure that confirms their evolution from an emerging segment to a sector with its own scale, still with ample growth potential. Several indicators confirm this maturity: 74% of the largest listed fintechs are already profitable, compared with 68% in the previous year, and the average operating margin (EBITDA) rose from 16% to 20%. Simultaneously, equity financing increased by 53% to $58 billion, following the return of investment to companies with more solid models, greater operational discipline, and sustained growth prospects. Last year, exit markets regained traction. The number of initial public offerings (IPOs) in the fintech sector grew 50% to 42 deals, while the global volume of mergers and acquisitions increased from $105 billion in 2023 to $184 billion in 2024 and $251 billion in 2025. Artificial Intelligence (AI) is also changing the way the sector competes. According to the report, fintechs that effectively apply this technology are achieving development productivity gains up to five times higher, with the most evident impact in areas such as engineering, risk assessment, compliance, and customer support. The true differentiator of AI lies in its ability to redesign workflows and operating models to generate concrete gains in efficiency, scale, and performance. Among the most relevant changes identified in the report is the progressive narrowing of the regulatory gap between banks and fintechs. For example, in the US, the UK, and the European Union (EU), licensing processes and obtaining banking status are becoming more accessible, albeit accompanied by requirements in terms of governance, risk, compliance, and supervision. In the past year, several large-scale fintechs have moved forward with applications for federal banking licenses in the US, seeking to reduce funding costs, gain greater control over product offerings, and strengthen the direct relationship with customers. The study also highlights that the number of federal banking license applications and new deposit institutions increased more than fivefold between 2024 and 2025, signaling a growing approach of fintechs to the traditional regulatory perimeter. The report by Boston Consulting Group and FT Partners also highlights the evolution of neobanks as one of the most structuring dynamics of the next phase of the sector. The main operators are expanding their value proposition to areas such as credit, investment, insurance, international transfers, and savings solutions for clients with greater financial capacity - moving from single-product models to more complete financial platforms, with greater ability to deepen customer relationships. Consumer credit, in particular, emerges as one of the most relevant expansion fronts. As Pedro Pereira, Managing Director & Senior Partner at BCG Lisbon, points out, "the fintech sector is no longer a promise of disruption but has become a structural pillar of the financial system, registering growth 4x higher than incumbents."

Y Combinator joins $75m round in US AI startup Model ML. Model ML, an AI startup based in London and New York, has raised US$75 million in early-stage funding led by FT Partners, with participation from Y Combinator, QED Investors, 13Books Capital, and LocalGlobe. Model ML will use the new funds to expand its teams in San Francisco, New York, London, and Hong Kong, and to hire more AI engineers. The company, founded about a year ago by Chaz and Arnie Englander, develops technology designed to automate tasks often handled by investment bankers, such as preparing pitch decks and due diligence reports. The startup previously raised US$12 million earlier this year but did not disclose its valuation for either round. Model ML's advisory board includes former HSBC CEO Noel Quinn and ex-UBS chairman Axel Weber. The firm has relocated its engineering team to London's King's Cross area, citing cost considerations. Food for thought. * Model ML raised $75M at an undisclosed valuation with little proof of banks in production or pilots. Endorsements from former HSBC CEO Noel Quinn and ex-UBS chair Axel Weber do not confirm deployments or revenue. * The 'Trusted by Industry Leaders' banner stays vague without names. The company has not disclosed Annual Recurring Revenue (ARR), named paying banks, or measured gains such as time saved per deal or accuracy lifts. * The team moved engineering to London's King's Cross for cost control. That move signals a focus on burn rate (its cash spend rate) while $75M must cover four offices and product-market fit in a cautious banking sector. * Model ML focuses on single-tenant (each bank gets an isolated instance), self-hosted (software runs inside the institution's environment) deployments inside a customer's Azure setup (Microsoft's cloud platform). This creates demand for cloud infrastructure and AI governance platforms (tools to manage model risk plus compliance). Compliance automation vendors can add evaluation frameworks, model monitoring, or data residency solutions (keeping data in specific jurisdictions). * The startup lets users query third-party data vendors like PitchBook and Crunchbase in natural language plus real-time as well as proprietary datasets. Middleware (software that connects disparate systems) and Application Programming Interface (API) management help run GenAI across fragmented data stacks without compromising security or auditability. Model ML offers no-code workflows, which means engineers do not write software. IT teams want quick time-to-value with fast deployment that avoids multi-year rollouts while minimizing technical debt (the downstream cost of maintaining quick, short-term fixes). How would you feel if you could no longer use Tech in Asia?