Our investment strategy stands on a strong foundation
Multiple global banking technology companies have scaled in our FinTech Labs. An area of our core expertise and right to win.
We enter early-stage but at revenue-stage when at least 1-2 paying enterprise clients exist. This mitigates some risk. Additionally, our ability to value-create and supercharge GTM is unique. This excites us.
Banking and Technology veterans with complementary skillsets, building product-oriented companies. Strong unit economics and a path to profitability are natural.
Enterprise technology for Financial Services is geography agnostic. Our ability to take portfolio companies to dollar-revenue markets such as the Middle East and Europe is a key investment factor.
AI is essential to our investment thesis, shaping the next generation of Financial Services.
AI in the Financial Services industry is a structural capability shift, not a standalone category – and value accrues to systems that are domain-specific, workflow-embedded, and regulation-aware. Explainability, auditability, and security are not differentiators but prerequisites for adoption.
The Financial Services industry is inherently suited for AI given its data intensity, regulatory complexity, and process-heavy operations, and adoption will scale as governance clarity improves alongside enterprise-grade controls. Over time, domain-specific models and governance layers are likely to matter more than generic ones.
Our focus is on AI-native infrastructure, workflow automation, and enterprise copilots across credit, compliance, fraud, and operations – targeting systems that directly improve cost efficiency and decision accuracy.
The market is shifting from systems of record to systems of decision, with competitive advantage moving away from UI toward proprietary data, workflow ownership, and regulatory integration. AI is accelerating this transition, making feature-level differentiation progressively weaker and workflow control increasingly more valuable.
Generic AI layers and undifferentiated applications are avoided in favor of companies with strong data moats, workflow control, and institutional adoption readiness. The bar is regulatory defensibility, durable margins, and a demonstrated ability to operate effectively in sensitive environments.
Opportunities are evaluated through moat depth, model risk, unit economics, and enterprise readiness, with portfolio strategy emphasizing domain specialization and AI-driven margin expansion. The overarching objective is to back platforms that can evolve into intelligent financial infrastructure over time.
Formal partnerships with key financial institutions and ecosystems
