Speaker Name: Gal Hadad

What's one thing you hope attendees take away from your session?

That greater analytical sophistication should not require greater opacity. As digital-asset activity becomes more complex and the decisions around it become more consequential, institutions should be able to ask not only what did the system conclude?, but why? The evidence, sources, methodology, confidence, temporal context and limitations behind a material finding should remain available for scrutiny.

In one sentence, what do you do and what is your role?

I’m Co-Founder and CEO of Bitint, where we are building an explainable blockchain-intelligence platform for institutions and regulated teams that need to understand digital-asset activity and make defensible decisions at scale.

What's the biggest challenge facing the industry in this area?

The challenge is no longer simply obtaining more blockchain data. It is turning a growing volume of fragmented, heterogeneous information into conclusions that are both operationally useful and intellectually defensible. Blockchain activity, attribution, behavioural patterns, external datasets and other contextual sources can each provide part of the picture. The harder problem is determining what can actually be established from them, distinguishing evidence from inference, preserving uncertainty where it exists and doing this consistently at institutional scale. As automation increases, maintaining that analytical discipline becomes more important, not less.

What's one piece of advice you'd give to someone working in fintech today?

Design for scrutiny, not only for speed. The strongest systems are not merely those that produce an answer quickly; they allow the people relying on that answer to understand its basis, challenge it when necessary and determine where the evidence ends and inference begins.

What book, podcast, newsletter, or resource would you recommend to our audience?

Moonshots with Peter Diamandis is a podcast I find useful for thinking in longer time horizons about how technology reshapes entire systems, particularly its techno-optimist framing around exponential change in AI, data infrastructure and financial systems. I also follow broader research and commentary that sits at the intersection of AI, digital assets and institutional infrastructure, as it helps contextualize how today’s analytical and financial primitives may evolve into more autonomous, intelligence-driven systems over time.

What topic will you be speaking about at FinTech Connect 2026?

Beyond scores and labels: explainable blockchain intelligence. I’ll be discussing how institutions can combine analytical depth, diverse sources and methodological transparency to understand digital-asset activity without reducing complex findings to opaque outputs – and how that can support defensible decisioning across screening, risk, investigation and monitoring.

Just for fun: if you could have dinner with any person, past or present, who would it be and why?

Bertrand Russell. Beyond his philosophical and mathematical contributions, I admire his relentless commitment to logical clarity, rigorous reasoning, and the discipline of separating what can be known from what is merely assumed. His work on logic, language, and epistemology reflects a deep insistence on evidence, structure, and analytical precision. That distinction between belief, inference, and demonstrable fact feels increasingly important as analytical systems grow more powerful.

What industry trend are you watching most closely?

The convergence of digital assets with mainstream financial infrastructure. Stablecoins, tokenized assets and blockchain-based settlement are increasingly moving beyond specialist crypto markets. As regulated institutions participate more deeply, digital-asset activity becomes part of a much broader financial context – and the infrastructure used to understand that activity will need to mature with it.

Why is this topic particularly important right now?

Because the consequences attached to digital-asset intelligence are changing. An output may increasingly inform onboarding, transaction decisions, escalation, exposure management, investigations or ongoing monitoring within regulated organizations. In those environments, identifying something as risky is only part of the requirement. Teams also need to understand what drove the conclusion, what evidence supports it, how current that evidence is and what remains uncertain. Explainability is therefore becoming less a presentation feature and more a property of trustworthy decision infrastructure.

What's one prediction you have for the next 3-5 years?

Blockchain intelligence will evolve from a specialist analytical category into a more deeply embedded layer of financial decision infrastructure. The winning platforms will not be differentiated solely by the number of chains, labels or signals they cover. The advantage will increasingly come from the ability to compound proprietary analytical and attribution capabilities, integrate diverse sources, preserve provenance and uncertainty, and make that intelligence consumable by both human teams and authorized AI systems without turning the underlying reasoning into a black box.