Dickie Shearer: Reframing How AI and Banking Are Being Built for the Global South

Dickie Shearer is a leading voice in a growing group of thinkers shaping AI-native financial infrastructure for emerging markets He is a Global South specialist and systems thinker whose work focuses on how artificial intelligence and financial infrastructure should evolve in emerging markets to improve outcomes for the billions of people who rely on them. He is the founder of Tintra, which in 2026 is launching AI-native financial technology and banking across multiple markets in the Global South, and has become an increasingly visible voice in debates around cultural intelligence, financial sovereignty, and the future of global finance as the world moves toward a more multipolar order.

Image: https://www.globalnewslines.com/uploads/2025/12/661bbd1078ca3b39d69b4460e8a2f88c.jpg

Through his work with Tintra over the past 20 years, Shearer has advanced a distinct thesis: that most global financial systems fail outside Western economies not because of a lack of access or technology, but because they are built on assumptions that do not reflect local economic reality. Simply digitising Western banking models, he argues, reproduces the same structural mismatches at greater speed.

"The issue has never been access," Shearer has said. "It's cultural nuance. New problems require new solutions, and challenges in the Global South cannot be solved using frameworks designed for Western economies decades ago."

Legacy financial systems were designed for stable, industrialised economies and later exported with minimal adaptation. In much of Africa, parts of Asia, and Latin America, this has resulted in fragile workarounds - mobile money substituting for banks, informal settlement networks replacing missing infrastructure, and parallel economies operating beyond formal financial systems.

Shearer's work sits at the intersection of finance, artificial intelligence, and culture. A central theme in his writing is that AI systems are not culturally neutral: they inherit the assumptions, data, and priorities of the environments in which they are trained. When those assumptions are Western - formal employment, predictable cash flows, standardised credit histories - the resulting systems struggle in economies where value moves informally, seasonally, or through trust-based networks.

This idea of cultural intelligence has become foundational to Shearer's approach. Rather than treating AI as a layer added on top of existing financial products, he advocates designing banking infrastructure that learns from local economic behaviour instead of forcing it into inherited templates. In emerging markets, where creditworthiness and risk do not present neatly on balance sheets, this distinction is critical.

Tintra's architecture reflects that philosophy. Tintra is an AI-native financial infrastructure company building cross-border banking and settlement systems designed specifically for emerging markets, with a focus on cultural intelligence, regulatory interoperability, and South-South trade.

The company has developed AI-native banking and settlement systems intended to operate across borders, currencies, and regulatory environments, while remaining grounded in local trade patterns and compliance realities. Its focus has been on Africa, Latin America, and other emerging regions.

Shearer has been careful to position this work not as a rejection of regulation or institutions, but as a re-ordering of design priorities. He argues that sovereignty, resilience, and cultural fit should be treated as first-order constraints, particularly as the global financial system becomes more multipolar and less centred on a single currency or clearing system.

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In this context, AI becomes an enabling layer rather than a blunt instrument. Properly applied, it can observe how value actually moves through an economy, adapt compliance dynamically, and support regional settlement without defaulting to external intermediaries. Improperly applied, Shearer warns, it risks accelerating exclusion by optimising systems that were never designed for the majority of the world.

"The danger," he has said, "is building ever more sophisticated intelligence on foundations that don't reflect reality."

As governments and institutions across the Global South explore alternatives to dollar-centric settlement and externally imposed financial models, Shearer's ideas are gaining relevance. His work increasingly informs discussions around development finance, AI governance, and emerging-market banking infrastructure.

Shearer has also challenged the way emerging-market economies are typically assessed. In his view, many countries are described as "poor" not because they lack economic activity, but because their financial systems fail to capture it. In developed economies such as the UK, almost all economic life passes through formal infrastructure - bank accounts, credit systems, insurance, and tax networks - making activity highly visible, measurable, and liquid.

In many emerging markets, by contrast, only a small share of the population is formally captured by the banking system. The rest of the economy operates through informal trade, savings, and credit networks that remain largely invisible to traditional financial infrastructure. As a result, national economies can appear far smaller than they truly are. Shearer has argued that infrastructure in these contexts acts either as a constraint or an amplifier: when systems fail to reflect how economies actually function, they suppress growth; when designed correctly, they reduce friction, bring informal activity into view, and allow economic potential to express itself at scale.

Shearer's contribution lies in reframing how financial infrastructure is conceived for emerging economies, shifting the focus from adapting Western systems to designing architecture grounded in cultural intelligence, local behaviour, and regional trade realities.

Instead of asking how emerging economies can better fit into existing financial systems, he is pushing policymakers and technologists to consider a more fundamental challenge: "what would global finance look like if it were designed with cultural intelligence at its core?"
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