AGIBOT, a Chinese robotics company specialising in embodied artificial intelligence, has launched operations in Malaysia, marking the beginning of its Asia-Pacific expansion strategy.

The company held a launch event on Tuesday at i-City in Selangor, attended by Malaysian government officials and industry partners, coinciding with the opening of an AI and Robotics Experience Centre developed with local property developer I-Berhad.

Also Read: SGInnovate backs Botsync in extended Series A amid AMR market surge

The timing reflects broader shifts in Southeast Asia’s manufacturing landscape. Rising labour costs, supply chain reshoring, and government-backed automation initiatives are driving demand for robotics solutions across the region.

Malaysia’s established electronics manufacturing base, relatively business-friendly regulatory environment, and central geographic position make it a logical entry point for regional expansion.

Market position and scale

AGIBOT, founded in 2023, has achieved rapid scaling. The company claims to have shipped over 5,100 humanoid units in 2025 and was ranked No. 1 globally by market research firm Omdia for humanoid robot shipments and market share, capturing 39 per cent of the global market. The company’s 5,168th mass-produced unit demonstrates industrial-scale manufacturing capability—a significant milestone for a robotics sector historically characterised by limited production volumes.

“AGIBOT made significant strides to improve the mass production and the practical use of embodied robotics last year,” said Deng Taihua, Founder, Chairman, and CEO. “This milestone puts AGIBOT in a strong position as we start 2026.”

AGIBOT’s approach differs from traditional industrial robots designed for single, repetitive tasks. The company focuses on embodied AI systems that learn and adapt in real-world environments through reinforcement learning, enabling robots to be trained and deployed directly in operational settings rather than relying solely on pre-programmed sequences.

Malaysia partnership and testbed model

The AI World Experience Centre represents a departure from conventional robotics commercialisation. Rather than focusing exclusively on factory automation, AGIBOT and I-Berhad are deploying robots across hospitality, property management, and urban operations at i-City. The partnership plans to develop the world’s first AI and Robotics Residential Tower, positioning residential environments as a testbed for humanoid robot integration.

Also Read: Serving up the future: How robots are revolutionising the F&B industry

“This launch marks the first of several strategic initiatives we will roll out in the Asia-Pacific region throughout 2026 and beyond,” said Abel Deng, President, Asia-Pacific & Middle East Region, AGIBOT.

Tan Sri Lim Kim Hong, Chairman of I-Berhad, stated that the initiative aims to advance Malaysia’s positioning as a regional AI innovation hub and establish “a new benchmark for intelligent, human-centric living in the region.”

Product portfolio

AGIBOT’s commercial offerings span multiple use cases: the A2 series for reception and hospitality; the X2 series for entertainment and education; the G2 series for industrial manufacturing; the D1 series for inspection operations; and the C5 autonomous floor-care robot.

The company targets eight application areas, including reception, entertainment, industrial manufacturing, logistics, security, data collection, and research.

Regional context

AGIBOT’s expansion arrives at a pivotal moment for robotics adoption across Southeast Asia. While the region’s robotics sector remains smaller than those in Singapore or South Korea, growth is accelerating. Rising labour costs across manufacturing hubs like Malaysia, Thailand, and Vietnam are driving automation investments. Supply chain reshoring initiatives, accelerated by pandemic disruptions, are creating new opportunities for local robotics deployment.

The sector, however, faces challenges. Compared to mature markets, Southeast Asia has a smaller installed base of industrial robots, lower overall adoption rates in some industries, and a developing ecosystem of local robotics companies. Yet these barriers also represent opportunities for players like AGIBOT to shape market standards and build early-mover advantages.

Also Read: Robotics, space, sustainability: The forces shaping Asia’s next tech chapter

Key trends include increased AI integration, expansion beyond manufacturing into services, and growing emphasis on localised solutions tailored to regional needs.

Strategic trajectory

AGIBOT frames the Malaysia launch as the first of multiple 2026 initiatives. The company indicates plans to expand partnerships across Asia-Pacific and deploy robots in “closed-loop commercial scenarios”, operational environments where continuous learning and improvement can occur. The emphasis on robots-as-a-service models suggests a subscription-based approach rather than outright sales.

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When Toku registered its offer document for a proposed listing on the Catalist Board of the Singapore Exchange, the announcement landed as more than just another IPO filing. For Singapore’s tech startup ecosystem, it signalled a potential inflexion point that touches public market confidence, regional-first tech building, the evolution of AI infrastructure and how startups scale across Asia.

At a time when global tech IPOs remain selective and private capital is increasingly disciplined, Toku’s move carries broader implications for founders and investors navigating 2026.

Perhaps the most immediate impact of Toku’s listing is narrative-driven rather than numerical. Singapore has only just begun to see the return of IPO activity among venture-backed tech companies in 2025. Against that backdrop, Toku’s decision to pursue a local listing reinforces the relevance of SGX Catalist as a viable exit route for growth-stage tech firms.

For founders, this matters. IPOs shape long-term decision-making well before a company reaches the listing stage. A credible example like Toku helps re-anchor expectations around governance, capital efficiency, and operational maturity — qualities that public markets now prioritise more than rapid, loss-driven expansion.

It also sends a message that startups do not necessarily need to look overseas for liquidity events. In an era of heightened geopolitical and regulatory complexity, a domestic listing pathway reduces execution risk while keeping companies anchored in Singapore’s ecosystem.

Also Read: Nasdaq tumbles, but Bitcoin soars past US$97K on massive short squeeze

Validating “Asia-built-for-Asia” tech

Toku’s positioning as an AI-powered customer experience platform, built specifically for complex, multilingual, and regulated markets, aligns closely with Singapore’s evolving tech identity. Rather than exporting standardised global products, the company has focused on solving problems that are particularly acute across the Asia Pacific, the Middle East, and emerging markets.

This reinforces a broader shift underway in the ecosystem: a growing emphasis on regionally grounded enterprise technology rather than consumer platforms or copycat SaaS models.

For investors, Toku’s IPO helps validate the commercial potential of startups that prioritise:

– Local compliance and data sovereignty
– Multilingual AI performance
– Deep integration with telecom and enterprise infrastructure

These are not easily replicable advantages, and they play to Singapore’s strengths as a regulatory-savvy, enterprise-facing tech hub. As a result, Toku’s listing could encourage more founders to pursue defensible, infrastructure-adjacent business models rather than chasing scale through uniform global deployment.

While AI remains a dominant theme across the startup landscape, much of the recent attention has focused on applications and user-facing tools. Toku’s story highlights a different layer of the AI stack: infrastructure that enables enterprises to deploy AI securely, compliantly and at scale.

Its end-to-end control over connectivity, orchestration and AI applications underscores the growing importance of systems-level innovation, particularly for regulated industries such as financial services, healthcare and government.

Also Read: Toku files for SGX Catalist IPO, doubles down on partner-led go-to-market strategy

This has implications for how investors assess AI startups in 2026. Rather than asking only what models or features a company offers, there is increasing scrutiny around how AI is deployed, governed, and integrated into existing workflows. Startups that can demonstrate robustness in these areas may find stronger traction with both enterprise buyers and capital providers.

Normalising partner-led go-to-market strategies

Equally significant is Toku’s emphasis on a partner-led go-to-market strategy as it scales beyond its core markets. Instead of relying solely on direct sales expansion, the company is doubling down on channel partners, systems integrators, and ecosystem alliances to efficiently enter new regions.

This approach reflects the realities of scaling in fragmented markets, where local knowledge, regulatory familiarity and on-the-ground execution often matter more than centralised sales teams. For other startups, Toku’s playbook offers a practical alternative to capital-intensive expansion models.

In 2026, this could influence how early- and growth-stage companies design their products and pricing with partners in mind from the outset. It also strengthens the role of Singapore-based enterprises, telcos, and consultancies as distribution enablers for regional startups.

More broadly, it suggests a maturing ecosystem where success is measured not only by the speed of expansion, but also by sustainable market entry and long-term customer value.

Also Read: The quiet currency shift: Southeast Asia’s strategic pivot to a multipolar monetary era

A signal beyond the listing

Taken together, Toku’s IPO represents more than a single company reaching the public markets. It reflects a broader recalibration underway across Singapore’s tech ecosystem, where investors and founders alike are placing renewed emphasis on fundamentals. In place of growth-at-all-costs narratives, there is a clearer preference for disciplined execution, clear paths to sustainability and tech that solves real, structural problems.

The listing also highlights a shift towards regional relevance over generic scale. Toku’s success has been built on addressing the complexities of multilingual, regulated and infrastructure-constrained markets, rather than forcing uniform solutions across geographies. This approach reflects a growing conviction that Singapore-based startups can succeed by embracing regional specificity, rather than competing directly with global incumbents on their own terms.

Equally important is what Toku’s story signals about the evolution of AI in enterprise settings. As excitement around AI matures, attention is shifting beyond surface-level applications to infrastructure, governance, and deployment at scale. Platforms that prioritise compliance, reliability and integration are increasingly viewed as more defensible and more valuable over the long term.

For founders, Toku’s IPO helps reset expectations around what success looks like in 2026. The pathway to public markets is no longer defined solely by speed, but by operational maturity, partner ecosystems, and credible regional strategies. These are traits that require time and patience to build, but which are increasingly rewarded.

While Toku’s long-term performance as a listed company remains to be seen, its decision to list locally already carries weight. It broadens the ambition set for Singapore’s tech sector, offering a concrete example of how companies can scale responsibly, remain regionally grounded and still access public market capital on home soil.

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The lead image is AI-generated.

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Asia-Pacific agrifoodtech startups raised US$4.2 billion in 2024, capturing 31 per cent of global sector funding. Yet across Southeast Asia, where agriculture remains a primary livelihood for hundreds of millions of people, this investment systematically misses the majority of farming communities. This isn’t just a market inefficiency—it’s a fundamental failure to understand how rural communities actually function.

Here’s the uncomfortable truth: every single agricultural app is designed for an imaginary user.

Picture Sari, a rice farmer in Central Java. Her smartphone buzzes with a weather alert suggesting she plant early because conditions are perfect. Sounds helpful, right? Except she can’t plant anything. Her village irrigation group hasn’t released water to her section yet. The planting schedule was decided three weeks ago in a community meeting she attended, but didn’t lead. Her father-in-law, the village elder, and the irrigation chief made that call together.

That weather app just became expensive digital noise.

This is the story of how Southeast Asian agritech is optimising for individual users in a world that makes collective decisions—and why the communities that need innovation most are getting left behind.

The fundamental misalignment

The person using your app isn’t the decision maker

The core problem isn’t technical sophistication or user experience design. It’s that the person using your app is rarely the person with power to change anything.

Consider the typical agritech user journey:

• Information flows to individuals (weather alerts, market prices, agronomic advice)
• Decisions are made collectively (planting schedules, input purchases, marketing choices)
• Impact requires community coordination (water management, pest control, harvest timing)

We’re building perfect steering wheels for passengers.

The three-layer reality of rural decision making

• Individual layer: The farmer who downloads your app, creates an account, receives notifications
• Community layer: Village councils, irrigation groups, farming cooperatives that make actual resource allocation decisions
• Ecosystem layer: Traders, input suppliers, financial institutions that control access to markets and credit

Most agritech stops at individual layer. Real agricultural change happens at community and ecosystem layer.

Also Read: Digital farming’s false promise: Why Asia’s US$180B bet on agritech-driven farming is failing smallholders

Case study: The US$15M weather app that changed nothing

AgriWeather (anonymised) raised US$15M Series A to provide hyperlocal weather forecasting to Indonesian farmers.

The pitch deck reality

• 250,000+ downloads in six months
• 85 per cent user retention after first month
• Sophisticated machine learning models
• Beautiful, intuitive interface in local languages

The field reality

In Bali’s rice terraces, they’d never heard of subak—thousand-year-old irrigation cooperatives that manage water allocation across entire watersheds. Planting schedules aren’t individual decisions; they’re collective negotiations about water timing, pest management, and harvest coordination.

When farmers showed the app’s recommendations to their subak leaders, the response was predictable: “We’ll plant when water reaches our section, like we discussed last month.”

The outcome

Great user metrics, zero agricultural impact. The startup eventually pivoted to B2B after burning through their Series A trying to acquire individual users who couldn’t act on their product.

The lesson

Technical sophistication is irrelevant if you’re solving the wrong problem for the wrong decision maker.

The collective decision-making framework

Understanding power structures across Southeast Asia

• Indonesia: Gotong royong (mutual cooperation) principles mean agricultural decisions flow through village councils (RT/RW) and traditional cooperatives (subak, gapoktan)
• Thailand: Farmer cooperatives and Royal-initiated agricultural groups maintain significant influence over individual farming practices
• Vietnam: Commune-level agricultural cooperatives (HTX) coordinate resource allocation and market access for member farmers
• Philippines: Barangay councils and irrigation associations (IA) make collective decisions about water management and crop timing

The four decision archetypes

• Water and timing decisions: Controlled by irrigation groups and village councils
• Input and credit decisions: Managed through cooperatives and traditional lending relationships
• Knowledge and technique decisions: Shared through farmer groups and extension networks
• Market and sales decisions: Constrained by existing trader relationships and cooperative marketing

Implication: Successful agritech must integrate with these existing structures, not bypass them.

Also Read: Indonesia’s agritech landscape: Keys to building a scalable agriculture startup

What actually works: The community-centred model

Case study: AgriCooperative’s US$50M success

Instead of targeting individual farmers, AgriCooperative worked through existing farmer cooperatives:

The approach

• Village leaders became part of the credit assessment process
• Community members provided social collateral
• The platform recognised that rural financial decisions involve community reputation

The results

• 1,847 farming families accessed US$12M in credit
• 94 per cent repayment rate (vs 67 per cent industry average for individual lending)
• Average household income increased 23 per cent over 18 months
• Platform became integral to existing community decision-making processes

Why it worked

They built for the reality of collective decision-making instead of fighting it.

The new framework: Community-centred agritech

Design principles for rural reality

• Map Power Before Building Product

• Who actually makes agricultural decisions in target communities?
• How do information and resources flow through village structures?
• What existing institutions could be enhanced rather than replaced?

• Design for Collective Intelligence

• Individual apps that feed into group decision-making processes
• Platforms that strengthen existing community coordination mechanisms
• Tools that make collective decision-making more efficient, not obsolete

• Measure Community-Level Impact

• Agricultural outcomes across entire villages or cooperatives
• Strengthening of traditional knowledge and decision-making systems
• Economic improvements at the household and community level

• Build Sustainable Revenue Models

• Community-centred solutions often require different monetisation approaches
• Success may come through institutional partnerships rather than individual payments
• Value creation happens at the ecosystem level, not just user level

Also Read: How Southeast Asia’s agritech startups are turning smallholder farms into high-tech powerhouses

Implementation roadmap

Phase one: Deep community research (three-six months)

• Ethnographic study of agricultural decision-making in target regions
• Mapping of existing community institutions and power structures
• Identification of technology integration points within collective systems

Phase two: Community partnership development (6-12 months)

• Build relationships with village leaders and cooperative managers
• Co-design solutions with community decision-makers
• Pilot programs that enhance existing coordination mechanisms

Phase three: Scaled community integration (12+ months)

• Deploy solutions through established community channels
• Measure impact at village and cooperative level
• Refine based on community-level feedback and outcomes

The investment challenge and opportunity

Why VCs struggle with community-centred models

• Traditional VC Metrics:

• Individual user acquisition costs
• Monthly active users
• Revenue per user
• Individual customer lifetime value

• Community-centred Metrics:

• Community adoption rates
• Collective behaviour change
• Village-level agricultural outcomes
• Ecosystem-wide economic impact

The multi-billion dollar opportunity

• Current market focus: Southeast Asian agritech primarily serves educated, connected, individually empowered farmers—a small fraction of the total agricultural population
• Underserved market: The vast majority of rural populations making collective decisions through traditional institutions like cooperatives, irrigation groups, and village councils
• Market potential: ASEAN agricultural production alone totaled 195.5 million tons of rice, 44.7 million tons of corn, and 82.1 million tons of cassava in 2022, representing massive economic activity largely untouched by current agritech solutions

New investment framework

• Community traction metrics:

• Number of village councils or cooperatives actively using the platform
• Collective decisions influenced by platform insights
• Community-level agricultural outcome improvements
• Integration depth with existing institutional structures

• Revenue model innovation:

• Institutional partnerships with cooperatives and government extension services
• Value-based pricing tied to community-level outcomes
• Revenue sharing with traditional institutions rather than competing with them

The competitive advantage of getting this right

Companies that successfully engage rural communities through collective decision-making systems will access currently underserved agricultural markets while building sustainable competitive advantages through deep community integration.

• Policy alignment opportunity

Asian Development Bank research highlights that Southeast Asian governments prioritize strengthening rural statistical systems and agricultural coordination—community-centered solutions align with these policy goals while improving agricultural outcomes.

• Investment trends supporting community-centred models

The 38 per cent year-over-year increase in Asia-Pacific agrifoodtech funding demonstrates growing investor confidence in agricultural innovation, creating opportunity for solutions that demonstrate genuine community-level impact rather than just individual user metrics.

Also Read: Need of the hour: How agritech platforms can protect farmers from climate change

The choice point for Southeast Asian agritech

• The individual path (Current trajectory)

• Target educated, connected farmers representing a small fraction of the agricultural population
• Compete on user experience and technical features
• Measure success through app-store metrics and individual user engagement
• Generate venture returns through individual customer acquisition in urban-adjacent markets

Outcome: Sophisticated solutions serving a narrow market segment while the majority of agricultural communities remain underserved

• The community path (Untapped opportunity)

• Partner with existing rural institutions like cooperatives and village councils
• Enhance collective decision-making processes that govern resource allocation
• Measure success through community-level agricultural outcomes and livelihood improvements
• Generate returns through ecosystem-wide value creation and institutional partnerships

Outcome: Access to vast underserved agricultural markets while creating genuine rural development impact

Call to action: Building for rural reality

• For entrepreneurs: Stop building for imaginary individual farmers. Start with deep community research. Map power structures before writing code. Design for collective intelligence rather than individual optimization.
• For investors: Fund founders who understand rural power dynamics. Demand community-level impact metrics before major funding rounds. Recognise that community-centred solutions may have different growth curves but potentially larger ultimate markets.
• For the ecosystem: Question who actually benefits from “agricultural innovation.” If the answer is primarily urban, tech-savvy users, we’re solving the wrong problems with the wrong metrics.

The agricultural community question

Southeast Asian agritech has attracted billions in investment to serve the region’s agricultural communities. Yet most of those communities remain unreached by the innovation they’re funding.

The technology exists. The market need is massive—ASEAN’s agricultural output continues growing, with rice production forecast to increase to 202.34 million tons in 2024. The missing piece is understanding that rural communities don’t function like individual app users—and that’s not a problem to solve but a reality to build for.

Rural Southeast Asia is ready for agricultural innovation. The question is whether the 31 per cent of global agrifoodtech funding flowing to Asia-Pacific will finally reach the communities that actually exist, not the individuals we imagine.

The communities are waiting. What are we going to build for them?

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Over the past few years, AI in the enterprise has been about trials and piloting of new solutions. While over 70 per cent of banking institutions globally use agentic AI today, only 16 per cent have moved to actual operational deployment. This gap reveals a critical truth: the barrier to production scale is not technology. It is organisational clarity and strategic intent.

In 2026, as we move beyond AI hype to reality, executives across financial services, insurance, healthcare, and technology will face a decisive question: are we committing to production-scale deployment, or continuing to cycle through experiments?

The upside for organisations is substantial. McKinsey estimates AI could unlock US$340 billion in annual value for banking alone. Yet less than one per cent of global executives report significant ROI from AI investments, defined as 20 per cent or greater improvement in profitability or cost savings. Only three per cent report a substantial ROI of 10 to 20 per cent. This disparity reflects a systematic failure of execution.

Across industries, we see that when institutions successfully transition from pilots to production scale, they achieve improvements in processing speed, automation rates, and regulatory compliance readiness.

The strategic shift: From cost reduction to autonomous efficiency

Organisations that are succeeding at production scale are not optimising for headcount reduction, but instead, they are optimising for autonomous efficiency. This means they are using AI to eliminate routine work completely so humans can focus on revenue-generating activities.

For example, this could include a lending algorithm approving customer applications instantly without human review or a fraud detection system blocking suspicious transactions automatically. Proven revenue levers include hyper-personalised cross-sell and upsell, financial inclusion lending powered by alternative data, premium digital wealth management, and AI-augmented compliance and fraud prevention.

Also Read: Looking beyond the bots: The unsexy digital skills that actually matter in 2026

This reframing of autonomous efficiency changes ROI calculations fundamentally. According to Larridin’s 2025 Enterprise AI Report, enterprises measuring AI properly report average productivity improvements of 27 per cent, time savings of 11.4 hours per knowledge worker weekly, and cost reductions of US$8,700 per employee annually. But these improvements accrue only to organisations that commit to autonomous workflows rather than basic human augmentation layers.

For example, the production-ready approach Dyna.Ai has pioneered demonstrates this principle. By operating at sub-200 millisecond response times with over 95 per cent accuracy, the platform enables businesses to deploy autonomous agents across lending decisioning, fraud detection, and customer engagement workflows. These are not experimental applications. They are production systems handling millions of transactions with consistent, measurable performance.

Southeast Asia is laying the foundations for enterprise AI success

Singapore has long been an innovation testing ground, and now it is at a unique inflection point. With the National AI Strategy 2.0, fostering nearly 900 AI startups and attracting US$1.04 billion in fintech investment in H1 2025 alone, the country and the region are establishing themselves as a production hub for enterprise AI. Unlike markets where AI remains primarily experimental, Southeast Asia is seeing financial institutions move directly from exploration to execution. 

Southeast Asian banks are doubling AI-driven value. For instance, one bank grew from US$273 million to US$555 million year-over-year, while DBS generated US$565 million from 350 use cases in 2024 through RM co-pilots and inclusion lending. 

The acceleration of AI adoption reflects both market dynamics and regulatory clarity. The Monetary Authority of Singapore’s sandbox approach and ASEAN regulatory frameworks are creating conditions where institutions can deploy AI at scale with defined governance structures.  

Also Read: 4 marketing trends that will dominate budgets and strategies in 2026

The C-Suite must lead AI governance, infrastructure, and delivery

Moving from pilot to production requires establishing robust governance frameworks before scaling. This means creating detailed AI system inventories, prioritising use cases with clear business value, ensuring data quality, and designing workflows to enable AI autonomy with human oversight. Production success hinges on delivering AI directly into workflows like RM consoles and mobile apps, implementing policy-as-code governance, and pursuing smart partnerships: buy to explore, partner to scale using embedded squads, API-first integration, and revenue-linked contracts.

According to the Bank of England’s Artificial Intelligence in UK Financial Services survey, 84 per cent of firms have established accountable persons for their AI frameworks, and 72 per cent allocate accountability for AI use cases to executive leadership. Yet the same survey reveals that 46 per cent of firms report only partial understanding of the AI technologies they deploy, particularly those sourced from third parties. This gap between governance structures and technical understanding underscores why production-scale deployment requires simultaneous investment in governance clarity, data modernisation, and organisational capability-building.​

Data infrastructure matters equally. Financial institutions increasingly recognise that real-time data capabilities and robust data governance are foundational to production-scale AI deployment. Organisations that establish this infrastructure early, alongside accountability structures and technical understanding of their AI systems, will execute production deployment faster than those attempting simultaneous infrastructure modernisation and AI scaling.

The production transition won’t happen immediately, and organisations must establish foundational infrastructure first, operationalise early wins with measurable business metrics, then scale from demonstrated success. Those executing this roadmap rigorously will move from being stuck in AI pilots to achieving actual production scale. 

With AI adoption growing across sectors, the demand for solutions is evident. What remains is organisational will. In 2026 and beyond, that will be the differentiator between leaders and laggards across financial services, insurance, technology, and beyond.

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There’s a paradox at the heart of modern international business.

We’ve digitised nearly every aspect of operations. Marketing runs on sophisticated attribution models. Sales teams use AI-powered CRMs. Supply chains are optimised down to the last mile.

Yet when it comes to moving money across borders—one of the most fundamental business functions—most companies are still using infrastructure designed for a pre-digital era. And paying dearly for it.

The opacity problem in cross-border payments

Traditional banking thrives on information asymmetry.

When you execute an international wire transfer through a legacy bank, you’re operating in a black box. The exchange rate you receive isn’t the interbank rate—the actual rate at which currencies trade. It’s a marked-up version, typically 1.5-2% above market.

This markup doesn’t appear as a line item. It’s embedded in the rate itself. Your finance team sees money leave in one currency and arrive in another, with what appears to be normal conversion loss.

The real question is: how much of that “conversion loss” is actual market movement, and how much is bank margin?

Most companies can’t answer that question. And that’s precisely the point.

The economics of scale—working against you

Here’s what makes this particularly painful for growing businesses.

As you scale internationally, this problem compounds. A startup processing $100K in cross-border payments loses $1,500-$2,000 annually to FX markups—annoying, but not existential.

A mid-market company processing $10M? That’s $150,000-$200,000. Suddenly, hidden FX fees rival your entire marketing budget.

Yet finance teams often lack visibility into this cost center because it’s not categorized as “fees”—it’s just absorbed into COGS or operational expenses.

This is margin erosion in its purest form: value quietly transferred from your business to financial intermediaries, with no corresponding increase in service quality.

Also read: Why WorldFirst’s latest move could change how digital platforms scale worldwide

The infrastructure shift that’s already happening

Forward-thinking CFOs have started asking a different question.

Not “What does our bank charge?” but rather “What does international payment infrastructure actually cost in 2025?”

The answer increasingly involves purpose-built fintech platforms designed for cross-border commerce. Companies like WorldFirst have built infrastructure that:

1. Provides transparent, near-market FX rates—typically 0.3-0.5% above interbank, versus 1.5-2% for traditional banks
2. Eliminates hidden intermediary fees—no correspondent banking markups
3. Offers real-time visibility—you know exactly what you’re paying before you commit

This isn’t about chasing marginal savings. It’s about fundamentally rethinking payment infrastructure as a strategic function rather than a commodity service.

The new customer economics

WorldFirst’s current offer for new customers illustrates where the market is heading:

• 5 free international transfers (complete fee waiver)
• 50% reduction in FX costs on ongoing transactions
• Auto-applied incentives (no manual intervention required)

For a business processing $1M annually in cross-border payments, this translates to $10,000-$15,000 in recovered margin, every year, indefinitely.

But the more interesting insight isn’t the promotional pricing. It’s that platforms can afford to offer these economics because their infrastructure costs are fundamentally lower than legacy banking systems.

This is what disruption actually looks like: not flashy innovation, but structural cost advantages that incumbents can’t match without cannibalizing their existing business model.

What this means for strategic planning

If you’re a CFO or finance leader planning for 2025, cross-border payment optimisation should be on your roadmap—not as a “nice to have,” but as a margin protection initiative.

Ask yourself:

• Do we have visibility into our actual FX costs? Not what we’re told they are, but what the market spread actually is?
• Are we treating payment infrastructure as strategic or commodity? If commodity, you’re likely overpaying.
• What would we do with an extra $15K, $50K, or $150K in annual margin? Because that’s what’s at stake.

The pattern we’re seeing

We work with hundreds of scaling businesses across Southeast Asia and beyond. The pattern is unmistakable:

Companies that successfully scale internationally have optimized their payment infrastructure early.

Not because they’re obsessed with saving pennies. But because when you’re operating on 10-20% margins (typical for e-commerce, manufacturing, or marketplace businesses), a 1.5% hidden cost is the difference between sustainable growth and a slow bleed.

The businesses still using traditional banks for cross-border payments fall into two categories:

1. Early-stage companies that haven’t reached scale where it matters yet
2. Mid-market companies with institutional inertia—”we’ve always done it this way”

The latter group is leaving significant money on the table. And in an increasingly competitive global market, that’s a luxury fewer businesses can afford

Also read: What facilitates the adoption of digital currencies in Southeast Asia?.

The action item

This isn’t a dramatic transformation. You don’t need to rip out existing systems or retrain your entire finance team.

It’s a simple evaluation:

1. Calculate your actual annual cross-border payment volume
2. Estimate your current all-in FX costs (including hidden markups)
3. Compare to alternative infrastructure pricing
4. Run a pilot with a portion of your volume

For most businesses with meaningful international exposure, the ROI is immediate and substantial.

The question isn’t whether to optimize payment infrastructure. It’s whether you can afford not to.

Ready to benchmark your current FX costs? Explore WorldFirst’s new customer offer and see where you stand.

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