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Born from breakthroughs in machine learning, generative AI refers to algorithms that can create text, images, code, and even entire virtual environments — tools that mimic human creativity to solve real-world problems at scale.
Generative AI is weaving itself into the fabric of fintech, unlocking new ways to deliver smarter, faster financial services. In emerging markets, it’s driving a quiet evolution, and one that’s making finance more accessible, adaptive, and locally relevant than ever before.
According to Monica Brand Engel, Co-founder and Managing Partner at Quona Capital, in her interview with me, the disruption is not about building flashy Gen AI-first companies, but rather about embedding this technology into practical applications that address deep, systemic inefficiencies. And in doing so, it’s giving fintech firms a sharp edge over their traditional finance counterparts.
Practical Intelligence, Not Hype
Quona Capital, with $800 million in assets under management and 70 portfolio companies across Latin America, South and Southeast Asia, Africa, and MENA, focuses on “fintech for inclusion.” Engel stresses that most of Quona’s portfolio companies are not built around Gen AI, but rather, they use it to supercharge their existing models.
She outlines three core use cases that have already delivered substantial business and social impact.
The first is workflow automation, which is not a novel concept, but one transformed by Gen AI’s ability to process complex, unstructured data. Take Sunday, an insurtech firm in Southeast Asia, which uses Gen AI to automate claims adjudication by interpreting dense documentation with language models. This allows them to fast-track straightforward claims and free up human adjusters for complex cases.
The second use case is hyper-personalized customer engagement. Clark, a challenger bank in Mexico, has used Gen AI to analyze customers’ savings behaviors and nudge them toward better financial decisions, such as switching to interest-bearing accounts.
This personalized approach not only enhances user satisfaction but has also demonstrably improved retention, even when the advice runs counter to the bank’s short-term profit.
Finally, there’s risk management through data augmentation. Vertel, a cross-border payments company, uses Gen AI for Anti-Money Laundering (AML) monitoring by parsing through thousands of documents to detect fraud signals. In jurisdictions regulated by the U.K.’s FCA as well as local financial authorities, this isn’t a nice-to-have, it’s an operational imperative.
From Skepticism to Trust
Introducing Gen AI into markets that have historically been paper-based and people-intensive is no small feat. Engel is quick to note that adoption curves depend heavily on digital readiness, not just among users, but employees as well.
Many customers in emerging markets start with low expectations, given the often poor quality of legacy financial services. In this context, Engel says, Gen AI isn’t replacing something beloved, but offering something radically better.
For example, users may initially feel wary of receiving financial recommendations from a bot. But when that bot speaks in their dialect and provides useful, timely suggestions — often outside traditional business hours — the value becomes clear.
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As Engel puts it, “Who doesn’t want personalized services?” Once the convenience and utility are proven, user resistance quickly gives way to enthusiastic adoption.
Still, trust must be earned. The companies in Quona’s portfolio succeed by serving those whom traditional finance excludes. This mission-driven model helps build credibility. Gen AI tools are embedded in products customers already trust, reinforcing rather than replacing those relationships.
The technology serves a purpose beyond profit; it enhances services for underserved populations in a way that aligns with the companies’ existing brand promise.
Employees: From Fear to Empowerment
Internally, the introduction of Gen AI raises its own set of questions. Engel emphasizes that these companies are not laying off workers. Instead, they are reallocating talent from low-value tasks, such as manually reviewing insurance claims, to more meaningful work.
Many of these employees are drawn to mission-driven startups precisely because of their impact. When they see that Gen AI enables them to serve more customers more effectively, buy-in increases.
This internal adoption is carefully managed through pilots, not sweeping overhauls. Companies roll out Gen AI in targeted segments, evaluate the results, and refine before scaling. Education is key, both to mitigate fear and to ensure successful implementation.
Digital literacy varies widely across roles and age groups, so companies must tailor their training strategies accordingly.
And the payoffs are real: greater efficiency, more engaging work for employees, and improved bottom lines. Most Quona portfolio companies offer equity or performance-based compensation, so employees have a tangible stake in the success of these tech integrations.
Navigating Bumps in the Road
Even as the benefits stack up, Engel is candid about the growing pains. Data integrity is the first hurdle — bad input leads to flawed decisions. From handwritten applications to miskeyed mobile entries, fintech in emerging markets must deal with messy, inconsistent data. This demands robust error-handling mechanisms and continuous model refinement.
Another challenge is bias and hallucination, particularly in high-stakes domains like credit underwriting or insurance. Engel acknowledges the risk but underscores that none of the portfolio companies rely solely on Gen AI. Human oversight remains essential, and applications are built to complement (not replace) expert judgment.
Privacy and governance are also top of mind. All companies using Gen AI must implement clear guidelines on what data can be shared and how it’s used. Engel describes a layered system of internal controls, from multifactor authentication to regular backtesting of outputs. The companies manage their own databases, and while Quona maintains reporting and transparency standards, it never demands access to raw, confidential customer data.
Sharing to Scale
One of the less visible but deeply impactful aspects of Quona’s approach is its platform function, a deliberate effort to ensure cross-pollination of insights across its portfolio. From town halls and guest speakers to direct introductions with domain experts, Quona fosters an ecosystem where lessons learned in Jakarta can benefit founders in Mexico City or Lagos.
This collaborative model accelerates innovation. Engel recalls how Clara, another portfolio company, benchmarked its Gen AI-driven customer service costs against those of NuBank — a company fifty times its size — and found them to be equivalent.
That kind of exponential efficiency only becomes possible when knowledge is actively shared and applied.
What’s Next?
Looking ahead, Engel sees two major fronts for expansion. First, as companies deepen their understanding of the customer data they already collect, more will leverage Gen AI for product innovation. This includes everything from better segmentation to creating entirely new financial offerings.
Second, operational efficiency will continue to scale, making small fintechs increasingly competitive with incumbents, even in cost-heavy areas like compliance and service.
But the real frontier? Engel hints at a future where Gen AI isn’t just a feature…it’s the product. While most current applications are add-ons to existing platforms, the next wave may well include startups built around Gen AI as their core infrastructure. That, she says, is where the next generation of transformative business models will emerge.
At a time when traditional finance still struggles with decades-old systems and cumbersome workflows, fintechs powered by Gen AI are leapfrogging. And with leaders like Engel at the helm, the future of inclusive, intelligent finance looks inevitable.
