Generative AI Examples in Finance: Real-World Fintech Use Cases
In this article, we’ll break down where generative AI is delivering proven value in fintech today and how to build a path toward it.
In this article, we’ll break down where generative AI is delivering proven value in fintech today and how to build a path toward it.
Fintech Development Outsourcing in Vietnam is examined in depth: its talent, cost advantages, engagement models, best-suited domains, and leading partners.
The pattern is consistent: success rarely depends on a single breakthrough decision. It’s the result of getting foundational choices right early, especially when selecting a development partner.
At Sun*, during the process of working with clients’ fintech mobile apps, we found the hard truth that mobile is not a constrained browser. It’s a compiled binary running on hardware you don’t control, inside a runtime environment that a motivated attacker can instrument and manipulate at a depth no web session ever exposed.
At Sun*, we work with financial institutions navigating exactly this inflection point. In this article, we break down the strategic framework for deciding which technology layers to own, which to buy, and what the stakes of getting it wrong look like in 2026.
The hardest part of an embedded finance engagement isn’t the technology. It’s the pace. Our PM breaks down what it actually takes to operate inside a live payment system — and why DNA match determines everything.
In this article, we break down what AI underwriting is, where traditional approaches fall short, and how machine learning transforms each step of the underwriting process.
We break down the top 06 tech stacks shaping fintech development in 2026 – what each one solves, where it earns its place in a serious financial architecture, and the real-world lessons our teams have learned building with each of them in production.
In this article, we break down what the PCI DSS standard actually means for your code, your infrastructure, and your deployment workflows.
Rules are static, but fraud is dynamic. We show how fraud detection and prevention in banking industry systems use ML to scale in under 200ms.