How The Intersection OF Fintech, AI And Robust Infrastructure Is Shaping The Future OF Finance

Source: Supplied. Justin Poiroux, chief technology officer and co-founder of Yellow Card.

Fintech is revolutionising financial services by streamlining transactions and expanding accessibility, while AI empowers industries through automation and predictive analytics. As these technologies intersect, they present an opportunity to tackle global challenges and foster a more interconnected, equitable financial ecosystem.

AI has long been an integral part of fintech, enhancing infrastructure behind the scenes through machine learning, natural language processing and computer vision. The latest technology advancements, particularly in AI agents, build upon this solid foundation, significantly amplifying capabilities. Unlike previous technologies confined to narrow functions, modern AI agents serve as specialised virtual experts, seamlessly integrating into specific financial domains.

At Yellow Card, for example, we have deployed AI-driven chatbots to support customers around the clock, while biometric and identity verification systems leverage AI to improve efficiency and reduce friction in the user experience. This evolution underscores AI’s transformative role in fintech, fostering greater accessibility, security and efficiency, while also decreasing user-experience friction.

Transforming fraud-detection strategies

Importantly, the evolution of AI agents has transformed organisations’ approach to fraud detection, enabling them to deploy specialised Fraud AI Agents trained extensively in institutional AML (Anti-Money Laundering), KYT (Know Your Transaction) and fraud mitigation frameworks. These agents autonomously configure and optimise detection systems with the sophistication that once required years of human analyst expertise.

One critical vulnerability in fraud prevention is regression, where companies, over time, deviate from established policies, procedures and rule sets due to code regression or malpractice. AI agents address this challenge not only by performing core detection tasks but also by enforcing quality assurance, ensuring that compliance standards remain consistently upheld.

AI infrastructure demands significant investment in specialised hardware to scale effectively and support a growing user base. For financial inclusion to thrive at the intersection of fintech, AI and infrastructure, publicly accessible digital ecosystems must be fostered and promoted.

Collaboration drives inclusion

Ideally, leading fintech players and the public sector should collaborate to define, develop and sustain open digital infrastructure, ensuring broad access to AI models and agents. A notable early example is the India Stack, which establishes an equitable digital ecosystem through open APIs available to all.

True inclusivity requires scalable, cost-effective access enabled through strategic partnerships across fintech, AI and infrastructure sectors, bridging gaps between innovation and accessibility.

Greenfield applications of AI, such as Large Language Models (LLMs) and agentic functionality, are still in their formative stages, with notable challenges including hallucinations and unpredictable cost scaling.

An often overlooked yet critical concern is intellectual property protection. Fintech firms must exercise caution when integrating AI infrastructure and tools, given the ease with which sensitive company data, such as intellectual property, personally identifiable information, and trade secrets, can be exposed.

Strategic approach to AI integration

To mitigate risks, organisations must adopt intentional, strategic approaches to AI integration, ensuring that proprietary data remains within internal infrastructure rather than relying on third-party providers like OpenAI.

Over the next five years, agentic AI will become deeply embedded in industries, products and workflows worldwide. AI is poised to evolve beyond its current chatbot implementations, replacing existing backend systems, automating workflows and redefining administrative functions. Emerging innovations like Model Context Protocol (MCP) and Agent2Agent Protocol will facilitate even deeper AI integration into existing platforms, establishing a foundation for transformative fintech solutions.

A key shift will be the reduced barrier to entry for organisations looking to build in-house AI infrastructure rather than outsourcing to SaaS providers. This trend will empower fintechs to operate more efficiently, increase self-reliance and optimise cost structures, driving a more adaptive, resilient industry.