The article is attributed to Priya Korana, Director of Engineering, Vymo.
AI is reshaping financial services. While the sector has been an early adopter of data-driven decision-making, translating AI research breakthroughs into enterprise-grade fintech products remains a challenge. Models that perform exceptionally in controlled research settings often face a steep drop in accuracy, scalability, and compliance readiness when applied to live financial systems.
This disconnect is a product of how AI research is conducted versus how fintech operates. Research environments optimise for accuracy and novelty, while financial institutions prioritise explainability, auditability, and adherence to regulatory norms. A model capable of reducing false positives in transaction monitoring by 20% in a lab setting may fail to clear operational, compliance, or latency thresholds in production. Bridging this divide requires deliberate alignment between the research process and the stringent realities of financial systems.
The operationalisation challenge
Fintech products must operate at high scale, with low tolerance for error, and in compliance with a shifting regulatory environment. For example, a payments fraud detection system processing millions of transactions per hour must combine real-time decision-making with the ability to justify each flagged transaction to regulators. While research teams may focus on optimising model precision or recall, operational AI in fintech must also incorporate model governance frameworks, bias audits, and rollback protocols.
This is where the gap widens. While 83% of financial services leaders see AI as a strategic priority, fewer than 20% have successfully deployed models at scale across multiple business units. The remainder are caught in a cycle of proof-of-concept pilots that never cross the threshold into production. Overcoming this requires not just better technology, but a stronger organisational interface between research and engineering, risk, and compliance functions.
Embedding research in the product lifecycle
In fintech, AI research cannot remain isolated from the product development lifecycle. The most successful implementations are those where data scientists, product managers, and compliance officers work in parallel from the earliest stages of design. This integrated approach ensures that accuracy gains in the model translate into tangible business value without triggering downstream operational or regulatory bottlenecks.
For example, in credit scoring, research teams might identify a new ensemble modelling approach that improves risk prediction for thin-file customers. Embedding this early into product design allows engineering to optimise infrastructure for model serving, while compliance teams prepare explainability documentation that meets jurisdiction-specific credit transparency rules. The result is not just a more accurate model, but one that reaches the market faster and with fewer regulatory hurdles.
The role of responsible AI in fintech
The stakes for responsible AI are particularly high in fintech, where biased or opaque models can lead to financial exclusion, regulatory penalties, and reputational damage. Global regulators are increasingly demanding explainability and fairness in AI-driven financial decisions. In Europe, the proposed AI Act classifies credit scoring as a high-risk application, requiring rigorous transparency and risk controls. Similar frameworks are emerging in the US, Singapore, and India.
This underscores the need for fintech firms to build research pipelines that integrate fairness metrics, synthetic data testing, and ongoing bias monitoring from the outset. The cost of retrofitting these measures late in the product cycle is significantly higher, and in some cases, prohibitive. AI research for fintech must therefore expand its definition of ‘performance’ to include compliance readiness and social responsibility alongside accuracy and efficiency.
Towards business impact
The fintech sector’s AI ambitions will only be realised when the path from breakthrough research to production deployment is seamless and repeatable. This requires rethinking organisational structures, incentives, and skill sets. Leaders must ensure that AI research teams understand the commercial and regulatory context of their work, while business teams are equipped to appreciate the capabilities and limitations of AI models.
Some of the most forward-looking fintechs are creating hybrid roles, AI product managers, for instance, who can translate between research, engineering, and compliance. Others are investing in internal model marketplaces that allow teams to discover, adapt, and deploy approved AI models without duplicating effort. Such mechanisms shorten deployment cycles and increase the return on research investment.
Closing the gap
As fintech competition intensifies, the ability to operationalise AI research quickly and responsibly will be a key differentiator. Firms that succeed will be those with the most effective pathways from innovation to impact. This means integrating research into the product lifecycle, embedding responsible AI practices from the start, and ensuring that every breakthrough is measured not only by its technical merit but by its readiness for the real-world demands of financial systems. For fintech, bridging this gap is the foundation for sustained innovation, regulatory trust, and customer value in an increasingly AI-driven financial landscape.