AI is now a defining advantage in financial technology. It is powering predictive analytics, dynamic decisioning, and autonomous responses that can reshape markets.
A growing school of thought holds that AI engines can increasingly be trained with smaller, cleaner datasets; leveraging transfer learning, synthetic data, and powerful foundation models. But for agentic AI, the kind that perceives, decides, and acts in real time, the situation flips.
These applications or platforms require access to all relevant data on real time basis to operate effectively.
The Crux of the Problem
For fintech innovators, the richest and most relevant datasets are corporate ones:
● Historical transaction patterns
● Supplier and counterparty performance metrics
● Commercial terms and contract details
● Cash flow and payment behaviours
Yet these are exactly the datasets corporates guard most tightly and for reasons that are totally justified i.e. confidentiality, competitive edge, compliance, and control.
From the corporate perspective, the stakes are high:
● GDPR and related laws mean even commercial data can be regulated if it contains, or can be linked to, identifiable individuals.
● Purpose limitation and data minimisation obligations run counter to the “full picture” access that agentic AI thrives on.
● Data sovereignty and cross-border flow restrictions complicate cloud-based AI deployments.
This is why, a number of AI fintechs find themselves able to design impressive models but unable to gain traction in real world. At best, they are still unable to use the full strength of their solutions and deliver high value to their clients.
Where Possibilities Begin
While no universal solution exists yet, several avenues hint at a way forward:
● Compute where the data lives: Bringing AI runtimes inside corporate environments so raw data never leaves.
● Controlled visibility: Exposing only features, signals, or aggregate scores to AI agents operating externally.
● Privacy-preserving AI: Using techniques like federated learning or secure multi-party computation to protect underlying records.
● Contractual innovation: Embedding data-access governance, audit rights, and IP protections directly into corporate–fintech partnerships.
Each of these approaches addresses part of the problem, but none is a silver bullet. The opportunity lies in combinations and trust frameworks that meet both operational needs and regulatory obligations.
The Open Question
If training can be done with less, but real-time execution demands more,
Can we architect AI ecosystems where “more” is available without losing control?
Those who can bridge that gap may not just solve a technical problem, they may unlock a new era of corporate–fintech collaboration.
Ali is a seasoned fintech and banking professional who specializes in transforming businesses through innovative working capital, trade and supply chain finance strategies.
Over the past 25+ years, Ali has helped top tier banks and technology companies including, J.P. Morgan, HSBC and SAP develop and grow profitable businesses and serve thousands of their clients across the world. Ali has hands-on experience of solving problems for businesses operating in diverse industries, economic and geo-political landscapes in Asia, Middleast and Europe
Ali is passionate about solving problems and building sustainable businesses and relationships and helps businesses in driving business development, technology innovation, strategic partnerships & cusiness transformation.