Artificial intelligence is becoming a bigger part of financial services. It is being used to assess credit, detect suspicious transactions, analyse investments and support financial decisions.
But there is still a basic problem. How do you know why an AI system made a particular decision?
A model may reject a loan application, flag a transaction or identify a customer as high risk, but simply producing a score does not explain what led to that result. For analysts, compliance teams and regulators, understanding the reasoning behind an AI decision can be just as important as the decision itself.
This is the problem Faisal Umar is trying to address through Fasai.uk.
What is Fasai.uk?
Fasai.uk is an AI-driven financial intelligence platform built around explainability.
The platform brings together several financial AI tools, including financial crime detection, credit risk modelling, investment analysis, portfolio optimisation and a live forex signal system.
The focus is not simply on producing a prediction. The aim is to show why the system reached that prediction and give users information they can actually review.
The forex system follows the same approach to transparency. Instead of showing only successful trades, it records both winning and losing signals.
“Most signal platforms only show the wins,” Faisal explains. “I wanted to show the complete record. If a system is going to be trusted, its losses should be visible as well.”
From Research to Practical Systems
Fasai.uk has grown alongside Faisal’s academic research.
He is the first author of two peer-reviewed IEEE conference papers and has three additional peer-reviewed journal publications covering areas including machine learning in fraud prevention, FinTech ecosystems and financial market innovation.
Rather than keeping the research separate from his technical work, Faisal has used those findings when developing the systems within Fasai.uk.
The result is a connection between academic research and practical financial technology.
Real-World Applications
The work has also gone beyond demonstrations and academic projects.
Faisal has independently designed and deployed AI systems for real organisations. One fraud detection system reduced manual monitoring intervention by 90%. An inventory intelligence system reduced discrepancies by 65% and reduced reporting time from a full working day to around ten minutes.
Another multi-branch reporting system improved inventory accuracy by 20% and reduced reporting errors by 40% across five locations.
These projects gave Faisal the opportunity to work with real operational data and constraints rather than relying only on controlled datasets.
Making Explainable AI More Accessible
Large financial institutions already have access to sophisticated AI and compliance systems. Smaller organisations often have fewer resources and smaller technology teams, despite facing many of the same challenges around transparency and responsible AI.
This is where Faisai.uk aims to make a difference.
The platform focuses on providing explainable financial AI that can be understood and reviewed rather than treating machine learning as a black box.
The systems are also publicly available through GitHub, allowing developers and researchers to examine and build on the work.
“Financial transparency should not be something only large institutions can afford,” Faisal says. “I want to build AI systems that people can understand, question and trust.”
Faisal Umar is an AI researcher and FinTech founder based in Glasgow, Scotland, and the founder of Fasai.uk.
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