I build decision intelligence systems for regulated financial services, and keep their decisions defensible when a regulator, board or operator asks how they were reached.
Building decision intelligence for regulated financial services from Singapore, inside the MAS regime.
Operating under the Monetary Authority of Singapore's regime for AI in financial services.
Dual degree in Business Administration and Data & Business Analytics.
Advice-led platforms, risk and advice models, and the data foundations beneath them.
Computer vision on medical imaging, where an answer nobody can explain is worth nothing.
These are the positions I argue from, on a panel or in a room with a leadership team. Each one comes out of building the thing rather than reading about it.
The model is usually sound. What tends to be missing is a clear definition of the decision it serves, an owner for that decision, and an agreed course of action for when the model is uncertain. Settle those and the accuracy problem usually recedes.
A recommendation nobody can justify carries risk rather than value, however good its metrics look. Treated as a requirement from the outset rather than a constraint bolted on late, explainability is what makes a system deployable at all.
Autonomy applied to a process nobody has mapped produces untraceable errors quickly, not leverage. Understanding the decision first is unglamorous work, and it is most of the job.
Four kinds of problem. They overlap, and the first one is usually the reason for the other three.
Modelling a decision explicitly, including its inputs, its flow, its owner and its escalation path, then building the platform that orchestrates it at scale and monitors the quality of what it produces.
Most enterprises cannot bolt AI onto the systems they already run. I rebuild the foundations so that intelligence belongs to the architecture rather than sitting above it as a layer of automation.
Clinical and financial settings in which error is costly and an answer nobody can explain is worth nothing: computer vision on medical imaging, and risk and advice models in regulated finance.
An AI-assisted delivery lifecycle whose gates, reviews and traceability are held in code rather than in a model's instructions. Agents work inside bounds they cannot widen, and every change still reaches a human with the evidence attached.
Two companies, both building decision systems for settings where an error is expensive and an unexplainable answer is useless.
Decision intelligence for fintech, insurtech and healthtech. We rebuild legacy estates onto AI-native foundations, and we build and operate WISDAM, our own decision intelligence platform for advice-led financial services.
A configurable decision intelligence platform for advisor workflows, in which the decision logic and the data model adapt to the institution.
Platforms the client owns outright, built using our accelerated AI-native delivery process.
Decision intelligence for medicine: machine learning for chronic disease detection from retinal scans, and hyperspectral imaging classification for intra-operative decision support.
Machine learning for chronic disease detection from retinal scans, built so that a clinician can see why a case was flagged.
Hyperspectral imaging classification that supports tissue decisions in theatre, where an unexplainable answer is worth nothing.
I am available for panels, podcasts and roundtables on decision intelligence in regulated industries, and for advisory work with boards and leadership teams deciding where AI belongs in their operation.
I speak as a practitioner about work I have done. Client engagements are confidential, so examples are anonymised and the operating detail is the substance.
Blair Ferguson, Co-Founder of PRVW and Founder of Preview Diagnostics, building decision intelligence for regulated financial services from Singapore.
PRVW runs an instance of the AI Readiness Index, the assessment framework developed by AI Singapore. It scores an organisation across leadership, governance, business value, data and infrastructure, and takes about fifteen minutes.
It is a more useful place to start than most AI strategy conversations, because it tells you which of those five pillars is actually holding you back rather than which technology to buy.
prvw.airihub.ioI hold a dual degree in Business Administration and Data & Business Analytics from IE University. Both halves matter equally: modelling earns its keep only when it changes what somebody decides, and a decision improves only when the modelling beneath it is sound.
Since then I have built and shipped production systems across advice-led financial services, clinical diagnostics and sports performance: decision platforms, computer vision on medical imaging, and the data foundations and access control that sit underneath both.
Current client engagements are not yet public, as the work is live and ongoing. I am glad to discuss specifics directly.
I am interested in problems where the decision matters more than the model, particularly in financial services and other regulated settings. For an event, the useful things to send are the date, the format and who else is on the panel.