If you are looking for a fractional CTO who understands both fintech and automated underwriting, you are looking for a narrow overlap. Plenty of technology leaders have shipped fintech products. Far fewer have built and run the decisioning that sits behind a lending business, where a model decides in milliseconds whether to advance money to a real person, and where getting it wrong is a regulatory and financial problem, not a bug.
This is a guide to what that overlap actually requires, the questions worth asking before you hire, and an honest account of where we fit.
Why fintech and underwriting is a specific combination
Fintech leadership and underwriting leadership are not the same skill, and the market treats them as though they are.
Fintech experience tells you someone has worked under FCA expectations, handled money movement, and built to the security bar that banks and payment partners demand. Necessary, and not sufficient.
Automated underwriting adds a harder layer. Now you are running credit risk models that make decisions about individuals, on data that is never as clean as the pitch deck claims, under rules that say you must be able to explain why you declined someone. The technology, the data and the regulation are tangled together, and a leader who knows only one of the three will miss the other two.
A fractional CTO worth hiring for this holds all three at once. They can shape the decisioning architecture, keep the model governance defensible, and answer for the customer outcomes when the FCA asks.
What automated underwriting demands from technology leadership
Six things separate a lending platform that scales from one that gets a business into trouble.
Decisioning architecture. The engine that turns an application into a yes, a no, or a referral, fast and consistently. It has to be fast enough for a live application, and stable enough that a rule change does not have unintended effects three steps downstream.
Data quality and feature pipelines. Underwriting decisions are only as good as the data feeding them. Bureau data, open banking data, application data, all of it needs to arrive clean, on time, and versioned, so a decision can be reconstructed later.
Model governance. Someone has to own how models are built, tested, approved and monitored. Not as a document, as a working discipline, with a clear record of what changed and why.
Explainability and fairness. Under UK GDPR you owe people meaningful information about automated decisions that significantly affect them, and the ICO sets out what that means. Under the FCA’s Consumer Duty you have to show you are delivering good outcomes and not baking in unfair bias. A black box that cannot explain a decline is a liability.
Monitoring and drift. Models decay. The population applying today is not the population the model was trained on, and arrears patterns shift. Without monitoring, performance slips quietly until a collections problem makes it loud.
Auditability. Every decision needs to be reconstructable. When a customer complains, or an auditor asks, you need to show the data, the model version and the rule that produced the outcome.
Miss these and the platform works right up until it does not, usually at the worst moment, when volume is climbing and the model is drifting at the same time.
The regulation that shapes every decision
Automated lending sits inside a dense set of rules, and a fintech CTO who does not know them will build things that have to be unbuilt.
The FCA’s Consumer Duty raises the bar on outcomes, which reaches directly into how you set risk appetite and how you treat borderline applicants. Affordability rules under CONC govern how you assess whether someone can repay. UK GDPR, and Article 22 in particular, governs solely automated decisions with significant effects, so you need the right lawful basis, the right safeguards and a route to human review. The Senior Managers and Certification Regime puts named accountability on the people running all of this.
None of it stops you automating. It shapes how, and a leader who has worked inside it builds decisioning that stays on the right side of the line without grinding delivery to a halt.
Build or buy your decisioning engine
One of the first calls a fractional CTO helps you make is whether to build your decisioning yourself or buy a decisioning platform.
Buying gets you moving fast, with a vendor who has already solved the plumbing and often brings ready-made bureau integrations. The cost is control. You are renting someone else’s logic, you may struggle to explain a decline in your own words, and switching provider later is rarely quick.
Building gives you full ownership of the logic, the data and the audit trail, which matters most when your risk models are part of your edge. It costs more up front and puts the weight of governance and monitoring squarely on you.
There is no universal right answer. It turns on how central credit risk is to your business, how much you need to explain and defend individual decisions, and how fast you have to be in market. A leader who has run both will steer you to the choice that fits your stage rather than the one that suits a vendor.
What to look for when you hire
Real lending experience, not adjacent experience. Ask whether they have actually run underwriting or a decisioning platform, not just worked at a company that had one. The difference shows fast.
Comfort across data, risk and engineering. Underwriting lives at the join. A candidate who can only talk architecture, or only talk models, will leave a gap where the risk sits.
A regulatory instinct. You want someone who reaches for the compliance question early, because retrofitting fairness and explainability into a live model is painful and expensive.
Seniority that matches the stakes. This is not a place to let someone learn on your loan book. Twenty years of judgement is the point of hiring fractionally.
A named person, not a bench. Ask exactly who will do the work and what they have done before. If the senior name at the pitch hands you to someone junior after signing, walk away.
Questions to ask before you sign
- Have you personally built or run automated underwriting, and on what kind of lending?
- How would you keep our decisioning explainable and auditable as we scale?
- What would you check first in our model governance?
- How do you keep delivery moving without cutting corners the FCA will notice?
- Who, by name, will lead this, and how many years have they done the actual job?
Specific, evidenced answers are the signal. Hedging and jargon are not.
Where ScaleAround fits
We are a Cardiff-based technology consultancy and a member of FinTech Wales, and this overlap is close to home.
Our founder, Oliver Smith, ran the AI and machine learning function at a UK consumer lender, where he built automated underwriting and a first-time arrears prediction capability from scratch. Before that, leading technology and quality at the same lender, he delivered the UK’s first virtual credit card and replatformed the core lending application behind 300,000 accounts, taking around £5m a year out of the cost base. He also facilitates sessions at the CDO Financial Services Exchange on the data challenges specific to machine learning, and he is a Fellow of the British Computer Society.
That is the underwriting and lending grounding, built on real accountability for live decisioning inside a regulated lender rather than a passing acquaintance with the topic.
A word on how we work. Oliver leads the company and stays hands-on, and our engagements are led by senior practitioners with at least 15 years of relevant experience, drawn from a vetted network. No junior analysts, no rotating associates. For fintech and underwriting specifically, you get people who have carried the accountability themselves, not people reading about it for the first time on your project.
A worked example
Take a UK consumer lender at Series A with a small engineering team and a founder who is technical but has never run underwriting. Delivery is fine, but an investor has started asking how decisions are made and monitored, and nobody has a clean answer.
A fractional CTO comes in two days a month. In month one they map the decisioning flow, the data pipelines feeding it and the state of model governance, and find the bureau data integration is brittle and there is no monitoring once a model is live. In month two they get model monitoring and decision logging in place, so every decision can be reconstructed, and document the governance in a form that survives an FCA conversation. By month four the decisioning is auditable, arrears are being watched rather than discovered late, and the technology story stands up in the diligence room.
That is the shape of it. Steady senior judgement applied to the few things that decide whether an automated lender scales cleanly or ends up explaining itself to a regulator.
Frequently asked questions
Do you have direct automated underwriting experience?
Yes. Our founder built automated underwriting and arrears prediction inside a UK consumer lender, and has led technology and quality in regulated financial services throughout his career.
Can a fractional CTO really cover something this specialised?
Yes, if the person is genuinely senior in the area. The value of fractional is buying rare, expensive experience for the days you actually need it, rather than trying to hire it full-time.
How do you keep automated decisions compliant?
By treating explainability, fairness and auditability as design requirements from the start, and by keeping model governance a live discipline rather than a document. That keeps you on the right side of the FCA and UK GDPR without stalling delivery.
We are pre-authorisation. Is it too early?
Often it is exactly the right time. Getting the decisioning architecture and governance right before you scale is far cheaper than fixing it under FCA scrutiny later.
How many days a month would we need?
Usually one to three to start, more during a build or a raise, then less once it is steady. We would tell you honestly rather than sell you days you do not need.
If you need a fractional CTO who understands both fintech and automated underwriting, our fractional CTO and CIO service explains how we work, and our case studies show what changed for real businesses. Book a 30-minute scoping call for an honest read on where you stand.