About Virticus
Built inside banks and lenders. Now built for your firm.
Virticus is a small team of practitioners. We have written fraud and AML rules, built credit models, and automated decisions inside banks, payment firms, and lenders, and built marketing models for retail and consumer-credit brands. We build the same for regulated firms that need them to hold when challenged, and we analyse the evidence when a decision is disputed.
Every rule, model, and workflow we deliver can be explained step by step and reproduced when a regulator, a board, or a customer asks how a decision was reached. Never a black box no one can answer for. Fixed fee and timetable agreed before we start, after a twenty-minute call.
Operating profile
Independent
When we review, we have no stake in what we review; when we build, the independent review goes to your second line or a third party, not to us.
Focused
Decisions that carry money or regulatory exposure, handled end to end.
Evidence-based
Findings anchored to what the system did, not what it claimed.
Confidential
Handled with business and legal sensitivity.
Where we work
The standard
Every output has to survive challenge
The common thread is not sector branding. It is exposure: systems operating where outputs matter, evidence matters, and explanations matter. It is the discipline fraud and AML environments demand, and what Virticus brings to FCA-regulated firms.
Sector 01
Financial Crime
Writing and tuning fraud and AML detection rules across banking and payment environments: authorised push payment (APP) scams, transaction monitoring, card, and application/mule fraud. Rules had to be written at pace, perform under pressure, and stand up when challenged: documented rationale, tested thresholds, traceability, and decision control, not detection performance alone.
Sector 02
Banking & Financial Services
Interest calculation models, driver-based financial models, and Buy Now Pay Later balance logic where errors created financial and regulatory exposure. The risk was not only model failure, but misplaced confidence in outputs affecting customer balances, management decisions, and regulatory standing.
Sector 03
Government & Public Sector
Data systems supporting urgent central government decisions during the COVID-19 period, where information had to be reliable, explainable, and timely under exceptional pressure. Errors in underlying data directly affected public decisions and formal reporting.
Sector 04
Public Health
Analytical and operational tooling at a national public health body, where outputs supported policy-shaping evidence chains and national reporting. The challenge was maintaining control and accountability while systems were used at pace under scrutiny.
Sector 05
Education
AI tools used to generate student feedback, support marking activity, and personalise learning pathways. The risk was consistency, fairness, and the difficulty of defending outputs that reached students without adequate review.
Sector 06
Accounting & Professional Services
AI automation in bookkeeping and submission preparation, where small classification errors or unsupported assumptions could flow through into financial and tax outputs. Validation controls were needed before outputs reached formal reporting.
Sector 07
Transactions
Transaction environments where data quality, consistency, and explainability were under commercial and investor scrutiny. Information supporting value and performance needed to withstand close external challenge during a sale process.
Experience
Where the standard comes from
Practice inside the places that set it: banks, payment firms, lenders, and government. The same problems seen from three sides: the institution that owns the decision, the function that has to evidence it, and the reviewer sent to take it apart.
What we have done
Experience
Banking, payments, lending, financial services, and government. Detection rules, credit and affordability models, marketing analytics, forecasting, regulatory reporting, and the controls around them: built, reviewed, and defended when somebody came looking.
What we know
Expertise
Fraud and AML rule design. Model risk under SS1/23. Creditworthiness and affordability under CONC. Expected credit loss. Automated decisions under UK GDPR as it now stands. Every regime read from the source document, because the summaries are wrong often enough to matter.
What we can do
Capability
A small team, deliberately. Regulatory framing, model development and validation, data and systems architecture, and delivery: enough to carry a problem from diagnosis to something running, without passing it between firms and losing it each time it changes hands.
Method
The same five steps, every time
Discovery, design, build, deploy and test, assure and hand over. The brief may change, but the method does not: scope the decision, design it before writing it, build it in your environment, prove it against real cases, and leave the record with the people who have to answer for it.
Discovery
The workflow or rule set, where it decides something, the scrutiny it has to survive, and the regime that binds it.
Design
The decision path before it is written: inputs, reasoning steps, the human-oversight checkpoint, and the record each run leaves.
Build
Built in your environment, with reproducible logging from the first line.
Deploy & test
Live behind a human-oversight checkpoint and tested against real cases, so the same inputs give the same decision every time.
Assure & hand over
The decision log, a plain-language control write-up, and a working session with your team, so your team owns it.
How we work
The standard is set by what we refuse to do
We read the rule, not the summary
Every regime a deliverable rests on is read from the source document and written up with the date it was read. Summaries are wrong often enough to matter, and a design built on one is wrong in a way nobody notices until it is challenged.
We say when a thing cannot be concluded
Not determined is a finding, and it is the one most reports quietly convert into a pass. Where the evidence is missing we say the evidence is missing, name what would settle it, and leave the conclusion open.
Every figure traces back to where it came from
A number nobody can re-derive is a number nobody can defend. Anything we hand over can be followed to its source and reproduced months later by somebody who was not there.
We would rather hand back the smaller answer
The wider claim that cannot be shown is worth less than the narrow one that can. Work that overreaches gets found out at the worst moment, in front of the people it was meant to convince.
The firm keeps what we build
Models, code, and the record of how they were made. A client who cannot run and change their own system without us cannot show they are in control of it, and we will not be a dependency inside a regulated decision.
Few engagements, long relationships
A small team is a deliberate constraint. It means the people who scoped the work are the people who do it, and it means we take on what we can do properly rather than what we could bill.
Get in touch
Start with a short conversation
Virticus works with regulated firms that need a fraud rule, a credit or marketing model, or an automated decision to do its job and stand up when questioned, and with legal teams that need one examined. A short call is usually enough to say what is needed.