Practitioners · regulated finance
We design, build, and review the controls that carry the risk
Five services, one for each team that signs for the risk. We write and review fraud and AML detection rules; we build the credit models a lender’s decisions and provisions rest on; we analyse the evidence in disputes and investigations; we build the customer and marketing models a firm targets with; and we design, build, and deploy the AI and automation the rest run on. Engineering rather than advice: the work is built in your own systems, and your team owns it. So when a regulator or an auditor asks, there is working to show.
Financial Crime
Fraud and AML rules that catch more fraud and stop fewer good customers
Well-tuned rules catch more fraud and turn away fewer genuine customers, and since mandatory APP-fraud reimbursement, FCA-regulated firms must also show those rules are adequate and defensible. The service spans APP scams, AML transaction monitoring, and card, application, and mule fraud, and works both ends: we design and tune the rules, then review them, delivering a rule-by-rule scorecard, coverage gaps mapped across recognised UK typologies, an estimated false-positive reduction where alert figures are shared, and a regulator-ready documentation pack. For independence, a rule we built is reviewed by your second line or a third party, not by us.
Credit Risk
Credit models a supervisor can read
Application and behavioural scorecards, affordability and income resilience, IFRS 9 expected credit loss, and impairment forecasting, for UK banks and lenders. A different signer buys this and a different regime governs it — PRA SS1/23 and CONC 5.2A rather than the PSR. The firm owns the model, the code, and the development record: purpose, the data it was fitted on, assumptions, operating boundaries, limitations, and tier. And as with the rule work, we cannot build a model and be its independent validation.
Legal & Forensic
Specialist technical support for disputes and investigations
For litigation, investigation and in-house legal teams: evidence analysis and disclosure support; fraud, AML and financial-crime expertise; forensic data and financial analysis, including transaction reconstruction and loss modelling; technical examination of AI systems, models and automated decisions; and support with disputed calculations and the other side’s technical evidence. We use AI where it improves the work, but no finding is accepted merely because an AI system produced it. Technical and analytical support, not regulated legal representation.
Marketing Analytics
Marketing spend that can show what it returned
Customer segmentation, value, retention, look-alike, and campaign measurement models, for retailers, consumer brands, and UK financial firms. In a regulated firm a segment decides who is offered credit, an account, or a price, and Consumer Duty asks the firm to evidence that outcome as fair. We build the models so every segment can be re-derived from the data, every input that could stand in for a protected characteristic is tested, and every campaign is judged against a group that did not receive it.
AI & Automation
AI and automation that takes work off your team and holds up at audit
AI and agents increasingly make or shape the decisions a business is accountable for, and often the harder half of the job is the process around them: redesigning a workflow, deciding what stays with a person, and rebuilding the roles that sit either side of it. We do that work with AI in it or without. What we deliver either way is automation built for regulated operations — traceable, human-overridable, and reproducible — so each decision leaves a record built to the standard an auditor, a regulator, or a customer challenge asks for. Start with a fixed-scope Audit-Ready Automation Sprint or an embedded build.
Start with the problem
Start with the team the problem sits with: a fraud rule or a financial crime return, a credit model or its provision, a dispute or an investigation, a segmentation or a campaign, or an automated workflow. We can also scope work across more than one area.
Underneath the five, and applied wider
The controls and returns every service above depends on, and the same method extended to AI governance and operational resilience.
Regulatory Reporting
The returns a firm files with a name against them — IFRS 9 expected credit loss, the annual financial crime return, APP scams performance data — reconciled to source and reproducible months later.
Models & Controls
The controls a firm is judged on whether or not a model sits underneath them — thresholds, limits, reconciliations, and the model and control inventory — scored on six dimensions, from a stated purpose to a test the firm can re-run without us.
Governance
Ownership and oversight for the AI and automated decisions a business relies on: who is accountable, how each decision is reviewed, what evidence is retained, and what can be shown when an outcome is challenged.
Cybersecurity
The controls that move money and keep services running — tested, owned, and mapped to the regulation each implements, so operational resilience can be evidenced on demand rather than asserted after the fact.
Risk
We trace where the controls are exposed — the rules and decisions that could not be defended if challenged today.
Consequence
We show what each gap means in practice: reimbursement cost, complaint risk, regulatory scrutiny, or operational failure.
Solution
We build the evidence, ownership, and documentation, prepared for regulatory, audit or litigation scrutiny.