Large quantitative models and applied ML
Models that forecast, price, score and detect, trained on your data and explainable to your regulator
Decisions on numbers still rest on spreadsheets and rules of thumb
Pricing, credit, stock and fraud decisions are made many times a day. Static rules and spreadsheet models miss patterns in the data, and they react late when markets move.
Machine learning can do better, but only if a model can be trusted. Regulators, auditors and customers expect to know why a loan was refused or a payment was stopped.
What you get
Decisions from your data
Forecasts, prices and scores that come from your own data and update as it changes.
Risk caught as it happens
Fraud, money laundering and unusual behaviour spotted as transactions happen, not at month end.
Decisions you can explain
Every model decision explained in terms a credit committee, an auditor or a customer can follow.
Scenarios tested first
Financial and supply-chain scenarios tested in simulation before money is committed.
What we deliver
Large quantitative models
Large quantitative models (LQMs) trained on numerical data for financial, numerical and supply-chain simulation, so you can test scenarios before acting on them.
Forecasting, dynamic pricing and demand sensing
Predictive models that forecast demand and revenue, set prices that respond to the market, and sense shifts in demand early.
Credit scoring and underwriting risk with explainable AI
Credit and underwriting models whose every decision comes with the reasons behind it, through explainable AI (XAI).
Classification, personalisation and recommendation
Models that classify cases, personalise offers and recommend the next product, content or action for each customer.
Real-time fraud, AML and anomaly detection
Detection of fraud, anti-money-laundering (AML) patterns and unusual behaviour as transactions happen, with cases routed to your investigators.
AI for R&D and scientific simulation
Models that support research and development, industrial engineering and scientific simulation, where experiments are slow or costly.
How it works
Credit and underwriting models carry explainable AI (XAI), so each decision can be traced to the data that drove it.
Define the decision
We agree the decision the model supports, how success is measured and what a wrong answer costs.
Prepare the data
We assess the data you hold, build the features the model needs and check them for gaps and bias.
Beat the baseline
We compare candidate models against your current method and keep the simplest one that clearly does better.
Explain and validate
We test the model on unseen data, explain its decisions and document it for your risk and audit teams.
Deploy and monitor
We connect the model to your systems and watch for drift, retraining it when its performance falls.
Technologies and standards
Large quantitative models simulate financial, numerical and supply-chain scenarios. Applied machine learning covers forecasting, dynamic pricing, credit scoring, recommendation, and real-time fraud and AML detection.
- AI and machine learning
- PyTorch
- TensorFlow
- scikit-learn
- XGBoost
- MLflow
- Methods and practices
- Large quantitative models (LQMs)
- Explainable AI (XAI)
- Forecasting and demand sensing
- Real-time fraud and AML detection
How to start
Clients usually start with one decision that has a clear cost, such as a credit model or a demand forecast, and extend from there.
What happens next
Agreeing a time
Our team emails you to agree a time for the session.
Pre-consulting session
A free 30-minute online pre-consulting session: we go through your goals, systems and constraints.
Readiness review
In a free 48-hour readiness review, a Principal Architect checks your project against the 6-point Definition of Ready and recommends the track that fits.
Choosing the track
Scope-ready projects receive a binding proposal within 3–5 business days. Concept-stage projects start in the Blueprint Studio. A live system that is failing, undocumented or needs integration starts with a diagnostic of 1–2 weeks. The Blueprint Studio and the diagnostic each have a fixed fee, quoted in the proposal.
AI-native product teams
Managed, cross-functional pods of senior engineers, equipped with agentic coding and QA harnesses.
Questions and answers
What is a large quantitative model?
A large language model is trained on text. A large quantitative model (LQM) is trained on numerical data, such as prices, transactions or supply-chain flows. It is used to simulate scenarios and forecast outcomes where the answer is a number.
How much data do we need?
It depends on the decision and how much the data varies. We assess what you hold at the start and tell you plainly if it is enough. Where it is not, we say what to collect, or whether a simpler method will do.
Can you explain the model’s decisions to auditors and customers?
Yes. We use explainable AI (XAI) methods that show which factors drove each decision, and we document how the model was built and tested. Your credit, risk and audit teams can review every model before it goes live.
How do you check for bias?
We test how the model performs across the customer groups that matter to you and to your regulator. Where results differ without a sound reason, we change the data, the features or the model before release.
Can the model stop fraud as it happens?
Yes. Fraud and AML models score transactions in real time, as they happen. Suspicious cases are held or routed to your investigators, with the reasons the model flagged them.
What happens when the model stops performing?
Markets and customers change, so models drift. We monitor performance and the data the model sees, alert your team when either shifts, and retrain the model through a tested pipeline.
Who owns the models and features?
You do. The code, features, trained models and documentation we produce for you are yours once each milestone is paid.
Start with a conversation, not a contract.
Tell us what you need to build, connect or secure. An architect goes through it with you, then recommends where to start.