AI consulting for finance | UK financial services firms
by Dr. Phil Winder , CEO
AI consulting for finance helps UK lenders, insurers and fintechs find the decisions they make at volume where a model would decide better, such as who to lend to, which payment to stop or what to charge, then build those models and run them under Financial Conduct Authority (FCA) and Prudential Regulation Authority (PRA) rules. Winder.AI is an engineering-led AI consultancy, independent since 2013. In 2023 we took a reinforcement learning system for a UK financial institution from proof of concept onto live customer traffic, and audited the machine learning operations of a UK car-finance lender.
Where AI changes a finance decision
Each decision below is one a firm makes thousands of times, and each has a failure that shows up before the model’s accuracy does.
| Decision | What we build | What goes wrong first |
|---|---|---|
| Lending and credit | Credit risk and affordability models, with their monitoring | The training data holds outcomes only for applicants the old process approved, so the model has to be checked on the applicants it would now accept |
| Fraud and financial crime | Transaction scoring and anomaly detection | Confirmed fraud labels arrive weeks later, when chargebacks and investigations close, so monitoring has to work before the labels do |
| Pricing | Pricing and propensity models for loans and insurance | Consumer Duty asks whether each product gives fair value, so every price segment needs a reason the firm can show |
| Collections and customer operations | Reinforcement learning (RL) and decision models for sequences of contacts | Each contact’s result shows weeks or months later, which suits RL, because a model trained one decision at a time optimises only the next step |
| Forecasting for finance teams | Cash, demand and loss forecasts | A model trained before a change in interest rates keeps forecasting the old conditions until it is retrained |
The UK rules a finance model has to satisfy
The PRA’s supervisory statement on model risk management, SS1/23, was published on 17 May 2023 and took effect on 17 May 2024. It applies to UK-incorporated banks, building societies and PRA-designated investment firms with internal model approval for regulatory capital, and it covers any model that informs a business decision, machine learning included. Its five principles run from model identification and risk classification through governance, development and use, and independent validation to risk mitigants. We write each model’s documentation and test evidence for the firm’s validators while we build it.
The FCA’s Consumer Duty came into force on 31 July 2023 for open products and 31 July 2024 for closed ones. Its rules in PRIN 2A.9 require firms to monitor the outcomes retail customers receive, and to check whether any group of customers gets different outcomes from another. A credit, pricing or collections model therefore needs outcome monitoring by customer group from the day it goes live, and we build that monitoring with the model.
Steps in a finance engagement
The work follows one decision from its data to a model in production:
- Decision review. We pick the decision, list the data behind it, and agree what the model will be judged on, including the measures risk and compliance will ask for.
- Prototype on historical data. We build against past decisions and their outcomes, and write the validation evidence as we go.
- Production on live traffic. The model goes live behind controls the firm agrees, with monitoring for drift and for outcomes by customer group.
- Handover or retainer. Your team runs it, or we stay on to retrain and extend it.
A firm that already runs models can start smaller, with an audit of how those models are built, deployed and checked, which ends in a prioritised plan.
UK financial services case studies
For a UK financial institution, we helped the data science team take a reinforcement learning proof of concept into production. The aim was to automate part of the customer lifecycle that had needed the attention of tens of people. We designed the simulated environment the agent trained in, the reward functions and the training approach, and the system went live on customer traffic for evaluation in 2023.
For a UK car-finance lender, we audited the processes around its machine learning development and deployment in 2023. We interviewed its developers, stakeholders, risk assessors and executives, scored their practice against a framework built for the lender, and wrote a report on the current state, the risks and opportunities, and a prioritised plan.
Tractable, a UK insurance technology company working in motor insurance, brought us in for remote MLOps coaching, which we published as a case study in 2023. The sessions picked out feature stores and model registries as the changes with the highest return for its machine learning work.
Cost
Our pricing page publishes indicative rates of £150 to £300 an hour on time and materials, drawn from real projects, and we quote each engagement on its scope. The same page lists an MLOps analysis-and-design engagement that took about two months on a fixed cost.
Who this is for
The work suits UK banks, building societies, lenders, insurers, payments firms and fintechs with a decision they make thousands of times, and finance teams that need better forecasts. If a vendor’s credit, fraud or document product already fits the problem, we will say so on the first call.
Other finance work
For agents that act in research, KYC, operations and reconciliation workflows, see custom AI agents for finance. For loan packs, KYC files, statements and regulatory filings, see document automation for financial services. London firms can start from AI consultancy in London.
Frequently asked questions
AI consulting for finance finds the decisions a bank, lender, insurer or fintech makes at volume where a model would decide better, such as credit, fraud, pricing, collections and forecasting, then builds those models and runs them under UK rules from the Financial Conduct Authority (FCA) and the Prudential Regulation Authority (PRA). Winder.AI, an engineering-led AI consultancy independent since 2013, does the review, the build and the monitoring with the same senior engineers.
Winder.AI’s pricing page publishes indicative rates of £150 to £300 an hour on time and materials, drawn from real projects, and we quote each engagement on its scope. The same page lists an MLOps analysis-and-design engagement that took about two months on a fixed cost. The number of decisions and data sources in scope sets most of the cost.
SS1/23, the PRA’s supervisory statement on model risk management, took effect on 17 May 2024 for UK-incorporated banks, building societies and PRA-designated investment firms with internal model approval for regulatory capital, and it covers machine learning models. Its five principles cover model identification and risk classification, governance, development and use, independent validation, and risk mitigants. We write each model’s documentation and test evidence for the firm’s independent validators while we build it.
The Consumer Duty applies to products sold to retail customers, from 31 July 2023 for open products and 31 July 2024 for closed ones. Its rules require firms to monitor the outcomes those customers receive and whether any group of customers gets different outcomes from another. A credit, pricing or collections model therefore needs outcome monitoring by customer group from the day it goes live, and we build that monitoring with the model.
In 2023 we helped a UK financial institution take a reinforcement learning system from proof of concept onto live customer traffic, and ran an MLOps audit of how a UK car-finance lender built and deployed its models. For Tractable, a UK insurance technology company, we ran MLOps coaching, published as a case study in 2023, that picked out feature stores and model registries as its highest-return changes.
A small team of senior engineers, working remotely from Europe and the USA and led by Phil Winder, author of O’Reilly’s Reinforcement Learning, does the review, the build and the monitoring. There is no sales layer between you and them.
Yes. Cash, demand and loss forecasting, and anomaly detection in ledgers and payments, are machine learning problems we build models for in any finance function. Our published finance case studies are with financial services firms: a UK financial institution, a UK car-finance lender and Tractable.
We build both, and each has its own page: custom AI agents for finance covers agents that act in research, KYC, operations and reconciliation workflows, and document automation for financial services covers extraction from loan packs, KYC files, statements and regulatory filings. This page covers the decision models and the regulation around them.