Wider experience
The same discipline, applied beyond financial crime
Fraud and AML rule defensibility is the flagship. It rests on a longer record of work where automated outputs affected customers, money, or public decisions — and had to be explainable when questioned. These scenarios are illustrative of that background.
Consumer Credit
Lending and Calculation Logic Under Regulatory Challenge
Interest on Buy-Now-Pay-Later balances had been calculated incorrectly over time, affecting large numbers of customers and creating regulatory risk. The lack of clear validation and oversight allowed errors to persist unnoticed, and there was no record of why the logic had been set up as it was. Illustrative work undertakes a full review of calculation logic and introduces controls to test, monitor, and evidence outputs. This supports remediation and helps ensure the logic is accurate, controlled, and able to withstand scrutiny.
Banking
Calculation Remediation Under Regulatory Attention
A credit card interest calculation was producing errors across customer balances, creating financial and regulatory risk at scale. The issue drew regulatory attention and required changes to the underlying approach. Illustrative work covers calculation accuracy, control design, and output validation, including assurance tooling and close work with compliance and legal teams. This helps build a defensible record around the calculation and supports remediation under scrutiny.
Transactions
Data, Assurance and Investor Scrutiny
In a transaction environment, a business was being prepared for sale to overseas investors, which raised the importance of data quality, consistency, and explainability. Information used to support value and performance needed to withstand close challenge from external parties. Illustrative work strengthens confidence in the underlying data and in how outputs are presented and understood. This reduces risk in a commercially sensitive process where claims have to hold up.
Banking
Driver-Based Financial Modelling and Management Decisions
Driver-based financial models supported management’s view of performance, planning, and decision-making. The risk was not only model error, but misplaced confidence in assumptions that could materially affect business choices. Illustrative work supports clearer validation, stronger challenge of outputs, and better alignment between modelling logic and decision use. This improves confidence in how model outputs are interpreted.
Government
COVID-19 Data and Decision Support
During the COVID-19 pandemic, data was used to support urgent public-sector decisions under intense pressure. The environment changed rapidly, while decisions still needed to be based on information that could be trusted and explained. Illustrative work improves the reliability, structure, and usability of data feeding decision processes. This reduces uncertainty in a time-critical setting.
Public Health
Tooling and Data Control in a High-Scrutiny Environment
In a public health setting, tooling was built to support operational and analytical decision-making where poor data handling could have immediate consequences. The challenge was to make systems usable at pace while preserving control, consistency, and accountability. Illustrative work helps set up tooling and improves confidence in how outputs are generated and used. This supports better visibility and stronger decision discipline under scrutiny.
AI Governance
Governing AI in Teaching, Feedback and Marking
AI tools were used to help prepare lessons, generate student feedback, and support marking activity. While this accelerated the work, outputs were not always consistent and could not be accepted without review. The same defensibility discipline behind the fraud-rule work was applied to govern how AI-generated material was used before reaching students. This improved consistency and reduced the risk of poor or unfair educational outcomes.
AI Governance
Governing AI in Bookkeeping and Submission Preparation
AI was used to automate bookkeeping tasks and support the preparation of financial and tax submissions. Small classification errors or unsupported assumptions could flow through into final outputs and create compliance problems. Review and validation controls were added before submission stages, applying the same defensibility discipline used for fraud rules. This improved reliability and reduced the risk of avoidable reporting errors.
AI Governance
Governing AI-Driven Learning Recommendations
AI was used to personalise learning pathways and generate recommendations for students. The main challenge was ensuring that outputs were suitable, explainable, and not misleading when used in real educational settings. Control was introduced around how recommendations were reviewed and applied. This helped keep learning decisions appropriate, consistent, and defensible.
For the flagship work this business is built on, see the fraud and AML cases
Discuss a related situation
If your fraud and AML rules would face the same scrutiny, it is worth knowing where they stand
A short discussion is usually enough to determine whether the issue is one of rule design, control, evidence, or a combination of the three.
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