Jul 2025 — present / Internal pilot · toward production
LexisNexis — Applied AI
From an idea to an end-to-end demonstration.
I architected a fraud-model optimisation solution designed for organisation-wide use and built an end-to-end working demonstration. I presented its core concept, business value and approach to scaling to senior stakeholders. After my placement, I was retained as an AI engineering contractor to develop the funded internal pilot toward production.
01The context
Fraud-model optimisation combines analysis, modelling and decisions that need accountable human review.
02My contribution
Architected a solution designed for organisation-wide use, built an end-to-end demonstration, and presented its concept, value and scaling approach to senior stakeholders. Retained after placement to develop the funded internal pilot.
03The approach
- Custom Python orchestration and reusable Copilot skills coordinate work with persistent SQLite state.
- Audit trails and mandatory review gates keep human judgement in the workflow.
- DVC and Papermill make notebook-based work recoverable; LightGBM discovers candidate rules, with constrained logistic regression for policy weighting.
04Evidence & limitations
- End-to-end working demo delivered by the end of the placement. Presented the core concept, value and scaling approach to senior stakeholders.
- Retained as an AI engineering contractor in August 2026 to develop a capital-funded internal pilot.
The boundaries of this account
Employer-confidential work: no source code, transaction data or internal evaluation results are published.
This describes a pilot building toward production, not a launched production platform.
Organisation-wide use describes the intended architectural scope, not a claim of organisation-wide deployment or tested production scale.
Descriptions are owner-supplied; no independently verified performance uplift is claimed.
05Tools & methods
- Python
- SQLite
- DVC
- Papermill
- LightGBM
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LexisNexis — ML