Jul 2025 — Aug 2026 / Industrial placement
LexisNexis — ML
Understanding fraud through data and representation.
During my industrial placement at LexisNexis Risk Solutions, I completed two fraud-model optimisations and analysed large-scale financial data using Python, SQL and Snowflake. Alongside this applied work, I researched self-supervised fraud representations and gained practical experience in GPU compute and batch training.
01The context
Fraud modelling involves understanding complex financial behaviour, developing useful features and evaluating changes carefully.
02My contribution
Completed two fraud-model optimisations and analysed large-scale financial data. Researched self-supervised representations alongside practical modelling work.
03The approach
- Used Python, SQL and Snowflake to analyse large-scale financial data.
- Worked across feature engineering, model tuning and evaluation for fraud-model optimisation.
- Investigated graph embeddings, contrastive learning and non-Euclidean geometry as approaches to self-supervised fraud representations.
- Gained practical experience in GPU compute and batch training.
04Evidence & limitations
- Two fraud-model optimisations completed during the industrial placement, July 2025 to August 2026.
- Research into self-supervised fraud representations accompanied applied data-science work.
The boundaries of this account
Employer-confidential work: no transaction data, source code or internal evaluation results are published.
Research directions are not claims of deployed models or measured performance uplift.
Descriptions are owner-supplied; no independently verified results are claimed.
05Tools & methods
- Python
- SQL
- Snowflake
- Feature engineering
- Representation learning
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