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arXiv · 2510.03730

Statistical Crime Linkage: Evaluating approaches within the Covenant for Using AI in Policing

Nathan A. Judd·Amy V. Tansell·Benjamin Costello·Liam Leonard·Jessica Woodhams·Rowland G. Seymour
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Abstract

Linking crimes by modus operandi has long been employed as an effective tool for crime investigation. The standard statistical method that underpins statistical crime linkage has been logistic regression. The simplicity and interpretability of this approach has been seen as an advantage for law enforcement agencies using statistical crime linkage. In 2023, the National Police Chiefs' Council published the Covenant for Using Artificial Intelligence in Policing designed to guide the development of novel methods for use within policing. In this article, we investigate more statistical and machine learning methods that could underpin crime linkage models. We investigate a range of methods including regression-, sampling-, and machine learning-based techniques and evaluate them against the principles of Explainability and Transparency from the Covenant. We investigate our methods on a new data set on romance fraud in the UK, where 361 victims of fraud reported the behaviours and characteristics of the suspects involved in their case. We propose a sensitive, Explainable, and Transparent machine learning model for crime linkage and demonstrate how this method could support crime linkage efforts by law enforcement agencies using a dataset of romance fraud with unknown linkage status.

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Nathan A. Judd, Amy V. Tansell, Benjamin Costello, Liam Leonard, Jessica Woodhams, Rowland G. Seymour. 2025-10-04. Statistical Crime Linkage: Evaluating approaches within the Covenant for Using AI in Policing. https://arxiv.org/abs/2510.03730

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