Application of Language Models to Suspect Prioritisation and Suspect Likelihood in Serial Crimes

Language models are successfully applied to the problem of analysing crime descriptions from a police database with the purpose of prioritising suspects for an unsolved crime, given details of solved crimes. The frequency of terms in each description relates to the behaviour of the offender and this can be used to link crimes to a common offender. Language modelling uses Bayes' theorem and thus require a prior probability. Such a prior can be based on each offender's past propensity to offend, derived from historic data. Language modelling yields a probability of a document being relevant, which in this case is interpreted as the probability of a suspect being the culprit. Although the absolute value of the probability does not carry any direct applied implications, the study does show that the general likelihood of identification of the actual suspect does correspond to the relative values. Thus these probabilities can be used for more than just ranking suspects.

1. Introduction

Central to investigative psychological studies of offending behaviour is the principle that offenders will show some degree of consistency of behavioural style across offences [5]. Police can use this fact to prioritise suspects for an unsolved crime by comparing the behavioural style or Modus Operandi(MO) with that of previously solved crimes. However, drawing such comparisons in systematic and objective fashion, presents a considerable challenge to real world policing. Although typically assumed to be pertinent to serious crime, studies have identified distinct behavioural styles or MOs within all crime types [4]. The police record free text descriptions of offender behaviour for volume crimes such as burglaries, vandalism and street robbery in databases. The present study shows how these data may be exploited to allow automatic judgements of MO similarity.