Johannes Pippidis Lorentzen

We investigate how effectively automatic named entity recognition can recover entities selected by investigators in real criminal cases. Two homicide cases and one synthetic case were processed with five NER models, and extracted entities were matched to investigator labels using a lightweight entity resolution and similarity scoring method. Police trained models aligned best with investigator annotations, while general-purpose models produced larger but less relevant entity sets. Manual validation confirmed that many labels are not text recoverable, establishing a realistic upper bound on NER performance. The results indicate that extraction and investigative relevance are distinct tasks, motivating a two-stage pipeline and future work on relevance modelling and improved resolution.

Case management systems (CMSs) are essential to modern criminal investigations but remain underexplored compared to systems for predictive policing and digital forensics. This study examines how investigators use CMSs, which capabilities remain insufficient, and how these systems can better support their work. Through interviews with practitioners across European jurisdictions, we identify an investigative workflow of six concurrent dimensions and propose a two-tier model distinguishing preparation and exploration for knowledge discoveries. While CMSs effectively manage actions and decision-making, critical gaps persist for knowledge discovery, such as text analysis, hypothesis testing, and information retrieval. We derive functional requirements by workflow dimensions, offering guidance on which capabilities should be embedded in core systems and which are better delivered as modular services. These findings inform the design of CMSs that combine stable infrastructure with flexible tools.

This study investigates the prevalence and patterns of irregular deposit practices and legal breaches concerning tenancy deposits in Norway’s private rental housing sector. Irregularities in handling tenancy deposits can adversely affect tenants, particularly vulnerable groups like students and low-income families. By analysing transaction data from DNB, Norway’s largest bank, covering January 1, 2014, to December 31, 2023, the research reveals that approximately 24.5–42.6 % of rental units experience irregular deposit practices. Many deposits are not stored in legally required accounts, and about 85 % of deposit repayments were round figures, indicating that interest accrued on deposits was often not paid back to tenants. These breaches of the Norwegian Tenancy Act highlight significant concerns within the private renting sector. As the first systematic investigation of irregular deposits in Norway’s rental market, this study provides a foundation for future research and interventions to protect tenant rights and improve compliance with deposit regulations. The findings emphasize the necessity for legislative reforms to strengthen tenant protections and ensure proper deposit-handling .

Purpose – The purpose of this paper is to develop, describe and validate a machine learning model for prioritising which financial transactions should be manually investigated for potential money laundering. The model is applied to a large data set from Norway’s largest bank, DNB. Design/methodology/approach – A supervised machine learning model is trained by using three types of historic data: “normal” legal transactions; those flagged as suspicious by the bank’s internal alert system; and potential money laundering cases reported to the authorities. The model is trained to predict the probability that a new transaction should be reported, using information such as background information about the sender/receiver, their earlier behaviour and their transaction history. Findings – The paper demonstrates that the common approach of not using non-reported alerts (i.e. transactions that are investigated but not reported) in the training of the model can lead to sub-optimal results. The same applies to the use of normal (un-investigated) transactions. Our developed method outperforms the bank’s current approach in terms of a fair measure of performance. Originality/value – This research study is one of very few published anti-money laundering (AML) models for suspicious transactions that have been applied to a realistically sized data set. The paper also presents a new performance measure specifically tailored to compare the proposed method to the bank’s existing AML system.