Abstract
Food fraud and associated criminalities pose significant challenges to market integrity,
public health, and consumer confidence, with annual costs estimated at USD 10–15 billion
globally. Recent literature outlines intricate relationships between criminal activities in
the food industry and financial incentives (Rizzuti, 2022b), situating this sector both as a
source for illicit proceeds and a conduit for money laundering (Milon & Zafarullah, 2023;
Tiwari, 2023, 2024). This paper evaluates how emerging technologies, such as blockchain
(Chuah, 2022) and generative artificial intelligence (GenAI), especially large language
models (LLMs) (Clercq et al., 2024; Ma et al., 2024), could aid in deterring wrongdoing
in the food sector. Utilising a structured literature review methodology, we analysed 31
studies employing Latent Dirichlet Allocation (LDA) for topic modelling combined with
Faff’s (2015) pitching research template for qualitative assessment, supplemented by bibliometric analysis of 517 publications. The quantitative assessment identified five distinct
thematic categories: criminological perspectives, AI and explainable methods, blockchain
and supply chain solutions, analytical detection methods, and biological authentication
with emerging applications. Findings reveal that biological authentication mechanisms
and blockchain technology dominate current research, while criminological perspective
and explainable AI methods remain underrepresented. LLMs emerge as promising frontier
for improving crime detection capabilities through analysing structured and unstructured
data, while requiring stringent oversight owing to potential misuse. These technologies
complement each other: blockchain facilitates supply chain transparency while LLMs
analyse diverse data sources to identify illicit patterns. Despite implementation challenges
including scalability and data quality concerns, this combination presents opportunities
to address food authentication challenges, improve traceability, and detect indicators of
money laundering. However, the analysis reveals a critical disconnect between technological focus and recognition of organized crime exploitation. The present work contributes
systematically by evaluating how this technological combination can disrupt food crime
as a money laundering typology.
public health, and consumer confidence, with annual costs estimated at USD 10–15 billion
globally. Recent literature outlines intricate relationships between criminal activities in
the food industry and financial incentives (Rizzuti, 2022b), situating this sector both as a
source for illicit proceeds and a conduit for money laundering (Milon & Zafarullah, 2023;
Tiwari, 2023, 2024). This paper evaluates how emerging technologies, such as blockchain
(Chuah, 2022) and generative artificial intelligence (GenAI), especially large language
models (LLMs) (Clercq et al., 2024; Ma et al., 2024), could aid in deterring wrongdoing
in the food sector. Utilising a structured literature review methodology, we analysed 31
studies employing Latent Dirichlet Allocation (LDA) for topic modelling combined with
Faff’s (2015) pitching research template for qualitative assessment, supplemented by bibliometric analysis of 517 publications. The quantitative assessment identified five distinct
thematic categories: criminological perspectives, AI and explainable methods, blockchain
and supply chain solutions, analytical detection methods, and biological authentication
with emerging applications. Findings reveal that biological authentication mechanisms
and blockchain technology dominate current research, while criminological perspective
and explainable AI methods remain underrepresented. LLMs emerge as promising frontier
for improving crime detection capabilities through analysing structured and unstructured
data, while requiring stringent oversight owing to potential misuse. These technologies
complement each other: blockchain facilitates supply chain transparency while LLMs
analyse diverse data sources to identify illicit patterns. Despite implementation challenges
including scalability and data quality concerns, this combination presents opportunities
to address food authentication challenges, improve traceability, and detect indicators of
money laundering. However, the analysis reveals a critical disconnect between technological focus and recognition of organized crime exploitation. The present work contributes
systematically by evaluating how this technological combination can disrupt food crime
as a money laundering typology.
| Original language | English |
|---|---|
| Pages (from-to) | 1-32 |
| Number of pages | 32 |
| Journal | European Journal on Criminal Policy and Research |
| DOIs | |
| Publication status | Published - 21 Aug 2025 |
