## 2 Related Work on Port Risk Management  
Strategic collaborations in logistics networks, of which ports are a significant node, reduce risks for all organizations in the network [6]. In ports, high-quality risk management is absolutely necessary for their sustainable development [7]. The present focus of risk management in ports are rather high level, mostly on development, management, organization and commercial issues of ports and terminals. Nevertheless, a few studies have tried to solve risk problems in practical operation of cargo delivery. In terms of the risk analysis process, both qualitative and quantitative techniques can be used. Nowadays a variety of techniques of risk analysis have been developed in industrial settings, normally in response to practical business problems. These techniques, such as physical inspections, flow charts, safety review, checklist analysis, relative ranking, cause-consequence analysis, 'what-if' analysis, failure modes, and so forth, all excessively depend on expert knowledge and manual preprocessing. As a result, in complex operational environments, experts may not be able to be fully aware of all situations, which may have negative influence on the result of risk detection and analysis.  
The widespread use of digital technologies has led to the intelligent port operation paradigm, the amount of data collected during operational processes is growing at a rapid pace. Data-driven risk management is emerging to provide companies with better means to obtain early warning from an increasingly massive amount of data and gain a powerful competitive advantage, especially in the operational context [8; 9]. More specifically, data from surveys, interviews and expert classification plays a significant role in extant research on port risk analysis. For instance, [PERSON]. [10] qualitatively assessed the potential risks for ports using data from interviews with the administrations of a container terminal in an empirical study. Furthermore, [PERSON]. [11] identified 19 port-centric disruptive events based on the literature and used data from surveys to generate risk metrics for analyzing port-centric supply chain disruption threats. In [7], fault tree analysis and event tree analysis were used to assess the risk factors associated with sea ports and offshore terminals operations and management based on experts' judgement.  
Regarding current research on cargo theft, most studies focus on the problem of theft prevention by introducing software systems, hardware facilities or a set of managerial measures. For instance, some algorithms for specific business settings and monitoring methods such as text messaging have been proposed as patents. A cargo theft information processing system is introduced in [12]. Moreover, the development of fast-working, nonintrusive X-ray and detection devices has been suggested to help ensure cargo. In addition to academic papers, there are also some regulations aiming at theft prevention, for instance the Freight Security Requirement and the Trucking Security Requirement issued by Technology Assets Protection Association. However, many fail to realize that the programs designed are not effective enough for preventing theft. To our knowledge, there are no data-driven methods to cope with cargo theft risk analytics in the real-world operational scenario in general and bulk cargo port in particular.  
This paper aims at proposing a data-driven method in order to apply it to a real-world bulk cargo port in the south of China to analyze the relations between risk components of cargo theft and early detection of theft risk. These predictive models are not only useful for understanding the dependency between relevant elements of fraud risk, but also for supporting the strategy optimization of preventing bulk cargo theft.