## 1 Introduction  
The operation in port is closely connected with other organizations, and organizational boundaries are frequently crossed, which leads to a complicated operation environment. Cargo theft is referred to as a silent crime, as it accounts for huge losses that frequently go unreported, and can even occur without the owner being aware until weeks or longer after the theft occurred [1]. In terms of bulk cargo, the situation can be worse since their cargo yards are normally huge and exposed without uniform packaging such as the coal yard. Even though more bulk cargo is illegally delivered, port and cargo owners rarely notice the cargo theft immediately. Most cases are detected in cases where there is not enough cargo to pick up for the final delivery. Some other cases are detected by prevention countermeasures of ports such as sampling inspection in terminals, or informer's accusations, such as witness drivers. According to the China Police Daily, Chinese Public Security Traffic Management Authorities has cracked more than 4000 criminal cases of port logistics from 2013 to 2017, saving hundreds of millions Chinese Yuan [2]. This paper aims to detect the fraud risk of bulk cargo theft during truck delivery in port. More specifically, this means that drivers deliver more bulk cargo during transportation than the amount in the bill of lading, e.g., by cheating or other illegal means. Up until now, the most common and effective means against fraud was through sampling inspection during the operation of truck delivery, which is pristine, reactive, and at enormous expense. According to interviews with Guangzhou Port Group and Guangzhou Port Security Bureau, delivery trucks mostly execute cargo theft by reforming or retrofitting trucks to carry heavy filler such as liquid tanks, cement, stones or scrap when entering the terminal and weighing empty trucks. Then the reformed trucks furtively unload these fillers in the terminal and'steal' bulk cargo due to the weight deviation of the empty trucks. Moreover, some common characteristics seem to exist among frauds, such as operational timing, weather, and locale of truck license. However, bulk cargo theft is difficult to detect manually, since (a) port terminals are vast, busy and dynamic; (b) various elements contribute to operational uncertainty, such as cargo type, truck information, and external environment; (c) automatic implements reduce manual intervention, such as the entry allowance using RFID; and (d) different organizations are involved in truck delivery, including the cargo owner, the port company, the delivery company, etc. As IT technologies including IoT are accommodated in port operations, massive data is captured with implications for the external environment and system behavior [3]. The added value can be obtained by applying data analytics coupled to IoT, which is the cornerstone technology for proactively detecting bulk cargo theft. Research has also demonstrated that the amount of data produced and communicated in the logistics and supply chain is significantly increasing, thereby creating challenges for organizations that would like to reap the benefits of analyzing real operational data [4; 5]. Hence, a data-driven approach based on Bayesian network is proposed for predicting fraud risk as well as analyzing risk elements to support better-informed management. The constructed model can be utilized to help cargo interests and security bureau to understand the inter-relations between the risk components of bulk cargo theft in port, further supporting early fraud detection and managerial strategy optimization. A real-world case study for a Chinese bulk port is presented to further evaluate the proposed data-driven method. Thus, the contribution of this paper is fourfold:  
* We propose a novel data-driven methodology for proactively detecting bulk cargo theft in port based on inspection records and operational data.