Fraud Prediction in Smart Supply Chains Using Machine Learning Techniques

Topology-Aware Adaptive Inspection for Fraud in I4.0 Supply Chains

“…Data mining for fraud detection in a variety of contexts has been studied extensively [31] particularly for matters of financial fraud [32]. Semi-supervised machine learning was used to classify transactions as fraudulent or not for smart supply chains [33] and unsupervised machine learning was used to detect anomalous itineraries to predict which shipping containers were likely to be risky enough to inspect [34] while another data-driven approach to detecting smuggling and miscoding in international shipping is used in [35]. Analysing a firm's financial reporting has been shown to predict fraudulent supply chain practices when considering managerial performance and personal information [36].…”

A Systematic Investigation of the Integration of Machine Learning into Supply Chain Risk Management

“…Constante-Nicolalde et al [64] use ML techniques to predict fraud in an intelligent supply chain. They enable an assessment and classification of whether a transaction can be classified as normal or fraudulent to reduce product quality risks.…”

“…These latter publications are therefore not listed in Table 1. [18] Conceptual work production supplier risks direct Benjaoran and Dawood [61] Case Study production production risks indirect Blackburn et al [62] Conceptual work & use case production production risks indirect Bouzembrak and Marvin [63] Conceptual work transport food quality/transport risks indirect Brintrup et al [57] Case study transport supplier risks direct Cavalcante et al [58] Conceptual work transport supplier risks direct Constante-Nicolalde et al [64] Conceptual work supply chain quality risks indirect Fu and Chien [65] Conceptual work production supply risks indirect Hassan [19] Conceptual work supply chain supply risks direct Lau et al [66] Conceptual work procurement information risks indirect Layouni et al [20] Survey production transport risks indirect Pereira et al [67] Conceptual work supply chain sales risks indirect Rodriguez-Aguilar et al [22] Conceptual work supply chain supply risks direct Wichmann et al [59] Conceptual work supply chain supplier risks direct Yong et al [23] Conceptual work supply chain supply risks direct…”

“…The results of the systematic literature analysis clearly show that only a few examples of ML in SCRM have been described in any meaningful detail within the scientific literature to date [18,57,61,62,65]. The existing few examples focus on supply chain areas of production [18,20,61,62,65], transport [56][57][58]63], and the entire supply chain [19,22,23,59,64,67]. This is not surprising for production divisions, where automation and built-in sensor technology for data acquisition has already been implemented for years and is, therefore, more mature than other areas of the supply chain.…”

“…Logistics 2021, 5, x FOR PEER REVIEW 7 of 16 [19,22,23,59,64,67]. This is not surprising for production divisions, where automation and built-in sensor technology for data acquisition has already been implemented for years and is, therefore, more mature than other areas of the supply chain.…”