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Telecommunication (telecom) fraud is one of the most common crimes and causes the greatest financial losses. To effectively eradicate fraud groups, the key fraudsters must be identified and captured. One strategy is to analyze the fraud interaction network using social network analysis. However, the underlying structures of fraud networks are different from those of common social networks, which makes traditional indicators such as centrality not directly applicable. Recently, a new line of research called deep random walk has emerged. These methods utilize random walks to explore local information and then apply deep learning algorithms to learn the representative feature vectors. Although effective for many types of networks, random walk is used for discovering local structural equivalence and does not consider the global properties of nodes.

The authors proposed a new method to combine the merits of deep random walk and social network analysis, which is called centrality-guided deep random walk. By using the centrality of nodes as edge weights, the authors’ biased random walks implicitly consider the global importance of nodes and can thus find key fraudster roles more accurately. To evaluate the authors’ algorithm, a real telecom fraud data set with around 562 fraudsters was built, which is the largest telecom fraud network to date.

The authors’ proposed method achieved better results than traditional centrality indices and various deep random walk algorithms and successfully identified key roles in a fraud network.

The study used co-offending and flight record to construct a criminal network, more interpersonal relationships of fraudsters, such as friendships and relatives, can be included in the future.

This paper proposed a novel algorithm, centrality-guided deep random walk, and applied it to a new telecom fraud data set. Experimental results show that the authors’ method can successfully identify the key roles in a fraud group and outperform other baseline methods. To the best of the authors’ knowledge, it is the largest analysis of telecom fraud network to date.

The issue of concurrent jurisdiction over cross-border crimes has become common in a globalizing world, while the rigid compliance with territoriality and active personality jurisdiction has created a legal vacuum for cross-border crimes in many situations. The jurisdiction dispute between mainland China and Taiwan over cross-border telecom fraud crimes is a good example. In recent years, the Ministry of Public Security of the People’s Republic of China cracked down a series of cross-border telecom fraud crimes against mainland residents and extradited suspects to mainland China. Given a certain proportion of Taiwan residents in criminal gangs, the Taiwan side raised jurisdiction objections, arguing that mainland China had no right to exercise jurisdiction over Taiwanese criminals. The essence of the jurisdiction dispute between two sides is the concurrence of Taiwan’s right to exercise active personality jurisdiction and the mainland’s right to exercise passive personality jurisdiction. The purpose of this paper is to analyze the connotation of different jurisdiction principles (namely, territorial, active personality, protective and passive personality jurisdiction) and reinterpret their prioritization of applicability from a jurisprudential perspective, and thus, enhance the theoretical basis for resolving the issue of concurrent jurisdiction over cross-border crimes.

By reviewing the historical trajectory of major jurisdiction principles since the 1920s, and studying the specificities of the case in this context, this paper argues that territorial jurisdiction and active personality jurisdiction have presumed priority but not an absolute priority for resolving the issue of concurrent jurisdiction. The applicability of protective and passive personality jurisdiction could precede the former provided the jurisdictions of territoriality or active personality are inadequate, incompetent or lack of motivation to combat crimes, which harm other jurisdictions.

The developmental trajectory and contemporary connotation of major jurisdiction principles suggests that the legitimacy of the mainland’s exercise of passive personality jurisdiction over Taiwan criminal suspects lies in the urgent need to recover mainland victims’ significant property loss, the incompetence of Taiwan in detecting and prosecuting telecom fraud crimes committed by Taiwanese residents and targeting mainland victims and that the mainland has guaranteed the Taiwan side’s right to be timely informed and fully participate in its exercise of criminal jurisdiction over crimes involving Taiwan suspects.

Current literature on jurisdiction doctrines mainly uses a historical or descriptive approach to reveal the attitudes of different countries toward jurisdiction principles, which helps little in resolving the issue of concurrent jurisdiction over cross-border crimes in an era of globalization. This paper uses an interpretative approach, reinterprets the contemporary connotation of different jurisdiction principles and redefines the criteria for determining their prioritization in the context of the specificities of a case. It is expected to update the academic literature for resolving concurrent jurisdiction, fill the legal vacuum for combating cross-border crimes created by rigid compliance with territorial jurisdiction, and meanwhile relieve concerns about abuse of extraterritorial jurisdiction as it provides concrete standards for weighting the applicability of jurisdiction principles.

The purpose of this paper is to make explicit why security needs to be viewed as a core activity and why senior management need to view security from a holistic perspective. Reference is made to various activities carried out by computer hackers and the costs associated with computer related crime.

A literature review was undertaken and a conceptual security model was produced. The key elements of the activities associated with security were highlighted and the links between the activities were made clear.

Organized criminal syndicates and international terrorist groups are increasing their level of activity. Senior managers within companies need to put in place an intelligence and security strategy to counter the activities of criminals and terrorists. Furthermore, senior managers will in the future have to work more closely with law enforcement representatives and industry representatives. They will also have to develop an appreciation of the strategic intelligence objectives of various governments. There is also evidence that senior management need to pay greater attention to identifying future threats associated with advances in internet technology.

More attention will need to be given to how facilitating technology such as the internet is providing computer hackers and criminals with ways to either disrupt business activities or extend the range of criminal activities that they are engaged in.

Senior management will need to refocus on the capability of staff vis‐à‐vis corporate intelligence and security work. The learning organization concept can be embraced and can be used to assist staff to identify the advantages associated with effective knowledge management. Scenario analysis and simulation exercises can be used to train staff in emergency work, and disaster management and prevention.

A diverse range of topics is covered and integrated into a security‐oriented context. Attention is focused on the link between organized criminal syndicates and international terrorist groups, and why senior managers in companies need to be engaged in disaster management recovery planning. The material highlights why senior managers in companies need to develop business contingency plans and embrace the counterintelligence concept.

A good decision support system for credit scoring enables telecom operators to measure the subscribers' creditworthiness in a fine-grained manner. This paper aims to propose a robust credit scoring system by leveraging latent information embedded in the telecom subscriber relation network based on multi-source data sources, including telecom inner data, online app usage, and offline consumption footprint.

Rooting from network science, the relation network model and singular value decomposition are integrated to infer different subscriber subgroups. Employing the results of network inference, the paper proposed a network-aware credit scoring system to predict the continuous credit scores by implementing several state-of-art techniques, i.e. multivariate linear regression, random forest regression, support vector regression, multilayer perceptron, and a deep learning algorithm. The authors use a data set consisting of 926 users of a Chinese major telecom operator within one month of 2018 to verify the proposed approach.

The distribution of telecom subscriber relation network follows a power-law function instead of the Gaussian function previously thought. This network-aware inference divides the subscriber population into a connected subgroup and a discrete subgroup. Besides, the findings demonstrate that the network-aware decision support system achieves better and more accurate prediction performance. In particular, the results show that our approach considering stochastic equivalence reveals that the forecasting error of the connected-subgroup model is significantly reduced by 7.89–25.64% as compared to the benchmark. Deep learning performs the best which might indicate that a non-linear relationship exists between telecom subscribers' credit scores and their multi-channel behaviours.

This paper contributes to the existing literature on business intelligence analytics and continuous credit scoring by incorporating latent information of the relation network and external information from multi-source data (e.g. online app usage and offline consumption footprint). Also, the authors have proposed a power-law distribution-based network-aware decision support system to reinforce the prediction performance of individual telecom subscribers' credit scoring for the telecom marketing domain.

Sampling taxpayers for audits has always been a major concern for policymakers of tax administration. The purpose of this study is to propose a systematic method to select a small number of taxpayers with a high probability of tax fraud.

An efficient sampling method for taxpayers for an audit is investigated in the context of a property acquisition tax. An autoencoder, a popular unsupervised learning algorithm, is applied to 2,228 tax returns, and reconstruction errors are calculated to determine the probability of tax deficiencies for each return. The reasonableness of the estimated reconstruction errors is verified using the Apriori algorithm, a well-known marketing tool for identifying patterns in purchased item sets.

The sorted reconstruction scores are reasonably consistent with actual fraudulent/non-fraudulent cases, indicating that the reconstruction errors can be utilized to select suspected taxpayers for an audit in a cost-effective manner.

The proposed deep learning-based approach is expected to be applied in a real-world tax administration, promoting voluntary compliance of taxpayers, and reinforcing the self-assessing acquisition tax system.

This paper uses data from 2010 to 2016 across 32 states and union territories to investigate the determinants of crime in India. Results indicate the significance of macroeconomic, demographic, socioeconomic and deterrence factors in accounting for various categories of crime.

Due to the evidence of heteroskedasticity and cross-sectional dependence, linear regression with panel-corrected standard errors is implemented.

It has been found that among the macroeconomic factors, only GSDP per capita was found relevant in explaining total crime rates. However, the unemployment rate and price level are crucial in explaining some categories of crime. The demographic factor, that is, population density, socioeconomic factors, that is, income inequality, poverty rate, literacy rate exhibit important and significant relationship with crime rates in India. Further, out of the four deterrence factors, charge-sheeting rate, conviction rate, pendency in police cases are important in explaining various categories of crime rates in India.

While implications of some socioeconomic variables are consistent with those found in previous studies, literacy rates and deterrence variables were found to have a positive association with crime. In particular, in a developing country such as India, white-collar crimes tend to increase as literacy rates increase. This calls for implementing policies that lead to greater employment opportunities for the educated masses. This paper also sheds light on the relationship between deterrence factors and crime rates in India. Deficiencies in the legal and judicial system have been detrimental to the nation's ability to curb crime rates.

Y2K isn't the only computer problem you should be worrying about. According to a 1999 survey by the Computer Security Institute and the Federal Bureau of Investigation, computer system penetration by unauthorized users has increased for the third straight year.

This case is about “Red”, a cross-border e-commerce platform developed from a community which was built to share overseas shopping experience. With sharp insights into the consumption behavior of urban white-collar women and riding on its community e-commerce advantage, “Red”, a cross-border e-commerce startup, pulled in three rounds of financing within just 16 months regardless of increasingly competitive market. On the other hand, well-established platforms such as T-mall International and Joybuy also stepped in, and their involvement will also speed up the industry integration and usher in a reshuffling period. Confronted with the “price war” started by those e-commerce giants, in what ways can “Red” adjust its shopping experience and after-sales services to enhance the brand value and sharpen its edge?

This paper aims to elucidate the responsiveness of China’s judicial system in addressing the challenges of identifying online illegal fund-raising crimes that have emerged in recent years. This study systematically evaluates the efficacy and potential pitfalls of legal guidelines contained in judicial interpretations, such as holistic determination, sampling verification and presumption of the nature of funds. In addition, the research endeavors to propose pertinent recommendations for refining the existing judicial rules.

This research mainly uses a doctrinal methodology, focusing on the principal judicial interpretations formulated by the Supreme People’s Court and other central judicial entities in China. The scope encompasses the realm of online illegal fund-raising crimes as well as other cybercrimes. The analytical framework involves a comprehensive examination of these authoritative judicial documents, coupled with a theoretical and critical analysis of relevant academic materials.

This research underscores that while judicial interpretations serve as an effective legal strategy to confront the challenges posed by online illegal fund-raising crimes, their implementation introduces a nuanced landscape. These legal guidelines, often emanating from diverse judicial departments and tackling specific issues, carry the inherent risk of giving rise to new complexities and fostering inconsistency. Judicial authorities shall exercise prudence in both the formulation and application of these guidelines, ensuring their harmonization with existing legal norms and fundamental legal principles.

This research constitutes a critical and comprehensive examination of judicial interpretations in China pertaining to online illegal fund-raising crimes. It offers valuable insights into the country’s judicial interpretation system and its legal responses to financial crimes. The paper serves as a valuable resource for academics, law enforcement professionals, policymakers, legislators and researchers.