The purpose of the research is to study the features of the subject’s predicted choice of security practices in connection with extreme life situations that are significant for him. The main hypothesis was the assumption that the choice of security practices can be associated with the type of extreme situation that initiates it, in such a way that each group of these practices corresponds with a certain type of extreme situations. The research was exploratory in nature. The authors used the methods of incomplete sentence and subjective scaling. A total of 200 university students participated in the study on a voluntary basis. The sample included 118 (59 %) girls and 82 (41 %) boys aged 18–21 years. The respondents identified subjectively significant extreme situations, such as illness, pressure on the psyche, domestic extreme, financial fraud, transport accident, trauma at work, criminal aggression, natural disaster, terrorist attack, destructive social conflict. Factorization has established their semantic grouping into general social, public and private extreme situations. The authors based the study on the selection of the following security practices: normative (compliance with safety instructions, safety memos, following the developed safety rules), socio-cultural (taking into account the signs of danger, the use of talismans, protective amulets, conducting a ritual of protection against threats) and individual (compliance with the recommendations of significant persons on safety, the use of their own safety rules, their own safety traditions). The authors established on an empirical basis, that the choice of security practices differs depending on the extreme situation that initiates it. The most preferable for students are individual security practices, the least preferable are socio-cultural ones. The choice of official security practices is more often associated with public extreme situations; the choice of socio-cultural and individual security practices is more often associated with private extreme situations. The problem has prospects for further research.
Although poor deception detection accuracy is thought to be an important risk factor for fraud among older adults, this link has not been explicitly studied. Using a cross-sectional design, older and young adults viewed and made judgments of real, high-stakes truths and lies with financial consequences. Older (vs. young) adults exhibited a greater truth bias when evaluating individuals pleading for help in finding a missing relative, which was associated with greater donations to deceptive pleaders. However, all participants were highly vulnerable to fraud. Future research should consider both risk and protective factors affecting financial fraud across the lifespan.
This research paper takes into consideration the tools that are availed by Modified Altman, Chanos, Beneish, among others, to evaluate Enron Corp yearly 10k financial report as filed with SEC for the years beginning 1997 to 2001 mainly to identify the financial fraud that the company committed and how it could have been prevented before it escalated beyond control. The reported base its pri-mary data source on the SEC Edgar Database, which has information on financial statements for all publicly listed companies. After successfully applying analytical tools such as Altman's Bankruptcy Predictor, Fraud Statement Index, and Analyt-ical tools, this research paper concludes that the company's fraud could have been detected early on between the years 1999- 2000 period (Lucas & Koerwer, 2004).
Keywords: Bankruptcy, Financial ratios, Fraud, Beneish model.
China's securities market has grown dramatically, but the rules that underpin its functioning have failed to keep pace and have been poorly enforced because regulators and courts lacked resources and the issue was never a political priority. Insider trading, stock price manipulation and other fraud is relatively common.
A wide range of sectors and institutions will need to adjust, including investors, listed firms, traders, law enforcers and courts.
Foreign firms investing in China will benefit from better protection from financial fraud.
China may send judges abroad to learn from other systems; Europe is a more likely destination than the United States.
One of the most important Information Security (IS) concerns nowadays is data theft or data leakage. To mitigate this type of risk, organisations use a solid infrastructure and deploy multiple layers of security protection technology and protocols such as firewalls, VPNs and IPsec VPN. However, these technologies do not guarantee data protection, and especially from insiders. Insider threat is a critical risk that can cause harm to the organisation through data theft. The main purpose of this study was to investigate and identify the threats related to data theft caused by insiders in organisations and explore the efforts made by them to control data leakage.
The study proposed a conceptual model to protect organisations’ data by preventing data theft by malicious insiders. The researchers conducted a comprehensive literature review to achieve the objectives of this study. The collection of the data for this study is based on earlier studies conducted by several researchers from January 2011 to December 2020. All the selected literature is from journal articles, conference articles and conference proceedings using various databases.
The study revealed three main findings: first, the main risks inherent in data theft are financial fraud, intellectual property theft, and sabotage of IT infrastructure. Second, there are still some organisations that are not considering data theft by insiders as being a severe risk that should be well controlled. Lastly, the main factors motivating the insiders to perform data leakage activities are financial gain, lack of fairness and justice in the workplace, the psychology or characteristics of the insiders, new technologies, lack of education and awareness and lack of management tools for understanding insider threats.
The study provides a holistic view of data theft by insiders, focusing on the problem from an organisational point of view. Organisations can therefore take into consideration our recommendations to reduce the risks of data leakage by their employees.
<span lang="EN-US">Now days, the analysis of the behavior of cardholders is one of the important fields in electronic payment. This kind of analysis helps to extract behavioral and transaction profile patterns that can help financial systems to better protect their customers. In this paper, we propose an intelligent machine learning (ML) system for rules generation. It is based on a hybrid approach using rough set theory for feature selection, fuzzy logic and association rules for rules generation. A score function is defined and computed for each transaction based on the number of rules, that make this transaction suspicious. This score is kind of risk factor used to measure the level of awareness of the transaction and to improve a card fraud detection system in general. The behavior analysis level is a part of a whole financial fraud detection system where it is combined to intelligent classification to improve the fraud detection. In this work, we also propose an implementation of this system integrating the behavioral layer. The system results obtained are very convincing and the consumed time by our system, per transaction was 6 ms, which prove that our system is able to handle real time process.</span>