Auswahl der wissenschaftlichen Literatur zum Thema „Fradulent“

In contemporary days the more secured data transfer occurs almost through internet. At same duration the risk also augments in secure data transfer. Having the rise and also light progressiveness in e – commerce, the usage of credit card (CC) online transactions has been also dramatically augmenting. The CC (credit card) usage for a safety balance transfer has been a time requirement. Credit-card fraud finding is the most significant thing like fraudsters that are augmenting every day. The intention of this survey has been assaying regarding the issues associated with credit card deception behavior utilizing data-mining methodologies. Data mining has been a clear procedure which takes data like input and also proffers throughput in the models forms or patterns forms. This investigation is very beneficial for any credit card supplier for choosing a suitable solution for their issue and for the researchers for having a comprehensive assessment of the literature in this field.

Lawyers, both practitioners and academics, engage with legal history in a variety of ways. Increasing attention is being paid to legal regulation of history and memory. This article argues that the interaction of law and history is particularly problematic within the context of a dispute with a religious element. It will use three case studies to illustrate these challenges: (1) The repeal of the Fradulent Mediums Act 1951 by the Consumer Protection from Unfair Trading Regulations 2008; (2) The Babri Masjid / Ram Temple dispute in Ayodhya, India; and (3) The Hindmarsh Island bridge controversy in South Australia. These case studies show the difficulties legal actors face when confronted with incompatible secular and sacred histories and diverse ways of ‘knowing history’, but also the importance, nonetheless, of understanding history in order to understand the relationship between law and religion.

Abstract– This research aimed at analyzing the effect of fraud pentagon proxied by nine variables, namely financial stability, personal financial needs, external pressure, financial targets, ineffective monitoring, industry characteristics, auditor changes, director changes, and frequent number of CEO’s picture towards fraudulent financial reporting proxied by Beneish M-Score at non-financial service companies listed on the Indonesia Stock Exchange (IDX) period 2015-2017. The data used were secondary data obtained from annual reports and financial statements of nonfinancial service companies period 2015-2017 with a total of 285 eligible samples. The data analysis methods used were descriptive statistical analysis and logistic regression analysis. The results showed that the variables of financial stability and the nature of the industry generated a significant and positive effect on fraudulent financial reporting. Meanwhile, the variables of personal financial needs, external pressure, financial targets, ineffective monitoring, auditor changes, director changes, and often the CEO's image were not significant to fraudulent financial reporting. Keywords: Fraud Pentagon, Fradulent Financial Reporting, Beneish M-Score

Ekonomisk brottslighet är mer lukrativt jämfört med annan brottslighet som narkotika, häleri och människohandel. Tidiga åtgärder som försvårar att kriminella kan använda företag för brottsliga syften gör att stora kostnader för samhället kan undvikas. En genomgång av litteraturen visade också att det finns stora brister i samarbetet mellan svenska myndigheter för att upptäcka grov ekonomisk brottslighet. Idag uppdagas brotten först ofta efter att en konkurs inletts. I studier har maskininlärningsmodeller prövats för att kunna upptäcka ekonomisk brottslighet och några svenska myndigheter använder maskininlärningsmodeller för att upptäcka brott men mer avancerade metoder används idag av danska myndigheter. Bolagsverket har idag ett omfattande register för bolag i Sverige och denna studie syftar till att undersöka om maskininlärning kan användas för att identifiera misstänkta bolag, genom att använda digitalt inlämnade årsredovisningar och information ur bolagsverkets register för att kunna träna klassificeringsmodeller att identifiera misstänkta bolag. För att träna modellen så har stämningsansökningar inhämtats från Ekobrottsmyndigheten som kunnat kopplas till specifika bolag av de inlämnade årsredovisningar. Principalkomponentanalys används för att visuellt visa på skillnader mellan grupperna misstänkta och icke misstänkta bolag och analyserna visade på ett överlapp mellan grupperna och ingen tydlig klustring av grupperna. Data var obalanserat med 38 misstänkta bolag av totalt 1009 bolag och därför användes översamplingstekniken SMOTE för att skapa mer syntetiskt data och för att öka antalet i gruppen misstänkta. Två maskininlärningsmodeller Random Forest och Stödvektormaskin (SVM) jämfördes i en 10 fold korsvalidering. Där båda uppvisade en recall på runt 0.91 men där Random Forest hade en mycket högre precision och med högre accuracy. Random Forest valdes och tränades på nytt och uppvisades en recall på 0.75 när den testades på osett data bestående av 8 misstänkta av 202 bolag. Ett sänkt tröskelvärde resulterade i en högre recall men med en större antal felklassificerade bolag. Studien visar tydligt problemet med obalans i data och de utmaningar man ställs inför med mindre data. Ett större data hade möjligjort ett strängare urval på brottstyper som hade kunnat ge en mer robust modell som skulle kunna användas av bolagsverket för att lättare kunna identifiera misstänkta bolag i deras register.
Economic crimes are more lucrative compared to other crimes as drugs, selling of stolen gods, trafficing. Early preventions that make it more difficult for criminals to use companies for criminal purposes can reduce large costs for sociaty. A litterature study showed that there are large weaknesses in the collaboration between Swedish authorities to detect serious economic crimes.Today most crimes among companies that commit fraud are found after a company has declared bancruptcy. In studies, machine learning models have been tested to detect economic crimes and some swedish authorites are now using machine learning methods to detect different crimes and more advanced methods are used by the danish authorites. Bolagsverket has a large register of companies in Sweden and the aim of this study is to investigate if machinelearning can be used to detect on annual reports that have been digitaly submited and information in Bolagsverket’s register to be able to train classificationsmodels and identify companies that are suspicious. To be able to train the model lawsuits have been collected from the Swedish Economic Crime Authority that can be connected to specific companies through their digitally submited annual report. Principal component analysis is used to visually show differences between the groups suspect companies and not suspected companies and the analysis show that there is an overlap between the groups and no clear clustering between the groups. Because the dataset was unbalanced with 38 suspicious companies out of 1009 companies the oversampling tecnique SMOTE was used to create more synthethic data and more suspects in the dataset. The two machinelearnings models Random Forest and support vector machine (SVM) was compared in a 10 fold crossvalidation. Both models showed a recall on around 0.91 but Random Forest had a much higher precision with a higher accuracy. Random Forest was chosen and was trained again and showed a recall on 0.75 when it was tested on unseen data with 8 suspects out of 202 companies. Lowering the treshold resulted in a higher recall but with a larger portion of wrongly classfied companies. The study shows clearly the problem with an unbalanced dataset and the challanges with a small dataset. A larger dataset could have made it possible to make a more selective selection of certain crimes that could have resulted in a more robust model that could be used by Bolagsverket to easier identify suspicous companies in their register.

碩士
淡江大學
國際事務與戰略研究所碩士在職專班
102
In Taiwan, economic fraud crimes is a new form of crime , fraud is a crime update from the traditional evolved into a new kind of economic fraud crimes, for normal social and economic order and the legitimate rights and interests of the people there are serious dangers. This new kind of crime has direct relations to the development of economy,because of the characteristics of intellectual ability that fraudulent economic crime reflects,it is the rapid development of economy that provides chances for criminals to commit such kind of crimes . In my opinion, the strategy to prevent fraudulent economic crime should be severely cracked down and the healthy economic order should be established. In consideration of this objective,this dissertation focus on discussing the regularity of the economical defraud,and put forward as countermeasures to punish the crime on prevention strategies.First, this dissertation describes the conception, origin, characteristics and harm- Fullness of the fraudulent economic crime,with a brief comparative re- Search on the crime. Second,this paper defines the legal feature of fraudulent economic crime,studies the constitutional elements of the crime, as the principles to differentiate the crime from what is not a crime,and study on the strategy to prevent fraudulent economic crimes.Third,this paper describes several major types of fraudulent economic crimes,including defraud in bankruptcy, defraud in insurance,defraud in loan and taxes evasion in different chapters. Fourth, in the discussion of criminal punishment principles for the fraudulent economic crime,the presuppositIV ion to punish,the applying of property punishment,life penalty,and security measures are focused. Finally,this dissertation discusses the countermeasures strategy to prevent the fraudulent economic crime in the fields of criminal and social policy,and build a view that it is a system engineering to prevent the fraudulent economic crime. We should promote the public recognition of the harmfulness of crime, strengthen the financial and taxation control ,reinforce the judicial coordination,increase the quantity of criminal punishment, and tackle the crime in a comprehensive way to effectively prevent it. Based on this point,description of economic fraud crime prevention practices and strategies for future development.