Fraud Detection

National Telecommunications Regulatory Authority (NTRA) has investigated the telecommunication performance in the service providers in Egypt between the periods of February 2018 till February 2019. Fraud is a multi-billions pounds' problem around the world. The problem is the huge financial loss of revenue that can affect the trustworthiness and performance of the telecommunication corporate. Egypt is one of the developing countries that are witnessing a rapid-growth development in its telecommunications infrastructure. As a result of the increment rate of corporate and internet users, fraudsters are exploiting the vulnerabilities to gain illegal revenue through the subscribers. The telecommunications sector in Egypt has suffered in the last two decades from different types of telecom fraud cases which have damaged the financial sector in both telecommunication side and governmental side. In this paper, we present: a) Statistical insights on users, mobile operators and Internet companies for the telecommunications sector in Egypt in 2018/2019. b) An illustration for the common fraudulent methodologies that are used against both users and telecom operators in Egypt. c) A discussion on the consequences of fraud cases with a suggested framework as a proactive methodology to prevent the escalated threat of telecom fraud.

Association rules are considered to be the best studied models for data mining. In this article, we propose their use in order to extract knowledge so that normal behavior patterns may be obtained in unlawful transactions from transactional credit card databases in order to detect and prevent fraud. The proposed methodology has been applied on data about credit card fraud in some of the most important retail companies in Chile.

—Despite all attempts to prevent fraud, it continues to be a major threat to industry and government. In this paper, we present a fraud detection method which detects irregular frequency of transaction usage in an Enterprise Resource Planning (ERP) system. We discuss the design, development and empirical evaluation of outlier detection and distance measuring techniques to detect frequency-based anomalies within an individual user's profile, relative to other similar users. Primarily, we propose three automated techniques: a univariate method, called Boxplot which is based on the sample's median; and two multivariate methods which use Euclidean distance, for detecting transaction frequency anomalies within each transaction profile. The two multivariate approaches detect potentially fraudulent activities by identifying: (1) users where the Euclidean distance between their transaction-type set is above a certain threshold and (2) users/data points that lie far apart from other users/clusters or represent a small cluster size, using k-means clustering. The proposed methodology allows an auditor to investigate the transaction frequency anomalies and adjust the different parameters, such as the outlier threshold and the Euclidean distance threshold values to tune the number of alerts. The novelty of the proposed technique lies in its ability to automatically trigger alerts from transaction profiles, based on transaction usage performed over a period of time. Experiments were conducted using a real dataset obtained from the production client of a large organization using SAP R/3 (presently the most predominant ERP system), to run its business. The results of this empirical research demonstrate the effectiveness of the proposed approach.

We present an unsupervised learning method for classifying consumer insurance claims according to their suspiciousness of fraud versus nonfraud. The predictor variables contained within a claim file that are used in this analysis can be binary, ordinal categorical, or continuous variates. They are constructed such that the ordinal position of the response to the predictor variable bears a monotonic relationship with the fraud suspicion of the claim. Thus, although no individual variable is of itself assumed to be determinative of fraud, each of the individual variables gives a "hint" or indication as to the suspiciousness of fraud for the overall claim file. The presented method statistically concatenates the totality of these "hints" to make an overall assessment of the ranking of fraud risk for the claim files without using any a priori fraud-classified or labeled subset of data.

We first present a scoring method for the predictor variables that puts all the variables (whether binary "red flag indicators," ordinal categorical variables with different categories of possible response values, or continuous variables) onto a common 1 to 1 scale for comparison and further use. This allows us to aggregate variables with disparate numbers of potential values. We next show how to concatenate the individual variables and obtain a measure of variable worth for fraud detection, and then how to obtain an overall holistic claim file suspicion value capable of being used to rank the claim files for determining which claims to pay and the order in which to investigate claims further for fraud. The proposed method provides three useful outputs not usually available with other unsupervised methods: (1) an ordinal measure of overall claim file fraud suspicion level, (2) a measure of the importance of each individual predictor variable in determining the overall suspicion levels of claims, and (3) a classification function capable of being applied to existing claims as well as new incoming claims. The overall claim file score is also available to be correlated with exogenous variables such as claimant demographics or high volume physician or lawyer involvement. We illustrate that the incorporation of continuous variables in their continuous form helps classification and that the method has internal and external validity via empirical analysis of real data sets. A detailed application to automobile bodily injury fraud detection is presented.

Credit card fraud is a serious and growing problem. While predictive models for credit card fraud detection are in active use in practice, reported studies on the use of data mining approaches for credit card fraud detection are relatively few, possibly due to the lack of available data for research. This paper evaluates two advanced data mining approaches, support vector machines and random forests, together with the well-known logistic regression, as part of an attempt to better detect (and thus control and prosecute) credit card fraud. The study is based on real-life data of transactions from an international credit card operation.

Fake checks are one of the most common instruments used to commit fraud against consumers. This fraud is particularly costly for victims, since they generally loose thousands of dollars as well as being exposed to judicial proceedings. Currently, there is no existing solution to authenticate checks and detect fake ones instantly. Instead, banks must wait for a period of more than 48 hours to detect the scam. In this context, we propose a block chain based scheme to authenticate checks. More precisely, our approach helps the banks to share information about provided checks without exposing the banks' customers' personal data. A proof of concept of our scheme was realized using Python language and relying on Name coin block chain.

Credit card fraud detection is presently the most frequently occurring problem in the present world. This is due to the
rise in both online transactions and e-commerce platforms. Credit card fraud generally happens when the card was stolen for
any of the unauthorized purposes or even when the fraudster uses the credit card information for his use. In the present world,
we are facing a lot of credit card problems. To detect the fraudulent activities the credit card fraud detection system was
introduced.

Fraud detection in energy systems by illegal consumers is the most actively pursued study in non-technical losses by electric power companies. Commonly used supervised pattern recognition techniques, such as artificial neural networks and support vector machines have been applied for automatic commercial frauds identification, however they suffer from slow convergence and high computational burden. We introduced here the optimum-path forest classifier for a fast non-technical losses recognition, which has been ...

A true story of a Hungarian fighter pilot of the Second World War, who went missing at the last days of the war (apparently killed, never returned) and a fraud, who started to use his identity after 1990. It was published in the 2012 issue of the Magyar Repüléstörténeti Évkönyv / Hungarian Aviation Historical Yearbook, pp. 73-78.

Character traits are overlooked when assessing an IG’s success. IGs benefit when they focus on developing interpersonal skills, and building relationships with stakeholders. By understanding these traits, and the traits of successful IGs, future IGs will be positioned on how to best address the challenges they are likely to face

This article presents a forensic accounting theory. Forensic accounting theory is an explanation of why and how the choice of methods and techniques used to detect creative accounting or fraudulent manipulations in financial reporting, and the outcome of using such methods or techniques, depends on the accounting and non-accounting decisions taken into consideration by the forensic accountant or investigator. The forensic accounting theory developed in this paper is useful to both practitioners and academics, and the resulting contribution to accounting theory and forensic science are useful to the problem-solving process in the global fight against financial crime.

Telecommunication companies today are operating in highly competitive and challenging environment. Vast volume of data is generated from various operational systems and these are used for solving many business problems that required urgent handling. These data include call detail data, customer data and network data. Data Mining methods and business intelligence technology are widely used for handling the business problems in this industry. The goal of this paper is to provide a broad review of data mining concepts.

In this era of big data, classifying imbalanced real-life data in supervised learning is a challenging research issue. Standard data sampling methods: under-sampling and over-sampling have several limitations for dealing with big data. Mostly, under-sampling approach removes data points from majority class instances and over-sampling approach engenders artificial minority class instances to make the data balanced. However, we may lose informative information instances using under-sampling approach, and under other conditions over-sampling approach causes overfitting problem. In this research work, we have presented a new cluster-based under-sampling approach by amalgamating ensemble learning (e.g. RandomForest classifier) for classification of imbalanced data that we implemented in Julia. We have collected actual illegal money transaction telecom fraud data, which is highly imbalanced with only 8,213 minority class instances among 63,62,620 instances. The proposed method bifurcates the data into majority class and minority class instances. Then, clusters the majority class instances into several clusters and considers a set of instances from each cluster to create several sub-balanced datasets. Finally, a number of classifiers are generated using these balances datasets and apply majority voting technique for classifying unknown new instances. We have tested the proposed method on a separate test dataset that achieved 95.82% accuracy.

يهدف هذا البحث بصورة أساسية الى إختبار أثر استخدام المراجع الخارجي لأساليب التنقيب في البيانات على فعالية اكتشاف والتقرير عن الغش في القوائم المالية. ولذلك ، فقد تم تقسيم البحث الى خمسة فصول.
تناول الفصل الاول الاطار العام للبحث والذى تضمن مقدمة البحث، اهدافه، اهميته، دوافعه، مشكلته، منهجيته وحدوده بالاضافة الى خطة البحث.
استهدف الفصل الثانى عرض الدراسة النظرية وصياغة فروض البحث.  ولتحقيق ذلك، فقد تضمن الفصل تحليلاً وتقييماً للدراسات السابقة ذات الصلة بطبيعة الغش وأنواعه وأسباب وأساليب ارتكابه في البيئة الإلكترونية. وكذلك تحديد مسئولية المراجع الخارجي عن تقييم مخاطر الغش وتخطيط عملية المراجعة في ظل البيئة الإلكتروني. وأخيراً تناول هذا الفصل طبيعة أساليب التنقيب في البيانات ، من حيث مفهومها ومراحل تطبيقها وأساليبها ومزاياها وإرشادات أستخدامها ، وأثر استخدام المراجع الخارجي لتلك الأساليب على فعالية اكتشاف والتقرير عن الغش في القوائم المالية ، وانتهى هذا الفصل باشتقاق فروض البحث.
استهدف الفصل الثالث عرض أدوات ومنهجية البحث المستخدمة فى اختبار فروض البحث. كما عرض الفصل توصيفاً للتصميم التجريبى والمتغير المستقل والمتغيرات التابعة.
استهدف الفصل الرابع عرض نتائج الدراسة التجريبية وتحليل البيانات التى تم الحصول عليها من ردود المشتركين فى الدراسة فى عينة المراجعيين الخارجيين للشركات المصرية المقيدة بالبورصة في حالتي المراجعة اليدوية والمراجعة باستخدام أساليب التنقيب في البيانات. وكذلك تحليل آراء المراجعيين الخارجيين في مكاتب المراجعة الأربعة الكبرى ومكاتب المراجعة الصغرى.
واخيراً، عرض الفصل الخامس خلاصة للبحث ، بالإضافة إلى التوصيات ومجالات البحث المقترحة.

Accounting is more integrated with the technology today compared to the previous years. The increase in a variety of technological developments and commercial transactions has further increased the number and type of errors as well as frauds related to the accounting profession. This causes misleading information for several stakeholders. Stakeholdes sometimes make false decisions based on the financial statements created as a result of false and fraudulent transactions. In order to minimize the errors and frauds concerning the accounting system of enterprises, effective internal controls and auditing systems should be in order. It is evident that, setting up the required internal controls and auditing systems reduce asset losses and provide great benefits in the long run for firms. Existence of strong internal controls and auditing systems in enterprises has gained great importance in Turkey. This study provides information about the audit techniques that can minimize accounting re...

This paper is aimed at empirically evaluating the relationship between forensic accounting and fraud detection and prevention in the Nigerian ministries. The study used a survey design using questionnaire to obtain data from the primary source. Sample size of one fifty (50) respondents consisting of auditors and accountants in ten (10) ministries chosen from FCT Abuja in Nigeria. Analysis of variance, (ANOVA) was adopted to test the hypotheses at 5% significant level. Findings of the study revealed that the use of forensic accounting in the Nigerian ministries is useful in discovering fraud; furthermore, significant correlation between the forensic accounting and the legal action in Nigerian courts. Similarly, use of forensic accounting in the ministries is also valuable in averting fraud. The study therefore, recommends that the ministries should install an uninterrupted enhancement in the internal control system and introduce erudite and competent scrutiny and monitoring, effective accounting system capable of encouraging the officers to carry out their responsibilities with integrity. Forensic accountant should be made to undergo appropriate training on forensic accounting skills. Furthermore, officers in the ministries should adopt truthfulness, accountability, justice, objectivity, as an essential ethical responsibility to diminish the rate of fraudulent practices in Nigeria.

One of the unfortunate results of fraud is major financial commotion that results in atrocious loss for those –inter alia, investors, banks, insurance companies-that dabble in the financial markets (Obiri, 2011; Klimaitiene & Grundiene, 2010). Additionally, occupational fraud 1 tends to have very damaging economic effects especially when the result is bankruptcy (Association of Certified Fraud Examiners, 2010; Klimaitiene & Grundiene, 2010; Obiri, 2011). As such, the ability to detect the likelihood of financial fraud before it occurs or while it is in operation; is of great significance to economic progress (Klimaitiene & Grundiene, 2010; Obiri, 2011). This paper has analyzed the bankruptcy of Enron –the US Energy, Telecommunications, Commodities and Services conglomerate (Kroger, 2004) – through inter alia; its 10K filings with the Securities and Exchange Commission (SEC) and information gleaned from academic journals. The objective was to determine whether Enron's gigantic fraud could have been detected sooner. After employing tools such as Non-Financial Measures; Gross Margin Test; Altman's Z-score Bankruptcy Predictor; Modified Altman's Z-score; Chanos Algorithm; and Beneish Model, this paper concludes that, it would have been possible to detect Enron's fraud, as early as 1998 or at worst 1999.

Are bored workers more likely to commit fraudulent offences in the workplace? In this following article, the author reveals how a lack of stimulation or want of productive engagement amongst an organization’s workforce can significantly increase the risk of financial fraud in the workplace. Given this relationship, the author continues, it would be advantageous if both CEOs and regulators paid greater attention to employees’ job-satisfaction levels.