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Journal articles on the topic 'Prevention of fraudulent transactions'
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1
Arkusha, Larysa, and Nataliia Chipko. "METHODS OF COMMITTING AND INVESTIGATION OF FRAUDULENT MOTOR VEHICLE TRANSACTIONS." Russian Law Journal 6, no. 4 (November 1, 2018): 126–53. http://dx.doi.org/10.17589/2309-8678-2018-6-4-126-153.
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Having set the goal to investigate and analyze modern methods of committing fraudulent motor vehicle transaction, we systematize and classify them. Special attention is paid to systematizing data on the identity of scammers, organized criminal groups and victims. Methodological recommendations aimed to create computer psychological profiles of scammers involved in fraudulent motor vehicle transactions are offered. Empirical methods of investigation based on available historical information about the peculiarities of the methods of committing crimes in fraudulent motor vehicle transactions are developed herein. Application of such methods will increase the effectiveness of law enforcement institutions in detecting, preventing and investigating fraudulent motor vehicle transactions.
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Zamachsari, Faried, and Niken Puspitasari. "Penerapan Deep Learning dalam Deteksi Penipuan Transaksi Keuangan Secara Elektronik." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 5, no. 2 (April 28, 2021): 203–12. http://dx.doi.org/10.29207/resti.v5i2.2952.
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The rapid development of information technology coupled with an increase in public activity in electronic financial transactions has provided convenience but has been accompanied by the occurrence of fraudulent financial transactions. The purpose of this research is how to find the best model to be implemented in the banking payment system in detecting fraudulent electronic financial transactions so as to prevent losses for customers and banks. Fraud detection uses machine learning with ensemble and deep learning with SMOTE. Financial transaction data is taken from bank payment simulations built with the concept of Multi Agent-Based Simulation (MABS) by banks in Spain. To build the best model, not only pay attention to the accuracy value, but the value of precision is a value that needs attention. A precision score is very important for fraud prevention. Fraud detection gets the best results without the SMOTE process by using deep learning with an accuracy score of 99.602% and precision score of 90.574%. By adding SMOTE, it will increase the accuracy score and precision score with the best model produced in the Extra Trees Classification with an accuracy score of 99.835% and precision score of 99.786%.
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TAGHIYEV, KHAYALADDIN R., TAMERLAN H. RUSTAMOV, and ARAZ A. HASANZADE. "ANALYSIS OF PAYMENT CARDS FRAUD TRANSACTIONS AND MEASURES TO PREVENT THEM." Economic innovations 23, no. 2(79) (June 20, 2021): 172–84. http://dx.doi.org/10.31520/ei.2021.23.2(79).172-184.
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Topicality. The trends of fraud and the history of fraud in general with the emergence of new gaps due to the rapid development of information technology is analysed in the article.The systematization of types and forms of fraudulent intervention, their consequences and ways of counteraction taking into account the interests of users is made.Aim and tasks. The causes of payment card fraud, the main forms and types of possible fraudulent transactions and areas of payment card fraud were further classified and investigated.Research results. The investigation revealed the most common cases, including lost and stolen payment cards, counterfeit cards and fraudulent transactions without a payment card, and identified measures to combat them. At the same time, the ways of using different methods for the implementation of fraudulent transactions were analyzed. In the context of the rapid growth of e-commerce during the global pandemic, widespread fraud and steps to be taken against them have been revealed. Another noteworthy part of the research is the continuation of research in the language of numbers based on statistical data, as well as the collection and accuracy of statistical reporting.Conclusion. Thus, first, the cases of fraud on payment card transactions in the European Union were disclosed, and then the correlation coefficient between payment transactions in the payment card market in the Republic of Azerbaijan and fraudulent transactions was calculated. An investigation was also conducted into the implementation of preventive measures against fraudulent transactions, which are important for financial institutions and payment service users, as well as and the steps to be taken in a consistent manner are explained in the article. The life cycle of payment cards is divided into three main stages, customer acceptance, identification and decision-making, the specific features of each stage and ways to prevent risks in these stages are noted in the article. In the end, the results of the research are reflected in the conclusion section of the article.
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Madkaikar, Kartik, Manthan Nagvekar, Preity Parab, Riya Raika, and Supriya Patil. "Credit Card Fraud Detection System." International Journal of Recent Technology and Engineering (IJRTE) 10, no. 2 (July 30, 2021): 158–62. http://dx.doi.org/10.35940/ijrte.b6258.0710221.
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Credit card fraud is a serious criminal offense. It costs individuals and financial institutions billions of dollars annually. According to the reports of the Federal Trade Commission (FTC), a consumer protection agency, the number of theft reports doubled in the last two years. It makes the detection and prevention of fraudulent activities critically important to financial institutions. Machine learning algorithms provide a proactive mechanism to prevent credit card fraud with acceptable accuracy. In this paper Machine Learning algorithms such as Logistic Regression, Naïve Bayes, Random Forest, K- Nearest Neighbor, Gradient Boosting, Support Vector Machine, and Neural Network algorithms are implemented for detection of fraudulent transactions. A comparative analysis of these algorithms is performed to identify an optimal solution.
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Lv, Fang, Wei Wang, Yuliang Wei, Yunxiao Sun, Junheng Huang, and Bailing Wang. "Detecting Fraudulent Bank Account Based on Convolutional Neural Network with Heterogeneous Data." Mathematical Problems in Engineering 2019 (March 25, 2019): 1–11. http://dx.doi.org/10.1155/2019/3759607.
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Detecting fraudulent accounts by using their transaction networks is helpful for proactively preventing illegal transactions in financial scenarios. In this paper, three convolutional neural network models, i.e., NTD-CNN, TTD-CNN, and HDF-CNN, are created to identify whether a bank account is fraudulent. The three models, same in model structure, are different in types of the input features. Firstly, we embed the bank accounts’ historical trading records into a general directed and weighted transaction network. And then, a DirectedWalk algorithm is proposed for learning an account’s network vector. DirectedWalk learns social representations of a network’s vertices, by modeling a stream of directed and time-related trading paths. The local topological feature, generating by accounts’ network vector, is taken as input of NTD-CNN, and TTD-CNN takes time series transaction feature as input. Finally, the two kinds of heterogeneous data, being integrated into a novel feature matrix, are fed into HDF-CNN for classifying bank accounts. The experimental results, conducted on a real bank transaction dataset, show the advantage of HDF-CNN over the existing methods.
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Sanz-Bas, David, Carlos del Rosal, Sergio Luis Náñez Alonso, and Miguel Ángel Echarte Fernández. "Cryptocurrencies and Fraudulent Transactions: Risks, Practices, and Legislation for Their Prevention in Europe and Spain." Laws 10, no. 3 (July 9, 2021): 57. http://dx.doi.org/10.3390/laws10030057.
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Cryptocurrencies have been developing very rapidly in recent years, and their use is becoming more and more widespread in different areas. The use of digital currencies for legal uses is advancing along with technological development, but, at the same time, criminal activities are also emerging to take advantage of this boom. The aim of this paper has been, first, to analyze the various ways in which individuals and criminal organizations have taken advantage of the phenomenon of cryptocurrencies to carry out fraudulent activities such as laundering money of illicit origin and, second, to provide an overview of the legal tools that have been developed in this regard in Europe and, more specifically, in Spain to combat these activities. Undoubtedly, cryptocurrencies bring great benefits to the economy, but it is also necessary to know the risks and abuses that have been developed to prevent them.
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Sadgali, Imane, Naoual Sael, and Faouzia Benabbou. "Adaptive Model for Credit Card Fraud Detection." International Journal of Interactive Mobile Technologies (iJIM) 14, no. 03 (February 28, 2020): 54. http://dx.doi.org/10.3991/ijim.v14i03.11763.
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<p>While the flow of banking transactions is increasing, the risk of credit card fraud is becoming greater particularly with the technological revolution that we know, fraudulent are improve and always find new methods to deal with the preventive measures that financial systems set up. Several studies have proposed predictive models for credit card fraud detection based on different machine learning techniques. In this paper, we present an adaptive approach to credit card fraud detection that exploits the performance of the techniques that have given high level of accuracy and consider the type of transaction and the client's profile. Our proposition is a multi-level framework, which encompasses the banking security aspect, the customer profile and the profile of the transaction itself.</p>
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Korsik, K. A. "Some Aspects of the Preventive Function of the Notaries." Actual Problems of Russian Law 16, no. 5 (June 9, 2021): 148–54. http://dx.doi.org/10.17803/1994-1471.2021.126.5.148-154.
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The paper examines the notaries as an institution that ensures the legal security of a transaction in the course of its daily work. The author identifies the issues that prevent notaries from preventing fraudulent actions of parties to civil transactions when performing a notarial act. The State Duma of the Federal Assembly of the Russian Federation is considering a draft Federal Law No. 925889-7 "On Amending Certain Legislative Acts of the Russian Federation", specifically about a registrar of persons found incapable by court, the register of notifications of cancellation of powers of attorney made in electronic form. Thus, the author analyzes the issue of creating a register of persons recognized as having no legal capacity or partially incapable. The paper considers the positive experience of the Federal Notary Chamber in maintaining public registers. The author concludes that strengthening of the preventive function of the notaries in the legal system of society depends on the powers granted to this institution.
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Esen, M. Fevzi. "A Robust Multivariate Outlier Detection Method for Detection of Securities Fraud." International Journal of Business Analytics 7, no. 3 (July 2020): 12–29. http://dx.doi.org/10.4018/ijban.2020070102.
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Insider trading is one the most common deceptive trading practice in securities markets. Data mining appears as an effective approach to tackle the problems in fraud detection with high accuracy. In this study, the authors aim to detect outlying insider transactions depending on the variables affecting insider trading profitability. 1,241,603 sales and purchases of insiders, which range from 2010 to 2017, are analyzed by using classical and robust outlier detection methods. They computed robust distance scores based on minimum volume ellipsoid, Stahel-Donoho, and fast minimum covariance determinant estimators. To investigate the outlying observations that are likely to be fraudulent, they employ event study analysis to measure abnormal returns of outlying transactions. The results are compared to the abnormal returns of non-outlying transactions. They find that outlying transactions gain higher abnormal returns than transactions that are not flagged as outliers. Business intelligence and analytics may be a useful strategy for detecting and preventing of financial fraud for companies.
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Nataraj, Geethanjali, and Ashwani. "Banking Sector Regulation in India: Overview, Challenges and Way Forward." Indian Journal of Public Administration 64, no. 3 (July 24, 2018): 473–86. http://dx.doi.org/10.1177/0019556118783065.
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The Indian banking industry is undergoing the rollout of innovative banking models in the form of more promotion to private banks for attaining the productivity and efficiency. However, increase in the quantity of non-performing assets, poor credit growth and low profitability of Indian banks cast doubt about the industry’s resilience towards maintaining the country’s economic growth trajectory. While taking lessons from global regulatory bodies and keeping in view the domestic problem of the Indian banking industry, the dire need of the hour is to maintain proper checks and balances on banking transactions. The article goes on to sum up the various measures initiated by government to deal with banking-sector challenges and how an attempt is made to adapt regulatory measures from global best practices which could help the banking sector in India become more robust, efficient and effective in preventing all fraudulent transactions and enhancing the quality of its assets.
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Naumov, A. I., V. I. Radygin, and M. N. Ivanov. "IDENTIFICATION OF MIXER TRANSACTIONS IN THE BITCOIN NETWORK IN THE FRAMEWORK OF SOLVING THE PROBLEMS OF PREVENTING MONEY LAUNDERING AND TERRORIST FINANCING." SOFT MEASUREMENTS AND COMPUTING 1, no. 2 (2021): 78–90. http://dx.doi.org/10.36871/2618-9976.2021.02.007.
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This article addresses the problem of using cryptocurrencies in crimes related to money laundering and terrorist financing. The development of technologies that allow anonymizing participants of various cryptosystems not only increases their reliability and security for ordinary users, but also exposes such systems to the risk of being used by criminals, and also significantly complicates countering fraudulent or other illegal actions committed using cryptocurrencies. The authors propose algorithms that allow us to classify bitcoin transactions on the subject of whether they use mixers – the main means of hiding traces in the public blockchain and, perhaps, the main participant in most criminal schemes, whether it is money laundering or terrorist financing.
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Rafique, Rushmila Bintay, and A. Vijayalakshmi Venugopal. "PREVENTIVE MEASURES TO MITIGATE THE RISK OF FRAUD IN LETTERS OF CREDIT TRANSACTIONS IN MALAYSIA." UUM Journal of Legal Studies 12, Number 1 (January 31, 2021): 27–49. http://dx.doi.org/10.32890/uumjls2021.12.1.2.
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This article attempts to analyse the issue of fraud in letters of credit (LC) transactions, also known as documentary credits. There are numerous reported cases of fraud in LC transactions, which remain a continuing risk. The UCP 600 is a popular standard of practice for banks, which confirms that banks must honour payment to the seller upon full compliance with the documentary credit requirements. Such payments have been made despite being presented with falsified documents or substandard goods being delivered. It might not be realistic to expect that the International Chamber of Commerce (ICC) can create global standards relating documentary credits, which cover the practicalities of the existing system and relevant legalities applicable to the letter of credit system in international trading. Each party involved may have a responsibility to take some preventive measures to mitigate the risk of fraud. The doctrinal method is used to conduct this study because it involves an in-depth analysis of the gap within the Malaysian system and the strategies that maybe be adopted to overcome the risks associated with LC fraud. Findings reveal that LC documents can be easily falsified, and the occurrence of LC fraud is not uncommon in Malaysia. However, given the lack of literature it has not been highlighted in the past couple of years. The primary focus of this article is to suggest preventive measures that the respective parties could take to protect themselves from fraudulent dealings involving LCs.
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Coita, Ioana –. Florina, Laura –. Camelia Filip, and Eliza-Angelika Kicska. "TAX EVASION AND FINANCIAL FRAUD IN THE CURRENT DIGITAL CONTEXT." Annals of the University of Oradea. Economic Sciences 30, no. 30 (1) (July 2021): 187–94. http://dx.doi.org/10.47535/1991auoes30(1)020.
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Preventing and combating phenomenon of tax evasion is a present concern of national governments due to the magnitude this phenomenon represents and because of the increasingly sophisticated techniques used by the authors in carrying out tax frauds. Evolution of tax evasion phenomenon at international level has acquired a profound technological character due to the increasingly elaborate methods. Illegal behaviour has some specific features that could be recognized easily by artificial intelligence models. They use real data in order to derive characteristics that could be identified in due time so that tax avoidant behaviour be identified and prevented. The use of forecasting models like logistic regression, random forests or decision trees in order to model tax avoidant behaviour shows having a good predictive power. Also, the use of the neural networks allowed scientists to calculate probability of an individual taxpayer that would attempt to evade taxes or commit other types of financial frauds. Scientific literature shows an increasing interest in using neural networks to detect and predict fraudulent behaviour in the fields of tax avoidance and financial domain. Cybercrime, cryptocurrency and blockchain were created in order to facilitate payments and help owner in accumulating wealth. Current landscape of financial frauds shows a different picture. Intracommunity frauds are more and more diversified. European Union and International bodies act together to prevent and combat fraud. Could these new technologies possess a real threat to the financial security of our transactions or encourage fraudulent behaviour? This paper tries to find the answer to this question.
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Matytsin, Denis. "The Neoindustrial Tools for the Turnover of Book-Entry Securities: Digital Technologies for Implementing and Protecting the Rights of Investors and Issuers." Legal Concept, no. 3 (November 2020): 73–83. http://dx.doi.org/10.15688/lc.jvolsu.2020.3.10.
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Introduction: the paper is devoted to the study of economic and legal bases of regulating the turnover of assets in the book-entry securities market, in which transactions of purchase and sale of stocks, bonds, and other instruments are implemented by different subjects of economic relations falling within the jurisdiction of the Russian Federation and foreign states. Methods: the methodological framework for the research is the method of historical materialism, the dialectical method, as well as such general scientific methods of knowledge as analysis, synthesis, hypothesis, analogy, and etc. As specific scientific research methods the comparative legal and functional methods were used. Results: the turnover of assets in the book-entry securities market is studied in the paper as a special channel for financing the economy. The main functions of the market of book-entry securities and their impact on the relationship of all participants of the securities market, among which the main ones are issuers and investors, are defined. The paper shows the vector of transition from a labor-intensive and resource-intensive method of protecting the right - vindication of shares. The categories and roles of investors, their behavior in the securities market, as well as a number of requirements that apply to all investors within the territory of the Russian Federation are compared. The popular mechanisms for protecting the rights of investors are studied. The role and legal possibilities of a vindication claim are analyzed as the main method of protecting the rights. The evolution of the society’s movement to the “Industry 4.0.” format and the application of an innovative method of investment using digital cryptographic records are considered. Conclusions: it is proved that the development of ICO investments is continuing rapidly, and capital investment using this tool is increasing due to attracting a new circle of investors. It is proved that the growing popularity of ICO will lead to the development of the technical “base” of the financial instruments market, strengthening the crypto protection of smart contracts and transactions within their execution, which will eventually make digital cryptographic records used to finance foreign trade transactions habitual investment tools, as well as change the position of individuals and legal entities in the market of bookentry securities, namely, in the process of protecting corporate rights. As a result of the research, it is recommended that the legislation in the field of the stock market provide a preventive method of electronic blockchain registration of jural facts and transactions with book-entry securities, which will avoid fraudulent actions by unscrupulous shareholders, as well as strengthen the rule of law in the execution of public (tax) obligations. The recommendations are made to improve the current legislation; the amendments to Article 149.3. “Violated copyright protection” of the Civil Code of the Russian Federation are proposed; a new version of the Article is given.
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Haoxiang, Wang, and Smys S. "A Survey on Digital Fraud Risk Control Management by Automatic Case Management System." March 2021 3, no. 1 (May 10, 2021): 1–14. http://dx.doi.org/10.36548/jeea.2021.1.001.
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In this digital era, a huge amount of money had been laundered via digital frauds, which mainly occur in the timeframe of electronic payment transaction made by first-time credit/debit card users. Currently, Finance organizations are facing several fraud attempts and it likely happens due to the current infrastructure, which only has an older database.. The current infrastructure diminishes the working environment of any finance organization sector with frequent fraud attempts. In this perspective, the roposed research article provides an overview for the development of an automated prevention system for any finance organization to protect it from any fraudulent attacks. The proposed automated case management system is used to monitor the expenses of the behavior study of users by avoiding the undesirable contact. The proposed research work develops a new management procedure to prevent the occurrence of electronic fraud in any finance organization. The existing procedure can predict digital fraud with an old updated database. This creates disaster and destructive analysis of the finance segment in their procedure. The cyber fraud phenomenon prediction is used to predict the fraud attempt with content-based analysis. The lack of resources is one of the enormous challenges in the digital fraud identification domain. The proposed scheme addresses to integrate all safety techniques to safeguard the stakeholders and finance institutions from cyber-attacks.
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Khlevna, Iuliia L., and Bohdan S. Koval. "DEVELOPMENT OF THE AUTOMATED FRAUD DETECTION SYSTEM CONCEPT IN PAYMENT SYSTEMS." Applied Aspects of Information Technology 4, no. 1 (April 10, 2021): 37–46. http://dx.doi.org/10.15276/aait.01.2021.3.
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The paper presents the demand for the spread of payment systems. This is caused by the development of technology. The open issue of application of payment systems - fraud - is singled out. It is established that there is no effective algorithm that would be the standard for all financial institutions in detecting and preventing fraud. This is due to the fact that approaches to fraud are dynamic and require constant revision of forecasts. Prospects for the development of scientific and practical approaches to prevent fraudulent transactions in payment systems have been identified. It has been researched that machine learning is appropriate in solving the problem of detecting fraud in payment systems. At the same time, the detection of fraud in payment systems is not only to build the algorithmic core, but also to build a reliable automated system, which in real time, under high load, is able to control data flows and effectively operate the algorithmic core of the system. The paper describes the architecture, principles and operation models, the infrastructure of the automated fraud detection mechanism in payment systems. The expediency of using a cloud web service has been determined. The deployment of the model in the form of automated technology based on the Amazon Web Services platform is substantiated. The basis of the automated online fraud detection system is Amazon Fraud Detector and setting up payment fraud detection workflows in payment systems using a customizable Amazon A2I task type to verify and confirm high-risk forecasts. The paper gives an example of creating an anomaly detection system on Amazon DynamoDB streams using Amazon SageMaker, AWS Glue and AWS Lambda. The automated system takes into account the dynamics of the data set, as the AWS Lambda function also works with many other AWS streaming services. There are three main tasks that the software product solves: prevention and detection of fraud in payment systems, rapid fraud detection (counts in minutes), integration of the software product into the business where payment systems and services are used (for example, payment integration services in financial institutions, online stores, logistics companies, insurance policies, trading platforms, etc.). It is determined that the implementation of an automated system should be considered as a project. The principles of project implementation are offered. It is established that for the rational implementation of the project it is necessary to develop a specific methodology for the implementation of the software product for fraud detection in payment systems of business institutions.
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Luehrman, Timothy A., and Lance L. Hirt. "HIGHLY LEVERAGED TRANSACTIONS AND FRAUDULENT CONVEYANCE LAW." Journal of Applied Corporate Finance 6, no. 1 (March 1993): 104–15. http://dx.doi.org/10.1111/j.1745-6622.1993.tb00377.x.
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Jain, Abhisu, Mayank Arora, Anoushka Mehra, and Aviva Munshi. "Anomaly Detection Algorithms in Financial Data." International Journal of Engineering and Advanced Technology 10, no. 5 (June 30, 2021): 76–78. http://dx.doi.org/10.35940/ijeat.e2598.0610521.
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The main aim of this project is to understand and apply the separate approach to classify fraudulent transactions in a database using the Isolation forest algorithm and LOF algorithm instead of the generic Random Forest approach. The model will be able to identify transactions with greater accuracy and we will work towards a more optimal solution by comparing both approaches. The problem of detecting credit card fraud involves modelling past credit card purchases with the perception of those that turned out to be fraud. Then, this model is used to determine whether or not a new transaction is fraudulent. The objective of the project here is to identify 100% of the fraudulent transactions while mitigating the incorrect classifications offraud.
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Bandyopadhyay, Samir Kuma. "Detection of Fraud Transactions Using Recurrent Neural Network during COVID-19." Journal of Advanced Research in Medical Science & Technology 07, no. 03 (October 7, 2020): 16–21. http://dx.doi.org/10.24321/2394.6539.202012.
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Online transactions are becoming more popular in present situation where the globe is facing an unknown disease COVID-19. Now authorities of several countries have requested people to use cashless transaction as far as possible. Practically, it is not always possible to use it in all transactions. Since number of such cashless transactions has been increasing during lockdown period due to COVID-19, fraudulent transactions are also increasing in a rapid way. Fraud can be analysed by viewing a series of customer transactions data that was done in his/ her previous transactions. Normally banks or other transaction authorities warn their customers about the transaction, if they notice any deviation from available patterns; the authorities consider it as a possibly fraudulent transaction. For detection of fraud during COVID-19, banks and credit card companies are applying various methods such as data mining, decision tree, rule based mining, neural network, fuzzy clustering approach and machine learning methods. The approach tries to find out normal usage pattern of customers based on their former activities. The objective of this paper is to propose a method to detect such fraud transactions during such unmanageable situation of the pandemic. Digital payment schemes are often threatened by fraudulent activities. Detecting fraud transactions during money transfer may save customers from financial loss. Mobile-based money transactions are focused in this paper for fraud detection. A Deep Learning (DL) framework is suggested in the paper that monitors and detects fraudulent activities. Implementing and applying Recurrent Neural Network on PaySim generated synthetic financial dataset, deceptive transactions are identified. The proposed method is capable to detect deceptive transactions with an accuracy of 99.87%, F1-Score of 0.99 and MSE of 0.01.
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Priyadarshini, Aishwarya, Sanhita Mishra, Debani Prasad Mishra, Surender Reddy Salkuti, and Ramakanta Mohanty. "Fraudulent credit card transaction detection using soft computing techniques." Indonesian Journal of Electrical Engineering and Computer Science 23, no. 3 (September 1, 2021): 1634. http://dx.doi.org/10.11591/ijeecs.v23.i3.pp1634-1642.
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<p>Nowadays, fraudulent or deceitful activities associated with financial transactions, predominantly using credit cards have been increasing at an alarming rate and are one of the most prevalent activities in finance industries, corporate companies, and other government organizations. It is therefore essential to incorporate a fraud detection system that mainly consists of intelligent fraud detection techniques to keep in view the consumer and clients’ welfare alike. Numerous fraud detection procedures, techniques, and systems in literature have been implemented by employing a myriad of intelligent techniques including algorithms and frameworks to detect fraudulent and deceitful transactions. This paper initially analyses the data through exploratory data analysis and then proposes various classification models that are implemented using intelligent soft computing techniques to predictively classify fraudulent credit card transactions. Classification algorithms such as K-Nearest neighbor (K-NN), decision tree, random forest (RF), and logistic regression (LR) have been implemented to critically evaluate their performances. The proposed model is computationally efficient, light-weight and can be used for credit card fraudulent transaction detection with better accuracy.</p>
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Barker, Rachel. "Knowledge management to prevent fraudulent e-banding transactions." COMMUNITAS 23, no. 1 (December 17, 2018): 71–86. http://dx.doi.org/10.18820/24150525/comm.v23.5.
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Wang, Xiaoguo, Yuanxiu Li, and Ran Zhao. "A Fraudulent Transactions Simulation Method Based on Genetic Algorithm." Journal of Physics: Conference Series 1302 (August 2019): 022090. http://dx.doi.org/10.1088/1742-6596/1302/2/022090.
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Radionova, Marina Vladimirovna, Anton Aleksandrovich Korzukhin, and Nikita Andreevich Saushev. "Mathematical methods of financial transaction evaluation for fraud." Вестник Пермского университета. Серия «Экономика» = Perm University Herald. ECONOMY 16, no. 1 (2021): 54–66. http://dx.doi.org/10.17072/1994-9960-2021-1-54-66.
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An increase in the number of the financial transaction is currently observed, which triggers more financial frauds and more losses from the cyber attacks in the global economy. Detection of the deviant transactions is a burning issue for modern studies because all bank system participants are looking for minimizing the risks which could arise from the vulnerabilities in online transaction. An increase in the financial losses caused by the financial fraud updates the importance of the mathematical methods to analyze the real data. The purpose of the present study is to develop and to define the best mathematical model to predict fraudulent transactions. The novelty of the study lies in designing different binary choice models based on the panel data to predict the deviant transactions, as well as to compare the econometric models with the models based on the neural networks and tree ensembles and in justifying the choice of the best model. Methodologically, the study applies correlational analysis methods, econometric and neural network methods, decision tree ensembles. The most significant results referred to the scientific novelty of the research are as follows: 1) panel data-based financial transactions have been econometrically analyzed within probit- and logit-models with fixed or random effects; 2) neural network methods and tree ensemble-based method have been applied to predict fraudulent transactions; 3) designed mathematical models have been comparatively analyzed, and the model giving the best result in detecting the fraudulent transaction has been defined. Further research is connected with more profound study of the impact of different factors to check the financial transactions for their fraud nature.
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Weirich, Thomas R., Norbert Tschakert, and Stephen Kozlowski. "Teaching Data Analytics Using ACL." Journal of Emerging Technologies in Accounting 14, no. 2 (September 1, 2017): 83–89. http://dx.doi.org/10.2308/jeta-51895.
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ABSTRACT We present a case for teaching data analytics skills in auditing and/or forensic accounting classes using ACL Analytics. We introduce fraudulent transactions into an existing dataset and then ask students to use ACL Analytics to identify these fraudulent transactions. The case can also be used in other accounting classes. We provide ideas and instructions on how professors can tailor the assignments to meet their needs. The case provides students with an opportunity to participate in active learning exercises that require independent study and the use of analytics software. Students were genuinely engaged in the learning process and recommended the case as learning assignments for subsequent classes. Data Availability: Please email the corresponding author.
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Gómez–Restrepo, Jackelyne, and Myladis Cogollo–Flórez. "Detection of Fraudulent Transactions Through a Generalized Mixed Linear Models." Ingeniería y Ciencia 8, no. 16 (November 30, 2012): 221–37. http://dx.doi.org/10.17230/ingciencia.8.16.8.
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The detection of bank frauds is a topic which many financial sector companies have invested time and resources into. However, finding patterns in the methodologies used to commit fraud in banks is a job that primarily involves intimate knowledge of customer behavior, with the idea of isolating those transactions which do not correspond to what the client usually does. Thus, the solutions proposed in literature tend to focus on identifying outliersor groups, but fail to analyse each client or forecast fraud. This paper evaluates the implementation of a generalized linear model to detect fraud. With this model, unlike conventional methods, we consider the heterogeneity of customers. We not only generate a global model, but also a model for each customer which describes the behavior of each one according to their transactional history and previously detected fraudulent transactions. In particular, a mixed logistic model is used to estimate the probability that a transactionis fraudulent, using information that has been taken by the banking systems in different moments of time.
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Can, Baris, Ali Gokhan Yavuz, Elif M. Karsligil, and M. Amac Guvensan. "A Closer Look Into the Characteristics of Fraudulent Card Transactions." IEEE Access 8 (2020): 166095–109. http://dx.doi.org/10.1109/access.2020.3022315.
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Domashova, Jenny, and Olga Zabelina. "Detection of fraudulent transactions using SAS Viya machine learning algorithms." Procedia Computer Science 190 (2021): 204–9. http://dx.doi.org/10.1016/j.procs.2021.06.025.
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Singh, Mandeep, Sunny Kumar, Sunny Kumar, and Tushant Garg. "Credit Card Fraud Detection Using Hidden Markov Model." International Journal of Engineering and Computer Science 8, no. 11 (December 1, 2019): 24878–82. http://dx.doi.org/10.18535/ijecs/v8i11.4386.
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Now a day the usage of credit cards and net banking for online payments has dramatically increased. The most popular mode of online as well as regular purchase payments is through credit card and security of such transactions is also a major issue as frauds are increasing rapidly. In the existing scenario, fraud is detected after the transaction is done and it makes more difficult to find out fraudulent loses barred by issuing authority. In this paper, we observe the behaviour of credit card transactions using a Hidden Markov Model (HMM) and show how it detects frauds. An HMM is initially trained with the normal behaviour of transaction. If the present credit card transaction is not accepted by the trained HMM with enough high probability, then it declares as a fraudulent transaction. At the same time, we try to ensure that no genuine transactions are rejected.
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., Aman. "Credit Card Fraud Detection using Machine Learning and Data Science." International Journal for Research in Applied Science and Engineering Technology 9, no. VII (July 31, 2021): 3788–92. http://dx.doi.org/10.22214/ijraset.2021.37200.
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It is important that companies are able to identify fraudulent credit card transactions so that customers are not charged for items that they did not purchase. These problems can be handled with Data Science and its importance, along with Machine Learning. This project aim is to illustrate the modelling of a data set using machine learning with Credit Card. Our objective is to detect 100% of the fraudulent transactions while minimizing the incorrect fraud classifications. Credit Card Fraud Detection is a sample of classification. In this process, we have focused on analysing and pre-processing data sets as well as the deployment of multiple anomaly detection algorithms such as Local Outlier Factor and Isolation Forest algorithm on the PCA transformed Credit Card Transaction data.
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Marlina, Nurlenni Astuti, Ahmad Rifa’i, and Ni Ketut Surasni. "Persepsi Karyawan Mengenai Pengaruh Efektifitas Pengendalian Internal, Ketaatan Aturan Akuntansi Dan Kesesuaian Kompensansi Terhadap Kecurangan." E-Jurnal Akuntansi 28, no. 2 (August 10, 2019): 957. http://dx.doi.org/10.24843/eja.2019.v28.i02.p07.
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The purpose of the study was to determine employee perceptions regarding the effect of the effectiveness of internal controls, compliance with accounting rules and conformity of compensation to fraudulent tendencies at PT. NTB Syariah Bank in the West Nusa Tenggara region. The dependent variable used in this study is fraudulent tendencies. The independent variable used in this study is the effectiveness of internal controls, compliance with accountingrules and conformity of compensation. This study uses employee respondents who are directly related to banking transactions, especially in the operational part of 50 people randomly selected. Method Analysis of the data used is multiple linear regression. The results of the analysis show the effectiveness of internal controls and suitability of compensation does not affect fraudulent tendencies at PT. NTB Syariah Bank. Whereas compliance with accountingrules has an influence on fraudulent tendencies.Keywords: Fraud, internal control, compliance, compensation.
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Belyakov, S. L., and S. М. Karpov. "IDENTIFY OF FRAUDULENT FINANCIAL OPERATIONS USING THE MACHINE LEARNING ALGORITHM." Vestnik komp'iuternykh i informatsionnykh tekhnologii, no. 188 (2020): 23–31. http://dx.doi.org/10.14489/vkit.2020.02.pp.023-031.
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Current work is devoted to the problem of automatic detection of fraudulent financial transactions. The article describes the causes of fraudulent transactions their typical attributes, as well as the basic principle of detection. The concepts of fraudulent and honest transactions are defined. Examples of algorithms for determining suspicious financial transactions in antifraud systems are given. Modern approaches to monitoring and detecting cases of fraud in remote banking systems are considered. The positive and negative aspects of each approach are described. Particular attention is paid to the problem of optimal recognition of transaction classes in highly unbalanced data. Methods for solving the problem of unbalanced data are considered. The choice of means for evaluating the operation of the machine learning model is justified considering the specifics of data distribution. As a solution, we propose an approach based on the use of ensemble classifiers in conjunction with balanced sampling algorithms, the key feature of which is to create a balanced sample not for the entire classifier, but for each student in the ensemble separately. Based on data on fraud in the field of bank credit cards, a comparison is made and the best classifier is selected among such ensemble algorithms as random forest, adaptive boosting and bagging of decision trees. To create balanced subsets of evaluators of ensemble algorithms, the algorithm of random insufficient sampling is used. To search for the optimal parameters of the classifiers, the random search algorithm on the grid is used. The results of experimental comparison of the selected methods are presented. The advantages of the proposed approach are analyzed, and the boundaries of its applicability are discussed.
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Demchenko, Maksim V. "ON THE ISSUE OF IMPROVING THE REGULATION OF THE REAL ESTATE MARKET IN THE RUSSIAN FEDERATION." Notary 8 (December 17, 2020): 28–32. http://dx.doi.org/10.18572/1813-1204-2020-8-28-32.
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The purpose of writing a scientific work is to study the regulation of the real estate market, the role of the notary in ensuring the protection of the rights of participants in civil turnover and the formation of proposals to expand the powers of the notary to certify real estate transactions. Examining the dynamics of litigation in relation to real estate objects, it is stated that a significant number of them, undoubtedly, arise due to the functioning of various kinds of fraudulent schemes when making the transactions in question. It is noted that in recent years, fraudulent schemes for the unauthorized use of electronic digital signatures for processing transactions have become widespread in the real estate market. Based on the analysis of judicial practice in cases of challenging registered rights to real estate, on invalidating contracts for the sale of residential premises, it was concluded that state registration cannot fully provide for cases of unfair actions in the field of real estate turnover. The best option to ensure the interests of the owners of rights to real estate when concluding transactions with it is notarization of the transaction. For this reason, the paper concludes that it is advisable to diversify the range of transactions, the notarization of which is most appropriate in modern conditions.
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Sanober, Sumaya, Izhar Alam, Sagar Pande, Farrukh Arslan, Kantilal Pitambar Rane, Bhupesh Kumar Singh, Aditya Khamparia, and Mohammad Shabaz. "An Enhanced Secure Deep Learning Algorithm for Fraud Detection in Wireless Communication." Wireless Communications and Mobile Computing 2021 (August 7, 2021): 1–14. http://dx.doi.org/10.1155/2021/6079582.
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In today’s era of technology, especially in the Internet commerce and banking, the transactions done by the Mastercards have been increasing rapidly. The card becomes the highly useable equipment for Internet shopping. Such demanding and inflation rate causes a considerable damage and enhancement in fraud cases also. It is very much necessary to stop the fraud transactions because it impacts on financial conditions over time the anomaly detection is having some important application to detect the fraud detection. A novel framework which integrates Spark with a deep learning approach is proposed in this work. This work also implements different machine learning techniques for detection of fraudulent like random forest, SVM, logistic regression, decision tree, and KNN. Comparative analysis is done by using various parameters. More than 96% accuracy was obtained for both training and testing datasets. The existing system like Cardwatch, web service-based fraud detection, needs labelled data for both genuine and fraudulent transactions. New frauds cannot be found in these existing techniques. The dataset which is used contains transaction made by credit cards in September 2013 by cardholders of Europe. The dataset contains the transactions occurred in 2 days, in which there are 492 fraud transactions out of 284,807 which is 0.172% of all transaction.
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Smith, Quintin-John, and Raul Valverde. "A Perceptron Based Neural Network Data Analytics Architecture for the Detection of Fraud in Credit Card Transactions in Financial Legacy Systems." WSEAS TRANSACTIONS ON SYSTEMS AND CONTROL 16 (July 2, 2021): 358–74. http://dx.doi.org/10.37394/23203.2021.16.31.
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Credit card fraud, a significant and growing problem in commerce that costs the global economy billions of dollars each year, has kept up with technological advancements as criminals devise new and innovative methods to defraud account holders, merchants, and financial institutions. While traditional fraudulent methods involved card cloning, skimming, and counterfeiting during transactional processes, the rapid adoption and evolution of Internet technologies aimed at facilitating trade has given rise to new digitally initiated illegitimate transactions, with online credit card fraud beginning to outpace physical world transactions. According to the literature, the financial industry has used statistical methods and Artificial Intelligence (AI) to keep up with fraudulent card patterns, but there appears to be little effort to provide neural network architectures with proven results that can be adapted to financial legacy systems. The paper examines the feasibility and practicality of implementing a proof-of-concept Perceptron-based Artificial Neural Network (ANN) architecture that can be directly plugged into a legacy paradigm financial system platform that has been trained on specific fraudulent patterns. When using a credit checking subscription service, such a system could act as a backup.
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Oluwafolake, Ayano, and O. Akinola Solomon. "A multi-algorithm data mining classification approach for bank fraudulent transactions." African Journal of Mathematics and Computer Science Research 10, no. 1 (June 30, 2017): 5–13. http://dx.doi.org/10.5897/ajmcsr2017.0686.
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Suyanto, Suyanto. "Fraudulent Financial Statement: Evidence from Statement on Auditing Standard No. 99." Gadjah Mada International Journal of Business 11, no. 1 (January 12, 2009): 117. http://dx.doi.org/10.22146/gamaijb.5539.
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The goals of this study are to empirically identify fraud risk factors and construct a model to predict the likelihood of financial statement frauds based on SAS No. 99. Employing logistic regression on 143 firms, this research finds that fraud risk factor proxies for Pressure—net profit/total assets—and Opportunity— inventory/total assets ratio, related party transactions, and Big 4—are significantly associated with fraudulent financial statements, whereas none of the fraud risk factor proxies for Rationalization is significantly associated with fraudulent financial statements. Consistent with prior research, it seems that the likelihood of fraudulent financial statements is easier to be observed publicly using fraud risk factor proxies for Pressure and Opportunity rather than Rationalization. The constructed model can correctly classify firms with a relatively high success rate.
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Shvets, Bogdan. "Unwirksamkeit von Rechtsgeschäften aufgrund eines Willensmangels – Ein kurzer deskriptiver Überblick über die ukrainische Praxis." osteuropa recht 66, no. 4 (2020): 545–50. http://dx.doi.org/10.5771/0030-6444-2020-4-545.
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This article describes the reasons for invalidity of legal transactions resulting from a defect of, and/or failure in the expression of will for concluding a particular legal transaction. This contribution begins with an introduction to the statutory requirements for declaring such transactions invalid - ranging from wilful deceit, coercion, misapprehension, fraudulent agreement to coincidence of severe circumstances - and focuses primarily on the legal positions taken up by the Ukrainian courts. In conclusion, the article suggests that the current Ukrainian case law with regard to the invalidity of legal transactions is characterized by several inconsistencies.
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Nigrini, Mark J. "The patterns of the numbers used in occupational fraud schemes." Managerial Auditing Journal 34, no. 5 (May 7, 2019): 606–26. http://dx.doi.org/10.1108/maj-11-2017-1717.
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Purpose This study aims to classify the numbers used in recent financial statement, corruption and asset misappropriation fraud schemes in such a way that these classes can be used to design effective proactive analytics-based fraud detection tests. Design/methodology/approach The data sources for the classification scheme include the court records of fraud prosecutions, investigative reports and research papers related to fraud cases. Findings Fraudulent numbers are most often amounts that are round, have a strong period-over-period growth, are just above or below internal control thresholds or other targets, are deviations from Benford’s Law, are purposeful duplicates of authentic transactions, are outliers due to being excessively large and are excessively rounded up or down. The study includes several examples of fraudulent numbers. Research limitations/implications The fraudulent number types are based on a sample of fraud-related court documents, and the sample might not be representative of the population of detected and undetected frauds. Further research is needed into the detection of corruption/bribery schemes. Practical implications The results are important for auditors and forensic accountants running proactive fraud detection tests. The discussions emphasize that the analysis should include refining and rerunning the tests, and then using groupings and filtering to deal with false positives. The importance of an effective audit of the notable transactions is stressed in the concluding section. Originality/value The study is an original in-depth coverage of the patterns found in fraudulent numbers. The discussion sections review implementation issues and considerations for future research.
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Bach, Mirjana Pejić, Nikola Vlahović, and Jasmina Pivar. "Fraud Prevention in the Leasing Industry Using the Kohonen Self-Organising Maps." Organizacija 53, no. 2 (May 1, 2020): 128–45. http://dx.doi.org/10.2478/orga-2020-0009.
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AbstractBackground and Purpose: Data mining techniques are intensely used in various industries for the purpose of fraud prevention and detection. Research that focuses on the leasing industry is scarce, although frauds in the field of leasing occur rather often. First, we identify clusters of business clients in one leasing company by using the method of self-organising maps based on leasing contract attributes. Second, we compare clusters based on the presence of fraudulent clients, in order to develop fraudsters’ profiles.Methodology: For detecting characteristics of fraudulent clients, we use a client database containing leasing contract attributes of one Croatian leasing company. In order to develop profiles of fraudulent clients, we utilise a clustering procedure with the Kohonen Self-Organizing Maps supported by Viscovery SOMine software.Results: Five clusters were identified and labelled according to the modal values of attributes describing the leasing object and the industry in which the client operates: (i) New cars / Trade; (ii) Used trucks or tugboats / Other services; (iii) New machinery / Construction; (iv) New motors / Trade; and (v) New machinery and tractors / Agriculture.Conclusion: Self-organising maps have proved to be a useful methodology for developing profiles of fraudulent clients in leasing companies. Companies can use our results and make additional efforts in monitoring clients from the identified industries, buying specific leasing objects. In addition, companies can apply our methodology to their own databases, in order to develop fraudster profiles for their specific purposes, and implement fraud alert mechanisms in their client database.
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Čunderlík, Ľubomír. "Fraudulent Schemes in the Financial Market (Financial Pyramids) – Detection and Prevention." Financial Law Review, no. 21 (1) (2021): 16–30. http://dx.doi.org/10.4467/22996834flr.21.002.13285.
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This contribution deals with fraudulent schemes in the financial market. The main aim of the contribution is to provide main identifying features of fraudulent practices that prove to be a financial pyramid. The author summarizes in one place numerous features that indicate the financial pyramid (Ponzi scheme), especially operating on the financial or capital market. He concludes, the state of play of legislation regarding the features is insufficient. The hypothesis to confirm or disprove is there is no uniform legal provision covering all features of the pyramid scheme in Slovakia, the relevant legislation is limited to the prohibition of certain practices, which a priori may not constitute a pyramid scheme.
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Valaskova, Katarina, and Richard Fedorko. "Beneish M-score: A measure of fraudulent financial transactions in global environment?" SHS Web of Conferences 92 (2021): 02064. http://dx.doi.org/10.1051/shsconf/20219202064.
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Research background: Earnings is a source of information for capital owners, potential investors, competitors, customer and supplier of the company. Managers have the direct motivation and knowledge and use adequate techniques to adjust legally the reported earnings to meet the specific requirements of the company and achieve stable financial results. Thus, earnings management is currently the most provocative and highly topical issue in the field of finance and accounting at the global perspective. Purpose of the article: The main purpose of the paper is to detect the manipulation with earnings in a specific sector of economy, following the global principles of financial reporting, and to reveal the degree of manipulation of enterprises in the selected countries of the Visegrad grouping. Methods: The model of Beneish M-score is applied using the sectoral data and compares the level of manipulation in the period 2015-2019. The Beneish model is a mathematical-statistical model that uses financial ratios calculated with accounting data of a specific enterprise aimed to detect if an enterprise is likely that the reported earnings of the company were manipulated. Findings & Value added: The paper monitors the development of the manipulation with earnings in the given sector (enterprises tend to manage earnings upwards), and analyses the influences of macroeconomic factors on the phenomenon of earnings management. The detection of earnings management by M-score helps protect business partners of an enterprise against fraudulent behaviour, especially in the global environment.
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Mariia, NEZHYVA, and MYSIUK Viktoriia. "ASP STRUCTURE: PREVENTION OFECONOMIC FRAUD." Herald of Kyiv National University of Trade and Economics 135, no. 1 (February 24, 2021): 41–52. http://dx.doi.org/10.31617/visnik.knute.2021(135)03.
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Measures to detect fraud and fraudsters have been developed in the article, including the definition of common "red flags" for a person who commits typical and accidental abuse. The main organizational control structures used to prevent fraud are identified. Evidence was analyzed in both paper and electronic formats, including big data, to form a classification of fraudsters and fraudulent activities. Possible solutions for effective fraud management and its prevention within the company by the help of ASP approach were developed. Keyworlds: fraud, economicfraud, fraudster, bribery, forensic, ASP, ACFE.
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Ratmono, Dwi, Darsono Darsono, and Nur Cahyonowati. "Financial Statement Fraud Detection With Beneish M-Score and Dechow F-Score Model: An Empirical Analysis of Fraud Pentagon Theory in Indonesia." International Journal of Financial Research 11, no. 6 (December 1, 2020): 154. http://dx.doi.org/10.5430/ijfr.v11n6p154.
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This research contributes to the Financial Statement Fraud (FSF) literature by examining the ability of the Beneish model and the F-Score model to detect FSF trends in the Indonesian context. This study also aims to provide empirical evidence on other issues that encourage fraud. The results of this study are empirical evidence that the financial target variables and CEO narcissism have a significant effect on financial statement fraud while financial stability, external pressure, supervision ineffectiveness, related party transactions, auditor turnover, and CEO dominance have no significant effect on financial statement fraud. Furthermore, when viewed in the table of the F-Score and M-Score models, there are several companies suspected or indicated of fraudulent financial reporting, including 284 companies out of 385 observation samples. The percentage of companies indicated to have financial statements fraud requires further examination to really prove that the company is cheating. The results of the fraudulent financial report analysis using the F-Score dan M-score for manufacturing companies in 2014 - 2018 successfully analyzed a total of 284 companies that indicated fraudulent financial reporting.
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Adams, Adrienne E., Angela K. Littwin, and McKenzie Javorka. "The Frequency, Nature, and Effects of Coerced Debt Among a National Sample of Women Seeking Help for Intimate Partner Violence." Violence Against Women 26, no. 11 (April 22, 2019): 1324–42. http://dx.doi.org/10.1177/1077801219841445.
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This study examines the frequency, nature, and effects of coerced debt, defined as non-consensual, credit-related transactions that occur in intimate relationships where one partner uses coercive control to dominate the other. The sample includes 1,823 women who called the National Domestic Violence Hotline. Results suggest that coerced debt, from both coercive and fraudulent transactions, is a common problem and is significantly related to control over financial information, credit damage, and financial dependence on the abuser. This study supports the need for policy reform and victim services aimed at addressing coerced debt, thereby mitigating a potentially significant economic barrier to safety.
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Kumar, Ankaj, Gouri Sankar Mishra, Parma Nand, Madhav Singh Chahar, and Sonu Kumar Mahto. "Financial Fraud Detection in Plastic Payment Cards using Isolation Forest Algorithm." Regular issue 10, no. 8 (June 30, 2021): 132–36. http://dx.doi.org/10.35940/ijitee.g8873.0610821.
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The need for technology has always found space in Financial Transaction as the number of fraud in financial transactions increases day by day. In this research we have proposed a new methodology by using the isolation forest algorithm and local outlier detection algorithm to detect the financial fraud. A standard data set is used in experimentation to classify a transaction occurred is a fraudulent or not. We have used neural networks and machine learning for classification. We have focused on the deployment of anomaly detection algorithms that is Local Outlier Factor and Isolation Forest algorithm (IFA) on financial fraud transactions data.
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Matloob, Irum, Shoab Khan, Habib ur Rahman, and Farhan Hussain. "Medical Health Benefit Management System for Real-Time Notification of Fraud Using Historical Medical Records." Applied Sciences 10, no. 15 (July 27, 2020): 5144. http://dx.doi.org/10.3390/app10155144.
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This paper presents a novel framework for fraud detection in healthcare systems which self-learns from the historical medical data. Historical medical records are required for training and testing of machine learning models. The main problem being faced by both private and government health supported schemes is a rapid rise in the amount of claims by beneficiaries mostly based on fraudulent billing. Detection of fraudulent transactions in healthcare systems is a strenuous task due to intricate relationships among dynamic elements including doctors, patients, service. In light of aforementioned challenges in health support programs, there is a need to develop intelligent fraud detection models for tracing the loopholes in procedures which may lead to successful reimbursement of fraudulent medical bills. In order to address the issue of fraud in healthcare programs our solution proposes a framework based on three entities (patient, doctor, service). Firstly, the framework computes association scores for three elements of the healthcare ecosystem namely patients, doctors or services. The framework filters out identified cases using association scores. The Confidence values, after G-means clustering of transactional data, are computed for each service in each specialty. Rules are generated based on the confidence values of services for each specialty. Then, an evaluation of identified cases is done using rule engine. The framework classifies cases into fraudulent activities based on the similarity bit’s value. The validation of framework is performed on local hospital employees transactional data which includes many reported cases of fraudulent activities in addition to some introduced anomalies.
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Chang, Remco, Alvin Lee, Mohammad Ghoniem, Robert Kosara, William Ribarsky, Jing Yang, Evan Suma, Caroline Ziemkiewicz, Daniel Kern, and Agus Sudjianto. "Scalable and Interactive Visual Analysis of Financial Wire Transactions for Fraud Detection." Information Visualization 7, no. 1 (February 21, 2008): 63–76. http://dx.doi.org/10.1057/palgrave.ivs.9500172.
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Large financial institutions such as Bank of America handle hundreds of thousands of wire transactions per day. Although most transactions are legitimate, these institutions have legal and financial obligations to discover those that are suspicious. With the methods of fraudulent activities ever changing, searching on predefined patterns is often insufficient in detecting previously undiscovered methods. In this paper, we present a set of coordinated visualizations based on identifying specific keywords within the wire transactions. The different views used in our system depict relationships among keywords and accounts over time. Furthermore, we introduce a search-by-example technique, which extracts accounts that show similar transaction patterns. Our system can be connected to a database to handle millions of transactions and still preserve high interactivity. In collaboration with the Anti-Money Laundering division at Bank of America, we demonstrate that using our tool, investigators are able to detect accounts and transactions that exhibit suspicious behaviors.
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Mirinaviciene, Stacey. "INTERNAL CONTROL AND FRAUD PREVENTION: PRIOR RESEARCH ANALYSIS." Science and Studies of Accounting and Finance: Problems and Perspectives 9, no. 1 (November 25, 2014): 173–79. http://dx.doi.org/10.15544/ssaf.2014.19.
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The focus of this study is to analyze prior research on fraud detection and prevention. Most researchers agree that strong internal controls are an influencing factor on fair financial reporting and fraud prevention and detection. Financial statement and employee fraud can be very expensive to businesses and the economy as a whole. The establishment and evaluation of the internal control methods and procedures can decrease fraudulent events and losses. Accounting professionals, CPA’s, and tax preparers are the first to detect “red flags” in business activities and must work together with boards of directors, CFO’s, and small business owners. Simple methods, such as ratio analyses can help to signal early signs of fraudulent events and prevent future damages. Implementation of fraud prevention measures are the most efficient deterrent. Some of the most effective controls like, job rotation, mandatory vacations, training, fraud hotlines, and surprise audits, need not be expensive and should be employed by all businesses. Unfortunately, the most important and effective fraud prevention techniques are seldom applied by businesses. Surprisingly, the least effective and most expensive measures, like external audits, are more frequently employed. As reported in this review of the literature, most businesses focus on fraud detection, while fraud prevention and implementing proper internal controls would result in better prevention of financial losses.
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Stojanović, Branka, Josip Božić, Katharina Hofer-Schmitz, Kai Nahrgang, Andreas Weber, Atta Badii, Maheshkumar Sundaram, Elliot Jordan, and Joel Runevic. "Follow the Trail: Machine Learning for Fraud Detection in Fintech Applications." Sensors 21, no. 5 (February 25, 2021): 1594. http://dx.doi.org/10.3390/s21051594.
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Financial technology, or Fintech, represents an emerging industry on the global market. With online transactions on the rise, the use of IT for automation of financial services is of increasing importance. Fintech enables institutions to deliver services to customers worldwide on a 24/7 basis. Its services are often easy to access and enable customers to perform transactions in real-time. In fact, advantages such as these make Fintech increasingly popular among clients. However, since Fintech transactions are made up of information, ensuring security becomes a critical issue. Vulnerabilities in such systems leave them exposed to fraudulent acts, which cause severe damage to clients and providers alike. For this reason, techniques from the area of Machine Learning (ML) are applied to identify anomalies in Fintech applications. They target suspicious activity in financial datasets and generate models in order to anticipate future frauds. We contribute to this important issue and provide an evaluation on anomaly detection methods for this matter. Experiments were conducted on several fraudulent datasets from real-world and synthetic databases, respectively. The obtained results confirm that ML methods contribute to fraud detection with varying success. Therefore, we discuss the effectiveness of the individual methods with regard to the detection rate. In addition, we provide an analysis on the influence of selected features on their performance. Finally, we discuss the impact of the observed results for the security of Fintech applications in the future.
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Kumar, Kapil, Shyla, and Vishal Bhatnagar. "Credit Card Fraud Transaction Detection System Using Neural Network-Based Sequence Classification Technique." International Journal of Open Source Software and Processes 12, no. 1 (January 2021): 21–40. http://dx.doi.org/10.4018/ijossp.2021010102.
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The movement towards digital era introduces centralization of information, web services, applications, and devices. The fraudster keeps an eye over ongoing transaction and forges data by using different techniques as traffic monitoring, session hijacking, phishing, and network bottleneck. In this study, the authors design a framework using deep learning algorithm to suspect the fraudulence transaction and evaluate the performance of the proposed system by updating data repositories. The neural network-based sequence classification technique is used for fraud detection of credit card transactions by including threshold value to measure the deviation of transaction. The reconstruction error (MSE) and predefined threshold value of 4.9 is used for determination of fraudulent transactions.