Instances Of Fraud Research Articles

Influence of Fraud Triangle Framework on the Fraud Prevention and Detection Programs in PT. XX

Given the significant variability of fraud instances in businesses of all sizes, it is captivating that despite their complexity, the fraud triangle's three elements can be used to explain all of them. As a result, the researcher was compelled to examine the impact of this framework on fraud programs. This report aims to thoroughly analyze the fraud triangle's influence on fraud prevention and detection programs, particularly at PT. XX. This qualitative research data collection comprises two main target population sources, the internal auditor team and the company's management, through a semi-structured interview approach where data is later processed using the in vivo and pattern coding technique. It is found from the study that each dimension of the fraud triangle which are pressure, opportunity, and rationalization, has a direct causal link to a fraud program described in the company's fraud SOP. The analysis concluded that the internal auditor team used the fraud triangle framework to identify patterns in fraud justifications and then developed several preventive fraud programs that management and users will implement to reduce the amount of fraud occurring in PT. XX. Further findings included the mutually reinforcing roles undertaken by the two divisions under study in creating the SOP and carrying out the company's fraud programs, which the study contends have already been effectively executed.

Fraud Detection of Credit Cards Using Supervised Machine Learning Techniques

Credit card fraud encompasses illicit activities aimed at unlawfully obtaining confidential information to enable unauthorized individuals to engage in illegal transactions. As technology advances, fraudsters have honed their skills in evading security measures, presenting a formidable challenge in fraud detection. To address this issue, an array of algorithms and analytical techniques has emerged to identify and mitigate instances of fraud. This research aimed to identify the most appropriate supervised machine learning algorithm for credit card fraud detection. Logistic Regression, Random Forest, Support Vector Machine, and Decision Trees were implemented and compared. Due to the imbalanced nature of the dataset, the SMOTE (Synthetic Minority Oversampling Technique) technique was employed to rectify the data imbalance by oversampling the minority class. The performance of the trained models was evaluated using various metrics, including the confusion matrix, accuracy, precision, recall, f1-score, Matthews Correlation Coefficient (MCC), and Area Under the Curve (AUC). The results of the analysis revealed that Random Forests exhibited exceptional performance, achieving an impressive recall score of 84% and surpassing other algorithms. This research provides the groundwork for future investigations involving diverse deep-learning techniques applied to real-time and dynamic datasets, enabling continuous enhancements in fraud detection and prevention mechanisms.

Forensic Accounting Techniques and Attainment of Results in Federal Government Parastatals in Nigeria

Economic and financial fraud instances have risen over the past few years. However, fraud is a dynamic phenomenon, and new ways to defraud people, businesses, even government and the environment as a whole, appear every day. Adopting significant accounting techniques and fraud detection measures that are capable of identifying and preventing any type of fraud, regardless of how complex, is therefore imperative. The forensic accounting technique is one of these methods. This is one of the types of techniques that is primarily responsible for the investigation of fraud and is created primarily in the field of public and private finances. This study therefore examines the effect of forensic accounting techniques on attainment of results in Federal Government parastatals in Nigeria. The study adopted survey research design. The population of the study was 5 federal government agencies. Purposive sampling technique was used to select 2 agencies. The target population of the study was 131 accountants in the two agencies chosen- Nigeria Port Authority and Nigeria Civil Aviation Authority. Total enumeration was adopted. A validated and structured questionnaire was used to administer 131 copies of the questionnaire to the respondents. 126 copies were retrieved which represented 96% response rate. The Cronbach alpha reliability coefficients ranged from 0.740 – 0.892. The data were analyzed using descriptive and inferential (multiple regression) statistics at 5% level of significant. The result found that forensic accounting techniques had significant effect on output maximization in public sector of Nigeria (Adj. R2 = 0.183; F(6, 125)= 7.995, p < 0.05). The study concluded that forensic accounting techniques had a significant positive effect on attainment of result (effectiveness). The study recommended that government should adopt forensic accounting investigation techniques in corruption investigation significantly achieve attainment of results in government parastatals.

LESSONS FOR THE INDIAN BANKING INDUSTRY FROM THE BANK FAILURES IN THE UNITED STATES

ABSTRACT The Indian banking industry is mature and efficient. However, it has witnessed nearly 38,000 instances of fraud involving a total amount of $22 billion in the last five years. The $1 billion fraud using the Society for Worldwide International Funds Transfer (SWIFT) platform in Punjab National Bank emphasizes the importance of internal control within the bank and the regulator’s failure in supervision. The amalgamation of the Lakshmi Vilas Bank (LVB) with the DBS Bank, Singapore was necessitated by large continuous losses in three consecutive years leading to erosion of equity. The liquidity stress faced by the Infrastructure Leasing & Financial Services (IL&FS) and Dewan Housing Finance Ltd. (DHFL) indicated the unsustainable market funding conditions for the non-bank financial corporations (NBFC). The Reserve Bank of India initiated the Corporate Insolvency Resolution Process (CIRP) in order to finalize the resolution of Dewan Housing Finance Corporation Limited (DHFL) after noticing misuse of customer funds. Despite 3,518 bank failures from 1980 to the present day, the banking industry in the United States is strong with assets valued at $24 trillion and deposits of $20 trillion thanks to the robust functions of the Federal Deposit Insurance Corporation (FDIC). Its coverage of 4,817 banks is a model for the Indian Banking industry that is plagued with the myth of benefits of nationalization, mounting non-performing assets, and instances of frauds. This paper offers empirical evidence of the strength of the US banking leading to suggestions of policy reforms for its counterpart in India. Keywords bank failure, deposit insurance, regulation, nationalization

The Effect of Audit, Corporate Culture, Audit Committee, Inspection, Policies and Procedures, and it Adoption on Fraud Effectiveness Risk Management Banking Industry in Indonesia

Fraud is a morally repugnant, illegal, costly societal issue. Because fraud has such a negative impact on economic development, profitability, and the welfare of business, it has emerged as a major threat on a global scale. Moreover, each year, there are a growing number of instances of fraud that are recorded. In Recent years, instances of fraud have captured the attention of the general public. This study aims to determine whether internal audit, corporate culture, audit committees, inspectors, policies, and procedures as well as the adoption of information technology affect the effectiveness of management fraud risk in the banking sector in Indonesia or not. The study uses quantitative research method. The results of study show that anternal audit, inspection, and IT adoption were discovered to have a positive and significant relationship with the efficiency of bank fraud risk management by the researchers. While other variables, such as corporate culture, audit committee, and policies and procedures, have a negative relationship with the effectiveness of fraud risk management, the policies and procedures have no significant impact on the effectiveness of fraud risk management.

Should the Electronic Communications and Transactions Act be amended to include electronic signatures for the sale of immovable property in South Africa?

Electronic signatures have become a core feature of digital transformation. Organisations can now transact with greater ease, regardless of physical distance or national borders. The Electronic Communications and Transactions Act 25 of 2002 facilitates electronic communications and transactions using electronic documents and signatures in South Africa. Electronic contracts and signatures are legally binding and constitute valid and admissible evidence in legal proceedings, although there are a few exceptions. One of the exclusions concerns agreements for the sale of immovable property. The Alienation of Land Act 68 of 1981, which regulates the sale of land, seeks to promote legal certainty as to the authenticity and contents of these contracts to limit instances of fraud and litigation. This article examines the risk associated with fraud and the case of Borcherds v Duxbury 2021 (1) SA 410 (ECP). In this case, contrary to legislation, the court accepted an electronic signature in a contract for the sale of immovable property. I recommend that the relevant legislation be amended to validate the use of advanced electronic signatures for the sale of immovable property. A holistic approach to electronic signatures is the only way to embrace an inevitable and complete digital transformation.

FORENSIC CREDIT CARD FRAUD DETECTION USING DEEP NEURAL NETWORK

This paper describes the ways in which forensic credit card fraud detection can be achieved using deep neural network (DNN). The problem that this research addresses is to identify a technique that can be used by forensic experts in credit card fraud investigations to detect if a fraudulent transaction has occurred. It is an obligation that institutions providing financial services must implement relevant safeguards against credit card fraud to prevent possible loss of their investments or clients’ funds. This paper presents a forensic detection model of credit card fraud that is based on sequential data modeling using Long Short-Term Memory (LSTM) DNNs. The current study determines whether LSTM-attention algorithm can identify the most important transactions in an input sequence that provides high-accuracy prediction of fraudulent transactions. The effectiveness of the LSTM-attention model is achieved by selecting the most relevant predictive features, uniform manifold approximation, transaction sequences, and attention mechanisms that improve the performance of the model. The results show that LSTM-attention algorithms can be used to conduct forensic credit card fraud detection with high accuracy and precision. The novelty of the research paper is that it successfully uses an LSTM-attention algorithm to detect credit card fraud instances and proves the applicability of the model in mitigating fraudulent transactions in banking institutions. Keywords: Credit Card Fraud, Machine Learning, Deep Neural Network, Convolutional Neural Network, Long Short-Term Memory DOI： https://doi.org/10.35741/issn.0258-2724.58.1.33

A critical evaluation of forensic accounting studies with a view to discerning how practice can be improved to prevent cases of fraud

This article provides a critical evaluation of the existing literature that has been developed to date regarding forensic accounting to then discern how the practice could be potentially improved to prevent instances of fraud from arising. To achieve this, this article first provides an understanding of what forensic accounting is and why it is important for preventing instances of fraud. Then, more specifically, this article considers the literature that has been produced in this area about whether and, if so, why there is a need for the practice of forensic accounting to be improved upon to prevent instances of fraud. On this basis, the literature is also evaluated to discern whether any recommendations may be made to prevent instances of fraud from arising in the future and on what basis these recommendations could be made. This is because it is hypothesised that whilst the practice of forensic accounting already achieves a considerable amount regarding the prevention of fraud because of the diverse nature of this kind of work, there is still considerable scope for improvement due to a variety of issues that are considered during this article’s literature review. Finally, this article concludes with a summary of the key points that have been derived from the analysis undertaken regarding the practice of forensic accounting and its potential for improvement to prevent instances of fraud from arising.

ONLINE FRAUD DETECTION

In today’s fast moving world as the demand of the internet is increasing with this cyber-attacks are also increasing and Phishing is one the most common cyberattack among them. Taking a user’s personal information such as credit card numbers, login details, confidential info, and so on through illegitimate activities of using the internet without the user’s knowledge and using it for blackmailing, debiting money from the user’s account, or any other purpose with the wrong intentions is known as phishing. In phishing attack, a phisher or attacker pretends to be masquerade as a known person or organization to the user and sends the mails or messages which contains malicious links in them and these malicious links contains harmful software’s or viruses which steals the user’s computer data, financial data, login credentials such as User ID and passwords, credit card details, etc. Phishing is the most common and dangerous cyberattack which is growing in the today’s world. Nowadays phishers are working smartly they are using the new techniques for creating the malicious links and embeds them in the emails and messages and sends it to the user which looks similar to the trusted mail or message to the user and as soon as the user clicks on the malicious link it redirects the user to the malicious webpage or runs the harmful software in the backend while the user is reading the email or message and takes over the соntrоl оf the user’s соmрuter аnd steаls аll dаtа оf the user’s соmрuter. Due to a speedy development inside the digital commerce generation, the usage of credit playing cards has dramatically extended. In view that credit card is the most popular mode of fee, the number of fraud instances related to it is also rising. As a result, in order to prevent these frauds, we need an excellent fraud detection system that can detect them correctly. We created the idea of credit card frauds in this paper, and we used a variety of device learning methods on an unbalanced dataset, including logistic regression, naivebayes, and random wooden area with ensemble classifiers using the boosting approach. An in-depth analysis of the existing and proposed models for credit card fraud detection has been completed, as well as a comparison of these tactics. So one of a kind classification models are applied to the statistics and the model performance is evaluated on the basis of quantitative measurements which include accuracy, precision, recollect, f1 score, confusion matrix. The realization of out observe explains the first class classifier via schooling and trying out using supervised strategies that offers better answer. KEYWORDS: phishing, credit card, cyberattack, malicious, detection, webpage.

Developing an Intelligent Credit Card Fraud Detection System with Machine Learning

Credit card fraud is a significant issue in the economic services sector. Each year, billions of rupees are lost due to credit card fraud. Due to confidentiality concerns, there are an absence of studies examining actual credit card records. In this paper, machine learning algorithms are employed to detect credit card fraud. First, standard models are utilized. Then, hybrid techniques consisting of Random Forest, AdaBoost, XGBoost, and majority voting are implemented. To evaluate the effectiveness of the version, a set of publicly accessible credit card records is utilized. Then, credit card records from a real-world economic institution are analyzed. The experimental results suggest that Random Forest and majority voting accomplish precise accuracy estimates for detecting credit card fraud instances.

The Cost of Fraud Prediction Errors

ABSTRACT We compare seven fraud prediction models with a cost-based measure that nets the benefits of correctly anticipating instances of fraud against the costs borne by incorrectly flagging non-fraud firms. We find that even the best models trade off false to true positives at rates exceeding 100:1. Indeed, the high number of false positives makes all seven models considered too costly for auditors to implement, even in subsamples where misreporting is more likely. For investors, M-Score and, at higher cut-offs, the F-Score, are the only models providing a net benefit. For regulators, several models are economically viable as false positive costs are limited by the number of investigations regulators can initiate, and by the relatively low market value loss a “falsely accused” firm would bear in denials of requests under the Freedom of Information Act (FOIA). Our results are similar whether we consider fraud or two alternative restatement samples. Data Availability: Data are available from the public sources cited in the text. JEL Classifications: G31; G32; G34; M40.

Simple steps help avoid fraud

Instances of fraud and financial malfeasance in the nonprofit sector are, unfortunately, not hard to come by. Neither, thankfully, are strategies to help ward off fraud by keeping on top of the organization's finances. But that requires an engaged board of directors.

Quand une influence minoritaire suffit à rendre plus tolérant à la fraude: Une étude expérimentale au sein de petits groupes

AbstractThis paper studies minority influence on individual tolerance to certain instances of fraud. Using an experimental approach, we divided 100 students from a business school into 25 groups and compared the individual trajectory of each group according to whether they were in a group composed of only fraud‐intolerant individuals (control), in a group with one fraud‐tolerant individual (minority), or in a group with two fraud‐tolerant individuals (equality). Our results indicate that individual fraud tolerance increases significantly after a group discussion in which only one more fraud‐tolerant individual participates, demonstrating the magnitude of the minority influence on the trajectory of individuals who were initially fraud intolerant.

When minority influence is enough to increase tolerance of fraud: An experimental small‐group study

AbstractThis paper studies minority influence on individual tolerance to certain instances of fraud. Using an experimental approach, we divided 100 students from a business school into 25 groups and compared the individual trajectory of each group according to whether they were in a group composed of only fraud‐intolerant individuals (control), in a group with one fraud‐tolerant individual (minority), or in a group with two fraud‐tolerant individuals (equality). Our results indicate that individual fraud tolerance increases significantly after a group discussion in which only one more fraud‐tolerant individual participates, demonstrating the magnitude of the minority influence on the trajectory of individuals who were initially fraud intolerant.

A Blockchain-based framework for Agriculture subsidy disbursement

Agriculture is the primary livelihood factor in the growth of India’s economy. One of the significant challenges faced by the Government of India in the agriculture sector is to increase the delivery of subsidies by re-engineering the existing process. One of the solutions initiated by the government of India on 1st January 2013 with the highest priority is called a Direct Cash transfer scheme under the Direct Benefit Transfer (DBT) scheme. It involves crediting subsidies (cash/payments) directly to the farmer’s account. However, the major problem associated with this scheme is that the government subsidies do not reach all the eligible farmers due to corruption, delay, intermediaries, and other reasons. The present direct cash transfers system’s major drawbacks such as (key loopholes are) lack of proper auditing, inability to track subsidy, and inadequate digital banking infrastructure especially in rural areas. In this regard, the disbursement of direct cash transfer scheme is addressed benefitting the farmers directly via Blockchain Smart Contracts technology thereby ensuring transparency, de-duplication, reduced delays, and reducing instances of fraud in the existing government subsidy delivery system. We aim to develop a blockchain-based smart contracts prototype model called a single-window approach for automating farmer’s subsidies and eliminating middlemen. The automated model will be fast, transparent, and immutable by reducing the complexities faced by Indian farmers.

Basic Understanding of Fraudulent Activities in Corporate Organisation

Purpose: This paper is to enhance our understanding of fraudulent behaviour, happening in corporate organisations Design/methodology/approach: The discussion in this paper is based on our empirical studies, in which the underlying logics are adopted based on fraud triangle theory. Findings: Fraud is a serious issue that could bring down a company, no matter its size. Because fraud is such a monumental problem, it is important to combat it vigorously. Fraud detection and prevention should be an ongoing initiative. In addition, while we cannot prevent every instance of fraud from happening to business, we can take precautions to minimize fraudulent activity. This paper provides insights that can significantly reduce fraudulent activities from occurring or cut losses if a fraud already occurred. Making the company policy known to employees is one of the best ways to deter fraudulent behaviour. Following through with the policy and enforcing the noted steps and consequences when someone is caught is crucial to preventing fraud. The cost of trying to prevent fraud is less expensive to a business than the cost of the fraud that gets committed. Originality: While research has suggested how a single individual engages in fraud, we still do not understand how groups of individuals become involved. In this paper, we seek to contribute to the literature by considering such issues. And this paper also offers the possible measures to deal with it.

Does IRS Monitoring Deter Managers From Committing Accounting Fraud?

We examine whether monitoring by the Internal Revenue Service (IRS) affects managers’ decisions to engage in fraudulent financial reporting. We argue that IRS monitoring provides a disciplining effect reducing managements’ incentives to engage in rent diversion activities such as costly financial statement misreporting. Using information on IRS audit rates and instances of fraud disclosed in Securities and Exchange Commission (SEC) Accounting and Auditing Enforcement Releases (AAERs), we find evidence consistent with IRS monitoring providing positive spillover effects in reducing the likelihood of accounting fraud. Our results are robust to using a matched sample of fraud and nonfraud firms. Altogether, we find evidence that tax authorities provide positive externalities in reducing agency costs through monitoring and enforcement.

Recommendations to protect patients and health care practices from Medicare and Medicaid fraud

Fraud is defined as knowingly submitting, or causing to be submitted, false claims or making misrepresentations of a fact to obtain a federal health care payment for which no entitlement would otherwise exist. In today’s health care environment, Medicare and Medicaid fraud is not uncommon. The negative impact of fraud is vast because it diverts resources meant to care for patients in need to the benefit of fraudsters. Fraud increases the overall costs for vital health care services and can potentially be harmful to Medicare and Medicaid beneficiaries. The objectives of this commentary are to describe the types and trends of Medicare and Medicaid fraud that are committed, and provide recommendations to protect patients and health care practices. Specifically, this article identifies types of Medicare and Medicaid fraud at beneficiary (patient) and provider level, and it can be intentional or unintentional. This article also describes the 3 primary laws that prohibit fraud and gives fraud case examples relevant to each law, including the False Claims Act, Anti-Kickback Statute, and the Stark Law. We also discuss currently trending and emerging areas, including opioid and pharmacogenetic testing; both have experienced heavier and higher-profile instances of fraud in today’s health care landscape. Last, the article summarizes detection methods and recommendations for health care providers and patients to protect themselves against fraud. Recommended strategies to combat fraud are discussed at policy, practice, and grassroots levels. Health care practitioners, including pharmacists, can use these strategies to protect themselves and their patients from becoming victims of fraud or unknowingly committing fraud.

High-throughput screening of argan oil composition and authenticity using benchtop 1 H NMR.

We use 60‐MHz benchtop nuclear magnetic resonance (NMR) to acquire 1H spectra from argan oils of assured origin. We show that the low‐field NMR spectrum of neat oil contains sufficient information to make estimates of compositional parameters and to inform on the presence of minor compounds. A screening method for quality and authenticity is presented based on nearest‐neighbour outlier detection. A variety of oil types are used to challenge the method. In a survey of retail‐purchased oils, several instances of fraud were found.

The Cost of Fraud Prediction Errors

We compare seven fraud prediction models with a cost-based measure that nets the benefits of correctly anticipating instances of fraud (true positives), against the costs borne by incorrectly flagging non-fraud firms (false positives). Our cost-based measure supplements traditional comparison metrics and is estimated separately for auditors, investors, and regulators. We find that the higher true positive rates in recent models come at the cost of higher false positive rates, and that even the best models trade off false to true positives at rates exceeding 100:1. Indeed, the high number of false positives makes all seven models considered too costly for auditors to implement, even when we consider extreme subsamples in which a priori the likelihood of misreporting is greater. For investors, M-Score and, when used at higher cut-offs the F-Score, are the only models providing a net benefit; the other models are only economically viable when applied to extreme subsamples of misreporting risk. For regulators, several models are economically viable as false positive costs are limited by the number of investigations they can initiate, and by the relatively low market value loss a ‘falsely accused’ firm would bear in denials of requests under the Freedom of information Act (FOIA). Our results are similar irrespective of whether we consider fraud or two alternative restatement samples.