Fraud Model Research Articles

Fraud Management Model and Forensic Accounting – A Systematic Literature Review

This study interprets the importance of critical thinking and concern for the triggers of fraudulent behavior and financial fraud. The purpose of the study is expanded by explaining the application of fraud management as corporate governance to prevent fraud, and the involvement of forensic accounting practices is necessary. This study, a systematic literature review (SLR) by adopting part of the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) method, to identify, and construct a fraud management and forensic accounting model. The findings, fraudulent behavior occurs because there is a triggering factor, and someone has difficulty ignoring it. Psychologically it has taken root and rationalized the truth of fraud on the basis of pressure, opportunity supported by capability, such as triggers for fraudulent behavior in the fraud triangle model and the diamond fraud model. Synthesis of findings, it is important for internal control in fraud management to be applied for early detection and investigation and it is necessary to involve forensic accounting practices to detect, investigate accounting and litigation to resolve financial fraud in the financial data-based justice system. The novelty of this research offers a fraud management and forensic accounting model to prevent fraud and the financial condition of the company.

On fraud and certification of green production

AbstractWe present a novel model of fraud and certification in green production. We focus on settings where firms decide between a green or a standard version of a product together with an advertising strategy that can include fraud. In addition, green firms can choose to certify their production to guarantee the truthfulness of their claims. This results in four production‐advertising possibilities (standard, genuine green, fraudulent green, and certified green), by which we provide new insights about the prevalence of fraud and certification. We characterize the perfect Bayesian equilibrium of the resulting game for given green production costs, certification costs, and consumers' willingness to pay for standard production, and we perform comparative statics for the main parameters of the resulting game. We find that changes in certification and green production costs affect consumers' beliefs differently, whereby increases in certification and decreases in green production costs can broaden the likelihood of fraud. These novel results are robust to different market structures and question the general desirability of public subsidies for promoting green production without accompanying certification.

Construction of Accounting Fraud and Its Audit Countermeasure Model Based on Computer Technology

A prediction model of financial fraud of listed companies based on machine learning method is proposed to predict financial fraud of listed companies. Using the data set of Chinese listed companies from 2000 to 2020 as observation samples, Benford’s Law, LOF local anomaly method and SMOTE oversample were adopted, grey samples were excluded, and characteristic variables were selected from five aspects: fraud motivation, solvency, profitability, cash flow and operating capacity. The financial fraud identification model Xscore is established based on the XGBoost method. The Xscore model can improve the accuracy of model prediction, and is superior to the Fscore model and Cscore model in accuracy, recall rate, AUC index, KS value, PSI stability, etc. It is more suitable for predicting the financial fraud of listed companies in China. The results of this study are helpful in promoting the research and application of artificial intelligence and machine learning in accounting, and provide references for promoting the disclosure of high-quality financial information by listed companies and maintaining the order of the capital market.

Can The Diamond Fraud Model Influence the Occurrence of Fraudulent Financial Reporting?

This research is quantitative in nature and is intended to examine whether can the diamond fraud model influence the occurrence of fraudulent financial reporting. The fraud diamond theory is measured by pressure (financial stability, financial targets, and external pressure), opportunity (ineffective monitoring), rationalization (change in auditor), and capability (change of director). State-owned business (BUMN) registered on the stock exchange of Indonesia between 2016 and 2022 comprise the population considered within this research. Purposive sampling was utilized in this research to select 154 companies for the sample. In this work, logistic regression analysis is the analytical method. The findings within this research show that the variables of financial stability and financial targets have a significant negative impact on the occurrence of fraudulent financial reporting, while the variables of external pressure and director change do not affect the occurrence of fraudulent financial reporting. The variables of ineffective monitoring and change in auditors also affect the occurrence of fraudulent financial reporting .

Online banking fraud detection: A comparative study of cases from South Africa and Spain

Background: The banking sector provides online banking to offer their customers convenient and easy access to banking services. As most banking services are nowadays performed online, alarming fraudulent activities occur daily. The online banking frauds are increasingly and commonly being experienced globally and are damaging to both banks and customers.Objectives: The goal of the study was to investigate issues in online fraud detection in the banking sector from South Africa and Spain.Method: The study followed the Design Science Research (DSR) methodology. Data were collected through the qualitative approach particularly using focus groups and semi-structured interviews. The study followed non-probability sampling since it focussed on a specialised area of online fraud model. In total, the population size consisted of 17 participants, which included fraud technical managers, fraud investigator specialists, fraud technical specialists and data scientists.Results: In the South African context, the study established that there is a lack of online fraud experts in the banking sector. The findings reveal that the lack of online fraud expertise may lead to banks having weak detection systems. In the Spanish perspective, the study revealed that there is a lack of law regulation that poses a high risk.Conclusion: Given the lack of online fraud experts in the banking sector, banks are urged to acquire and develop online fraud expertise. Online banking technology is developing expeditiously than traditional transactions; therefore, regulations and policies need to be updated regularly to keep up with rapidly evolving technological changes.Contribution: Considering the significance of global online banking, the study suggested areas that the banking sector may investigate to develop and enhance online fraud detection models to combat online fraudulent activities.

PENTAGON FRAUD MODELLING: HOW IT AFFECTS FINANCIAL STATEMENT

This study examined conceptually the detection of financial statement fraud using Banking fraud in Indonesia Stock Exchange (BEI) Companies Listed on the Indonesia Stock Exchange (BEI) during 2017-2021 in a total of 200 samples from 40 companies using Eviews analysis. The methodology adopted in this study is quantitative research in which relevant and extant literature related to elements in Pentagon fraud model is reviewed about financial statement fraud. Financial stability does not affect financial statements in a significant linear fashion, and financial targets and external parties that seek to suppress have a significant negative impact on financial statements and do not show significance on other variables, being the result of this study. Current fraud insights are an important factor, both for managers in managing finances and for regulators/policymakers in preventing structured and reliable financial statement fraud.

Pentagon fraud model and financial statement fraud: The moderating role of Islamic corporate governance

The study aims to investigate the impact of Pentagon Fraud Model in five key factors, namely Pressure, Opportunity, Rationalization, Capability, and Action, on the risk of Financial Statement Fraud (FSF). Additionally, it explores the moderating role of Islamic Corporate Governance (ICG) in the relationship between these factors and FSF. The research employed a quantitative approach, utilizing survey data from a sample of 270 respondents. The findings support the hypotheses that all five factors significantly influence FSF, with Pressure exerting the highest impact. Furthermore, ICG is found to moderate the relationships between these factors and FSF, underscoring its role in reducing financial fraud risk. Practically, this study offers essential guidance for organizations in managing FSF risks more effectively. Integrating ethical and governance principles, as moderated by ICG, can help strengthen internal controls, ethics training, and a culture of integrity. Policymakers should consider these findings for enhancing regulatory frameworks, and educational institutions can integrate these results into their curriculum. The study's contribution lies in shedding light on the significance of ICG as a risk-mitigating factor in the context of FSF. The findings provide valuable insights for academics, practitioners, and policymakers. They enhance the understanding of FSF and its mitigating measures and offer a foundation for further research in the field.

Policy Formulation of Fraud Offenses in the New Penal Code Concept for Combating Technology-Related Crimes


 
 
 
 
 
 
 
 
 
 
 
 This study critically analyzes the formulation of criminal offenses related to fraud in the revised Penal Code (KUHP) within the context of contemporary technological advancements. Employing a normative legal research method, the investigation draws on relevant legal literature, legislation, and scholarly publications. The findings reveal inadequacies in the current legal provisions, particularly in addressing cyber fraud facilitated by information technology. The newly introduced Article 492 of the revised KUHP remains rooted in conventional fraud models, lacking the capacity to accommodate digital modus operandi and failing to encompass diverse identity theft techniques and system vulnerabilities. The absence of specific regulations pertaining to electronic transactions, computer networks, and digital systems further underscores the limitations. While the revised KUHP exhibits positive enhancements, urgent revisions are warranted to incorporate comprehensive elements, impose stricter penalties, and reinforce global cooperation in combating cybercrime. The overarching goal is to ensure that criminal provisions remain adaptive and responsive to the dynamic landscape of technology-driven offenses in the future.
 Highlights:
 
 
 The revised Penal Code inadequately addresses digital fraud, necessitating a comprehensive update to encompass evolving technological modus operandi.
 
 
 Existing legal provisions lack specificity concerning electronic transactions, computer networks, and digital systems, revealing a crucial gap in addressing contemporary cyber threats.
 
 
 Urgent revisions are imperative to fortify criminal statutes, impose stricter penalties, and enhance international collaboration, ensuring a responsive legal framework for combating future technology-based crimes.
 
 
 Keywords: Technological Fraud, Penal Code Revision, Cybercrime Legislation, Legal Adaptation, Global Cooperation
 
 
 
 
 
 
 
 
 
 
 

Evaluation of the anti-money laundering programs implementation in Indonesia

The term money laundering emerged in the early 19th century. Although various prevention and enforcement efforts have been developed since then, money laundering still occurs throughout the world. Therefore, it is necessary to carry out continuous evaluations to ensure that the applicable policies to curb money laundering remain adequate. This research aims to evaluate the effectiveness of anti-money launder-ing programs in Indonesia using the opportunity element approach in fraud models that are represented by the regulations, implementations, and supervisions. This research will evaluate: (1) the effectiveness of money laundering control, (2) banks levels of compliance, (3) the regulatory and supervisory body’s performance, and (4) the problems in implementation of the anti-money laundering program. This study uses a qualitative research method with an evaluation approach. This study obtained data from Indonesia’s Financial Service Authority, the Financial Intelligence Unit and the relevant banks as well as other relevant evaluations reports and indexes. The results of this study are that the implementation of the AML program in Indonesia is good but still needs improvement in its risk-based preventive measure and enforcement measures. The results of this research also shows that the identification of beneficial owner is one of the problems faced by banks.

Analysis of Hexagon Fraud Model, the S.C.C.O.R.E Model Influencing Fraudulent Financial Reporting on State-Owned Companies of Indonesia

Abstract This research aims to analyze the influence of each Hexagon Fraud factor that causes fraudulent financial reporting, namely Pressure, Opportunity, Rationalization, Arrogance, Capability and Collusion based on respondents’ perceptions of the content. The research methodology used is a quantitative method, sample determination was carried out using a purposive sampling method with certain criteria. The questionnaire was distributed to 3 leaders and 3 officials based on the selected sample. Primary data was obtained to test 6 research hypotheses based on the answers of 96 respondents from 16 State-Owned Enterprises (SOEs) from the selected sample. Data processing uses Smart PLS version 3.00 to test validity and reliability and test research hypotheses. The results of research on hexagon fraud theory show that Opportunity, Arrogance and Capability have a positive and significant effect on Fraudulent Financial Reporting, while Pressure, Rationalization and Collusion do not have a significant effect on Fraudulent Financial Reporting. Research findings reveal that fraud perpetrators are parties who have a deep understanding of the company’s internal control policies, especially knowledge about the effectiveness or ineffectiveness of internal controls, both from within and from outside SOEs. Fraud perpetrators may come from leaders or officials who are knowledgeable about the organizational structure and governance of officials in strategic positions.

Audit, Fraud, and Forensic Accounting

First, the paper will define fraud and analyze a fraudulent accounting situation and the responsibilities of a forensic accountant. Then, the paper will examine and analyze an accounting fraud case involving a money laundering scheme involving a retired couple who own a business and their son. Lastly, the paper will offer insight into the differences between an accounting audit and a fraud examination of financial statements and recommendations on what current fraud model offers the best explanation for the actions of the fraudulent retired couple.

Ensemble learning algorithm - research analysis on the management of financial fraud and violation in listed companies

In recent years, despite the strict "zero tolerance" crackdown on the financial fraud and violation behavior of listed companies, the cases of financial fraud, revenue and profit overstatement, and suspected fraud have continued to be exposed. This study first established a financial fraud index system and used the XGBoost algorithm to construct a prediction model for financial fraud and violations of listed companies. The indicators were selected and input into the model. A dataset was obtained for experiments. The XGBoost algorithm was compared with two other algorithms. The receiver operator characteristic (ROC) curves showed that the XGBoost algorithm had the best prediction performance among the three algorithms. It was found that the precision of the XGBoost algorithm was 93.17%, the recall rate was 92.23%, the value was 0.9270, and the area under the curve was 0.90, indicating a better performance than the prediction models based on the Gradient Boosted Decision Tree (GBDT) algorithm and the Logistics algorithm. Considering the data of various evaluation indicators, it is found that the predictive effect of the financial fraud and violation prediction model built by the XGBoost algorithm is the best.

Determinasi Teori Fraud Hexagon dan Karakteristik Komite Audit dalam Mendeteksi Kecurangan Laporan Keuangan

Purpose: This study aims to determine the influence of elements from the fraud hexagon theory and characteristics of audit committees on detecting financial statement fraud. Methodology/approach: The study data uses secondary data sourced from annual reports of manufacturing companies in the basic and chemical industry sebsectors listed on the IDX for 2019-2022 period. Findings: This study resulted in findings that pressure has a positive effect and opportunity has a negative effect on financial statement fraud. Rationalization, capability, arrogance, collusion and two characteristics of audit committee, namely financial expertise and frequency of audit committee meetings has no effect on financial statement fraud. Practical and Theoretical contribution/Originality: These findings contribute to researchers and business managers in increasing understanding of the factors that lead to fraud through the hexagon fraud model an characteristics of audit committees, so as to reduce frequency and amount of losses due to fraud. Novelty this study is to use the independent variable characteristics of audit committee, namely financial expertise and frequency of meetings on detecting financial statement fraud. Research Limitation: The independent variables in this study are only 31.6% which affect the detection of financial statement fraud. While the remaining 68.4% is influenced by other variables outside this research model. In addition, this study also cannot be generalized because only one sub-sector of the company is examined.

An internal fraud model for operational losses in retail banking

AbstractThis article presents a novel dynamic model for internal fraud losses in the retail banking sector, incorporating internal factors such as ethical quality of workers and bank risk controls. The model's parameters are calibrated for each bank in the Operational Riskdata eXchange (ORX) consortium, based only on publicly available exposure indicators. The model generates simulated internal operational losses, exhibiting standard stochastic properties and tail behavior that closely align with actual operational losses. At an aggregate level, the model endeavors to replicate the average frequency and severity of losses observed within the internal fraud—retail banking category. Moreover, we identify macro‐environmental factors that exert influence over the severity and frequency of model‐simulated losses, consistent with findings in the existing literature.

Research on Early Warning Model of Financial Report Fraud in China

At present, financial report fraud is becoming more frequent with the continuous development of the world economy. How to provide early warning before financial report fraud occurs has become more and more important. The purpose of this paper is to set up a logistic regression model, namely an ex-ante warning model, which can provide early warning before financial report fraud occurs, by comparing the governance of financial report fraudulent companies and non-fraudulent ones. First, this paper uses the “fraud triangle” theory as a framework to find the relevant proxy variables for fraud opportunities, fraud pressure, and fraud rationalization. Second, the study uses T-test, Mann-Whitney test and chi-square test to identify statistically significant differences among these proxy variables. Hypotheses were made about the relationship between the coefficient values and the presence of false behavior in the reports. Finally, an ex-ante fraud warning model is set up from the indicators with statistically significant differences, meanwhile the hypotheses regarding the behavior of the indicators and their impact on the model are tested. The overall accuracy of the ex-ante fraud early warning model developed in this paper is 70.9%. How to further debug the model to make the screening of fraudulent companies more accurate is the difficulty and further research direction of the article.

Design of XGBoost prediction model for financial operation fraud of listed companies

Design of XGBoost prediction model for financial operation fraud of listed companies

Baseline comparative analysis and review of election forensics: Application to Ghana's 2012 and 2020 presidential elections

Many allegations have been levelled against the electoral process of many countries across the world by most opposition leaders, especially when they lose a presidential election e.g. Ghana in 2012 and 2020. Therefore, the need to apply election forensic techniques to the certified election results data of valid votes count to statistically verify if some suspected or possible anomalies and irregularities exist in the voting pattern. This paper seeks to provide a comprehensive review of election forensics techniques and make a comparative analysis of Benford's Second-order test of conformity (using the first two digits) and Hartigans' dip test of unimodality to examine the existence of possible anomalies and irregularities in the 2012 and 2020 presidential elections held in Ghana. The findings of the two tests suggest that the electoral process produced possible anomalous data in the 2012 presidential election results (with an overall 16.67% suspected anomalies), whilst possible non-anomalous data was produced in the 2020 presidential election results (with an overall 0% suspected anomaly) of valid votes count. Therefore, the study recommends that for better statistical data analysis on election anomaly detection, Benford's test of conformity and Hartigans' dip test of unimodality should serve as baseline tests (initial screening tools), highlighting areas that may require further investigation or more rigorous analysis and progressively dig deeper into the application of finite mixture fraud models and machine learning techniques. In spite of the promising results Benford's Law, dip test, machine learning algorithms, and network analysis have produced in detecting irregularities in election data, real-world applications remain challenging, particularly when dealing with complex and evolving forms of fraud. Therefore, there is the need for continuous research and innovation to improve the accuracy and effectiveness of these methods and promote transparency and accountability in democratic societies.

Unbalanced credit fraud modeling based on bagging and bayesian optimization

Credit fraud modeling is a crucial area of research that is highly relevant to the credit loan industry. Effective risk management is a key factor in providing quality credit services and directly impacts the profitability and bad debt ratio of leading organizations in this sector. However, when the distribution of credit fraud data is highly unbalanced, it can lead to noise errors caused by information distortion, periodic statistical errors, and model biases during training. This can cause unfair results for the minority class (target class) and increase the risk of overfitting. While traditional data balancing methods can reduce bias in models towards the majority class in relatively unbalanced data, they may not be effective in highly unbalanced scenarios. To address this challenge, this paper proposes using Bagging algorithms such as Random Forest and Bagging to model highly unbalanced credit fraud data. Bayesian optimization is utilized to find hyperparameters and determine the accuracy of the minority class as an optimization function for the model, which is tested with real European credit card fraud data. The results of the proposed packing algorithms are compared with traditional data balancing methods such as Balanced Bagging and Balanced Random Forest. The study found that traditional data balancing methods may not be compatible with excessively unbalanced data, whereas Bagging algorithms show promise as a solution for modeling such data. The proposed method for finding hyperparameters effectively deals with highly unbalanced data. It achieved precision, recall, and F1-score for the minority category of 0.94, 0.81, and 0.87, respectively. The study emphasizes the importance of addressing the challenges associated with unbalanced credit fraud data to improve the accuracy and fairness of credit fraud models

The Fraud Diamond of Public Service in Indonesia

Fraud Diamond is a fraud model that can lead to acts of corruption. Corruption is a dynamic crime that has occurred at almost all levels of government, both central and regional. This condition becomes a serious problem in the public service process. Various causes of corruption problems in the public service process are related to the Fraud Diamond. This study uses the literature review method to analyze the Fraud Diamond Model in cases of public service’s corruption so that it can find the potential for corruption and make appropriate recommendations. The results show that public service corruption is influenced by Fraud Diamond (rationalization, pressure, opportunity, and capability) which can be grouped into four dimensions. Rationalization because of low bureaucratic integrity. Pressure such as collusion and convoluted bureaucratic practices. Opportunities such as power failure in supervisory agencies, while capabilities like authority, power, and total assets. The recommendations are to increase transparency and accountability and improve the monitoring system. In addition, it is also necessary to implement a comprehensive bureaucratic reform.
 Keywords: fraud diamond, corruption, public service, bureaucratic

Analysis of Measurement of Financial Difficulties and Financial Report Fraud (A Case Study at PT Asabri (Persero) 2010-2018)

This study aims to examine the financial health condition and whether there is an indication of fraudulent financial statements or not at PT ASABRI (Social Insurance for the Armed Forces of the Republic) in 2010-2018 by using the Altman Z-Score, Springate S-Score, Grover G-Score, Beneish M-Score, and Dechow F-Score methods. PT ASABRI became the object of the study due to the result of an audit conducted by the BPK (The Audit Board of the Republic of Indonesia) in 2021 that found fraud in financial management and investment funds at PT ASABRI. To measure financial distress, the researchers used the Altman, Springate, and Grover models. While to examine financial statement fraud, the researchers used the Beneish and Dechow models. The results revealed that each model showed varied results. In the financial distress model, the Altman Z-Score could show financial distress 4 times while the Springate S-Score showed financial distress 5 times in 9 years of observation. Moreover, the other models showed different results. The Grover G-Score model did not indicate any financial distress at PT ASABRI. While in the fraud model, the Beneish M-Score could detect indications of fraudulent statements at PT ASABRI 5 times in 8 years of observation. Different conditions occurred in the Dechow F-Score model that did not show fraudulent financial statements at PT ASABRI in 9 years of observation, from 2010 to 2018.