Improving Financial Statement Fraud Detection: A Large Language Model Processing Approach

The study examines conceptually the detection of financial statements fraud using fraud pentagon. The study discusses the relationship between fraud pentagon and financial statement fraud. The methodology adopted in this study is library research whereby relevant and extant literature related to the elements in fraud pentagon were reviewed in relation to financial statements fraud. In the search for literature, it was observed that the various elements of fraud pentagon such as pressure (financial targets, financial stability, institutional ownership and external pressure), opportunity (ineffective monitoring and nature of industry), rationalization (auditor switching and change of director), competence (the changes in the company directorship and the proportion of independent directors in the board) and arrogance (frequent number of CEO's picture and CEO duality) significantly affect financial statements fraud. This study recommends that fraud pentagon should be given the statutory support in order to address financial statements fraud.
 
 Received: 27 August 2023 / Accepted: 19 October 2023 / Published: 5 November 2023

The purpose of this study is to respond to research gaps and inconsistencies in research findings by empirically testing how financial targets and external pressures affect the detection of financial statement fraud. The study utilizes a descriptive research design with a quantitative approach. A method of purposive sampling was used, focusing on companies listed on the Jakarta Islamic Index (JII) from 2019 to 2023. Statistical analysis was done using multiple regression analysis. The results show that financial targets have a significant effect on the detection of financial statement fraud, while external pressures do not have a significant effect. However, both financial targets and external pressures have a simultaneous effect on the detection of financial statement fraud. The practical implications, financial targets, and external pressures can have an impact on the detection of fraud within the organization. Under these conditions, fraud may be more difficult to detect because the organization's or individual's focus is more on achieving targets or meeting external expectations. Therefore, companies need to have a strong internal control system and independent audits to help identify and manage potential risks of financial statement fraud.

The objective of this research is to analysis the effect of auditor characteristics that represented by professional scepticism and auditor experiences toward financial statement fraud detection through artificial intelligence in data analytic. This study builds upon previous research where respondents were auditors who had used big data analytics and incorporate artificial intelligence, specifically big data analytic as mediator between auditor characteristics and the detection of financial statement fraud. Quantitative research was used in this research by obtaining primary data through 66 respondents from auditor at several public accounting firms in the Jakarta area. Our findings reveal that auditor experience has direct influence effect on financial statement fraud detection while professional scepticism has no effect. But surprisingly, the role of artificial intelligence in data analytics is only able to mediate professional scepticism on financial statement fraud detection, while this does not apply to auditor experience. This study provides empirical evidence that the role of artificial intelligence, namely big data analytic, can act as a catalyst that transforms professional scepticism from mere doubt to evidence-based action, by enhancing the auditor’s ability to identify data patterns and anomalies, reducing cognitive bias and increasing objectivity.

This research aims to examine the fraud hexagon theory in the detection of fraudulent financial reporting that occurs in a company. According to the hexagon fraud theory, fraud occurs due to 6 factors, namely pressure/stimulus, opportunity, capability, ego/arrogance, rationalization, and collusion. These 6 factors are proxied with 10 proxies, namely: financial stability, financial targets, external pressure, change in director, change in auditor, ineffective monitoring, industry nature, number of CEO photos, political connections, and collusion. The population in this research are companies listed in the LQ 45 index period I February-July 2017-2021. The sample in this study is 88 samples. The data analysis approach used is statistic descriptive, classic assumption test, and multiple regression analysis using SPSS 25. The results of this study reveal that the nature of industry and external pressures have a negative effect on the detection of financial statement fraud, and change in auditor positively effect on the detection of financial statements fraud. While the variables of financial stability, financial targets, political connections, change in directors, number of CEO photos, ineffective monitoring, and collusion have no effect in detecting financial statements fraud. This result can be used for companies management as their references about what makes fraudulent financial statements occurs, so they can make preventive action for their companies

This study aims to analyze the influence of the fraud Pentagon elements on detecting financial statement fraud in BUMN listed on the Indonesia Stock Exchange (IDX) for 2020-2022. The study population was all state-owned enterprises BUMN listed on the Indonesia Stock Exchange. The sample was selected using a purposive sampling method, resulting in 18 companies. Data analysis used multiple regression analysis. The results showed that financial stability and personal financial needs were proven to have no significant effect on financial statement fraud. Financial targets, changes in director and auditor, and the number of CEO photos in the annual report showed a significant positive effect on financial statement fraud. Meanwhile, ineffective monitoring and dualism positions showed a significant negative effect. These findings indicate that several elements in the fraud pentagon framework significantly affect financial statement fraud in BUMN in Indonesia, with the model explaining 62.6% of the variance in fraud detection. ABSTRAK Penelitian ini bertujuan menganalisis pengaruh elemen fraud pentagon terhadap pendeteksian kecurangan laporan keuangan pada BUMN yang terdaftar di Bursa Efek Indonesia (BEI) periode 2020-2022. Populasi penelitian adalah seluruh badan usaha milik negara BUMN yang terdaftar pada Bursa Efek Indonesia. Sampel dipilih menggunakan purposive sampling method sehingga sejumlah 18 perusahaan. Analisis data menggunakan analisis regresi berganda. Hasil penelitian menunjukkan financial stability dan personal financial needs terbukti tidak berpengaruh signifikan terhadap kecurangan laporan keuangan. Financial targets, change in director, change in auditor, dan jumlah foto CEO dalam laporan tahunan menunjukkan pengaruh positif signifikan terhadap kecurangan laporan keuangan. Sementara itu, ineffective monitoring dan dualism position menunjukkan pengaruh negatif signifikan. Temuan ini mengindikasikan bahwa beberapa elemen dalam kerangka fraud pentagon secara signifikan mempengaruhi kecurangan laporan keuangan pada BUMN di Indonesia, dengan model menjelaskan 62,6% varians dalam pendeteksian fraud.

Financial statement fraud has been a serious concern for many investors and other stakeholders, and this motivated many researchers in developing traditional regression analysis in order to detect financial statement fraud. Intelligent financial statements fraud detection systems were continuously developed to uncover any potential for fraud signals which could assist the stakeholders in taking their decisions. This study reviews and identifies the gaps in the previous studies that tested financial statement fraud detection using data mining techniques. The study expands the knowledge of this research topic among researchers, forensic accountants, auditors and regulators and provides organizations with useful information regarding the various types of financial fraud and data mining techniques available.

Fraud detection: Intentionality and deception in cognition

Detecting evolutionary financial statement fraud

The detection and prevention of financial statement fraud is a critical concern in maintaining the credibility and reliability of financial reporting. In response to this ongoing challenge, researchers are exploring innovative solutions that leverage artificial intelligence (AI) technology. This study investigates the potential application of AI techniques, such as machine learning algorithms, natural language processing, and data mining, in enhancing forensic accounting practices for detecting and preventing financial statement fraud. Furthermore, the research examines the inherent challenges and limitations involved in implementing AI systems within forensic accounting. The findings of this research contribute valuable insights to organizations, regulatory bodies, and forensic professionals, assisting them in their efforts to combat financial fraud and promote the accuracy of financial reporting systems.

The purpose of this study aims to detect financial statement fraud in property and real estate companies listed on the Indonesia Stock Exchange from 2015 to 2020. The type of data in this study is secondary data. The sampling method used is purposive sampling with a total sample of 43 companies. The data analysis technique uses the Beneish Model which consists of the Beneish Ratio Index and the Beneish M-Score. The results showed that there were 21 companies indicated as manipulators in 2016, 26 companies indicated as manipulators in 2017, 21 companies indicated as manipulators in 2018, 17 companies indicated as manipulators in 2019, and 14 companies indicated as manipulators in 2020. There were 22 companies indicated as non-manipulators in 2016, 17 companies indicated as non-manipulators in 2017, 22 companies indicated as non-manipulators in 2018, 26 companies indicated as non-manipulators in 2019, and 29 companies indicated as non-manipulators in 2020. There are no companies indicated as gray companies. The results also show that property and real estate companies listed on the Indonesia Stock Exchange experience earning manipulation, and the growth rate of financial statement fraud has decreased from 2016 to 2020. Keywords: financial statement fraud, beneish ratio index, beneish m-score, earning manipulation.

Financial statement fraud has been a difficult problem for both the public and government regulators, so various data mining methods have been used for financial statement fraud detection to provide decision support for stakeholders. The purpose of this study is to propose an optimized financial fraud detection model combining feature selection and machine learning classification. The study indicated that random forest outperformed the other four methods. As to two feature selection methods, Xgboost performed better. And according to our research, 2 or 5 variables are more acceptable for models in this paper.

Financial statement fraud has been one of the biggest challenges in the modern business world. Financial accounting fraud detection (FAFD) has become an emerging topic of great importance for academic, research and industries. In this paper, the effectiveness of Data Mining (DM) classification techniques in detecting firms that issue fraudulent financial statements (FFS) and deals with the identification of factors associated to FFS are explored. Our study investigates the usefulness of Data Mining techniques including Decision Trees, Neural Networks and Bayesian Belief Networks in the identification of fraudulent financial statements. At last, we compare the three models in terms of their performances.

This study aims to examine the effect of Fraud Hexagon on Financial Statement Fraud in mining companies listed on the Indonesia Stock Exchange (IDX). Fraud Hexagon consists of pressure, opportunity, rationalization, capability, arrogance, and collusion, while Financial Statement Fraud is measured using the Beneish M-Score. This study uses a quantitative approach with logistic regression analysis and purposive sampling techniques based on the companies' financial statements and annual reports for the observation period. The results show that simultaneously, the Fraud Hexagon influences Financial Statement Fraud, but partially, not all variables have a significant influence. These findings are expected to contribute to the detection and prevention of financial statement fraud in the mining secto. Keyword: Fraud Hexagon, Financial Statement Fraud, Beneish M-Score, Pertambangan.

State of the art in financial statement fraud detection: A systematic review

Financial statement fraud is a discrepancy between the application of accounting principles and the preparation of financial statements with the aim of deceiving and deceiving users of financial statements. The approach taken to identify the factors that influence financial statement fraud, one of which is the fraud hexagon model. There are six factors in the fraud hexagon, namely Stimulus which will be represented by financial targets, Ability will be represented by a change of director, Collusion will be represented by political connections, Opportunity will be represented by the quality of external auditors, Rationalization will be represented by audit opinion and Ego is represented by many photos CEO. This study aims to analyze the effect of the fraud hexagon on fraudulent financial statements in infrastructure, utility and transportation sector companies listed on the Indonesia Stock Exchange in 2015-2019. Detection of the possibility of fraudulent financial statements is measured by the F-Score model. This study uses a purposive sampling technique with a sample of 25 companies and 125 data on annual reports and financial statements. The results showed that financial targets, the quality of external auditors, and the number of CEO photos had no effect on financial statement fraud, while the change of directors had a negative and significant effect on financial statement fraud, political connections had a positive and significant effect on financial statement fraud. Audit opinion has a negative and significant effect on financial statement fraud.