Financial Fraud Detection Research Articles

Explainable fraud detection of financial statement data driven by two-layer knowledge graph

In modern economic activities, financial statement fraud will seriously mislead the economic decisions of investors and regulators, and will lead to huge investment losses even corporate bankruptcies. Although the powerful abilities have been gained by current machine learning methods in financial statement fraud detection problems, the explainability and the ability of extracting fraudulent patterns are still very scarce. In this study, an explainable Financial Statement data Fraud Detection method is proposed by introducing a Two-Layer Knowledge Graph (FSFD-TLKG) and a fraudulent pattern mining strategy on two-layer knowledge graph. Wherein, a two-layer knowledge graph comprises a semantic layer and a syntactic layer. Concretely, the subordination relationships among financial variables are represented in the semantic layer, and their articulation relationships are represented in the syntactic layer. Moreover, an explainable approach is designed to extract financial statement fraudulent patterns for credible fraud assertion. Experimental results show that, FSFD-TLKG can effectively extract explainable financial statement fraudulent patterns and obtain better detection accuracy than almost all traditional machine learning and deep learning methods except an unexplainable method: Extreme Gradient Boosting (XGBoost). Even for XGBoost, the explainable financial statement fraudulent patterns extracted by our method still can further improve its performance. Clearly, FSFD-TLKG gains the optimal practical performance which is much better than existing methods.

The effect of big data competencies and tone at the top on internal auditors fraud detection effectiveness

Financial reports provide information about a company's assets, liabilities, equity, income, expenses and cash flow. This information can be used by various parties such as investors, creditors, government and management to make business decisions and assess company performance. Companies in obtaining good financial reports need to detect fraudulent financial statements first. Financial statement fraud can be detrimental to investors and creditors because it gives a wrong picture of a company's financial performance. This study aims to examine the effect of big data competence and the tone of the top internal auditors on the detection of financial statement fraud, as well as to mediate the effect of big data competence on the detection of financial statement fraud through self-efficacy. This research uses a sample of 183 respondents who are internal auditors in companies in Indonesia. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results of the study show that big data competence has no significant effect on the detection of financial statement fraud, but has a positive and significant effect on self-efficacy. In addition, the internal auditor's tone of the top also has a positive and significant effect on the detection of financial statement fraud. Finally, self-efficacy partially mediates the relationship between big data competence and fraud detection of financial statements. This research provides important implications for practitioners and decision makers in developing internal auditor competence in the field of big data and paying attention to tone of the top as an important factor in detecting fraudulent financial statements. In addition, this research also contributes to strengthening the understanding of the relationship between big data competence, tone of the top, self-efficacy, and fraud detection of financial statements in the Indonesian context.

Enhancing Business Sustainability Through an Intelligent Framework for Unveiling Financial Frauds

The aim of this research is to examine the convergence of intelligent frameworks and financial fraud detection as a strategic approach for strengthening business sustainability in the banking industry. A rigorous preprocessing regimen, which includes data cleansing, normalization, and SMOTE algorithm application for class rebalancing, sets the stage for a refined dataset. Our proposed framework employs Logistic Regression, Decision Trees, and Gradient Boosting models to conduct a multifaceted analysis that accommodates both linear and non-linear relationships within the data. The results are presented through visual representations such as distribution plots and RoC curves that confirm the effectiveness of the framework in detecting potentially fraudulent activities. The comparative analysis offers detailed insights into how versatile the framework is. This study contributes to the broader discourse on intelligent systems in financial fraud detection with practical implications for businesses seeking to enhance their sustainability through advanced risk management strategies.

Abnormal Detection of Financial Fraud in Listed Companies Based on Deep Learning

This study aims to improve the accuracy of detecting financial fraud in listed companies by applying various deep learning algorithms. First, we comprehensively reviewed the current state of research on financial fraud theory and identified 67 recognition features to create a new recognition indicator system. Then, we collected data samples of all A-share listed companies from 2010 to 2022, preprocessed them, and generated a basic dataset. To address the unbalanced dataset, we used the Borderline-SMOTE algorithm. Empirical analysis results show that this algorithm can significantly improve the recognition performance of the model. Finally, we conducted experiments on the new dataset using three types of deep learning algorithms. The results show that the model constructed using the Long Short-Term Memory (LSTM) algorithm has the best prediction performance, with an accuracy rate higher than that of the DCRN, autoencoder, and other models. In addition, the classification effects of all deep learning algorithms are better than basic models and ensemble models. This research provides a powerful tool for the regulatory authorities of listed companies, helping them more effectively monitor and prevent financial fraud. We have three innovations in this study: (1) Development of a comprehensive recognition indicator system with 67 features; (2) Utilization of the Borderline-SMOTE algorithm to handle data imbalance; (3) Demonstration of the superior performance of the LSTM algorithm compared to other deep learning, basic, and ensemble models.

The effectiveness of electronic auditing on improving the financial performance: Evidence from the Jordanian banking industry

The purpose of this research is to find out the effectiveness of E- auditing in improving financial performance at banks operating in Jordan by identifying the association between financial performance, (ROE, Profitability) and E- auditing. To attain the study objectives, A cross-sectional survey method was used to collect data from a sample of employees who work in banks, The preliminary data was collected using electronic structured Likert-Scale questionnaires. The population comprised 25 banks operating in Jordan with 160 employees in the accounting departments. A total of 113 questionnaires were completed and returned electronically from accountants who work in the banks. The SPSS statistical programs were used to analyze the data collected; the results of the analysis found that E- auditing significantly improves the financial performance at banks operating in Jordan. The study concludes that banks should adopt the effective and expanded use of E- audit as it gives independent, truthful, and trusted financial audit information. Banks in Jordan should embrace the effective and expanded use of E- auditing to assure detection of financial fraud, thus sealing routine financial loopholes.

THE ROLE OF AUDIT IN PREVENTING FINANCIAL FRAUDS

An audit is an activity that contributes to the assurance that an organization’s financial statements are accurate and reliable. One of audit objectives is the detection and prevention of financial fraud. Fraudulent activities cause financial losses and damage an organization’s reputation, The subject of the paper is the consideration of the role of auditing in detecting and preventing financial fraud. The conclusion is that the audit helps organizations in detecting and preventing fraud, and helps organizations to take measures to prevent such occurrences in the future.

Forensic accounting and the current state of the infrastructure components of its implementation

Forensic accounting is a specialized accounting field that deals with the detection and Prevention of financial fraud and employee crimes. It involves the use of accounting, auditing and investigative skills to analyze financial transactions and provide evidence in legal proceedings. The importance of criminal accounting has increased significantly in recent years due to the high number of cases of financial fraud around the world , and the application of Forensic accounting depends heavily on the state of the infrastructure components, we aim in this study to study the Forensic accounting infrastructure and find out the reasons that required criminal accounting and the infrastructure that should be available in the Forensic accountant and understanding the difference between the Forensic accountant and the independent auditor and the knowledge of the essence of the elements of Forensic accounting as an element of the registration system and cause of technology has facilitated the way to commit thıs kınd of crimes and that is why the number of financial crimes has increased, it is necessary to know the technology and techniques that Infrastructure for Forensic accounting.

Data Science in Finance: Challenges and Opportunities

Data science has become increasingly popular due to emerging technologies, including generative AI, big data, deep learning, etc. It can provide insights from data that are hard to determine from a human perspective. Data science in finance helps to provide more personal and safer experiences for customers and develop cutting-edge solutions for a company. This paper surveys the challenges and opportunities in applying data science to finance. It provides a state-of-the-art review of financial technologies, algorithmic trading, and fraud detection. Also, the paper identifies two research topics. One is how to use generative AI in algorithmic trading. The other is how to apply it to fraud detection. Last but not least, the paper discusses the challenges posed by generative AI, such as the ethical considerations, potential biases, and data security.

Financial fraud detection: A comparative study of quantum machine learning models

In this research, a comparative study of four Quantum Machine Learning (QML) models was conducted for fraud detection in finance. We proved that the Quantum Support Vector Classifier model achieved the highest performance, with F1 scores of [Formula: see text] for fraud and nonfraud classes. Other models like the Variational Quantum Classifier (VQC), Estimator Quantum Neural Network (QNN), and Sampler QNN demonstrate promising results, propelling the potential of QML classification for financial applications. While they exhibit certain limitations, the insights attained pave the way for future enhancements and optimization strategies. However, challenges exist, including the need for more efficient quantum algorithms and larger and more complex datasets. This paper provides solutions to overcome current limitations and contributes new insights to the field of QML in fraud detection, with important implications for its future development.

Advancing financial fraud detection: Self-attention generative adversarial networks for precise and effective identification

The burgeoning prominence of electronic payment methods has significantly intensified the importance of financial fraud detection, especially concerning credit card fraud. The challenges encompass data imbalance, overfitting, and a limited capacity to realistically emulate the complexity and dynamics of fraudulent activities. This paper introduces self-attention generative adversarial networks (SAGANs) for the detection of credit card fraud. Leveraging self-attention mechanisms, SAGANs can distinguish salient features and patterns within extensive transaction datasets, thereby fostering a more profound understanding and refined identification of credit card fraud. By incorporating generative adversarial networks (GANs), our model is capable of generating data that mirrors actual fraudulent behavior, which consequently enhances and optimizes fraud detection algorithms. Empirical findings from numerous credit card transaction datasets underscore the considerable superiority of our approach in terms of accuracy and recall. Our proposed SAGANs more competently emulate the complexity and diversity inherent in fraudulent behavior, thereby augmenting both the precision and efficacy of fraud detection.

Retracted: Financial Fraud Detection in Healthcare Using Machine Learning and Deep Learning Techniques

Retracted: Financial Fraud Detection in Healthcare Using Machine Learning and Deep Learning Techniques

Deep Fraud Net: A Deep Learning Approach for Cyber Security and Financial Fraud Detection and Classification

Given the growing dependence on digital systems and the escalation of financial fraud occurrences, it is imperative to implement efficient cyber security protocols and fraud detection methodologies. The threat's dynamic nature often challenges conventional methods, necessitating the adoption of more sophisticated strategies. Individuals depend on pre-established regulations or problem-solving processes, which possess constraints in identifying novel and intricate fraudulent trends. Conventional techniques need help handling noise data and the substantial expenses incurred by false positives and true positives. To tackle these obstacles, the present study introduces Deep Fraud Net, a framework that utilizes deep learning to detect and classify instances of financial fraud and cyber security threats. The Deep Fraud Net system model entails the utilization of a deep neural network to acquire intricate patterns and characteristics from extensive datasets through training. The framework integrates noise reduction methods to enhance the precision of fraud detection and improve the quality of input data. The Deep Fraud Net method attains a precision of 98.85%, accuracy of 93.35%, sensitivity of 99.05%, specificity of 93.16%, false positive rate of 7.34%, and true positive rate of 89.58%. The findings suggest that Deep Fraud Net can effectively detect and categorize instances of fraudulent behavior with a reduced occurrence of misclassifications. The method exhibits potential implications for diverse domains that prioritize robust security and fraud detection, including but not limited to banking, e-commerce, and online transactions.

Integrated SVM-FFNN for Fraud Detection in Banking Financial Transactions

Detecting fraud in financial transactions is crucial for guaranteeing the integrity and security of financial systems. This paper presents an integrated approach for detecting fraudulent activities that incorporates Support Vector Machines (SVM) and Feedforward Neural Networks (FFNN). The proposed methodology utilizes the strengths of SVM and FFNN to distinguish between classes and capture complex patterns and relationships, respectively. The SVM model functions as a feature extractor, supplying the FFNN with high-level representations as inputs. Through an exhaustive evaluation utilizing labeled financial transaction data, the integrated SVM-FFNN model shows promise in detecting fraud with increased accuracy and precision. This research contributes to the development of innovative techniques for enhancing financial fraud detection systems.

Rare Category Analysis for Complex Data: A Review

Though the sheer volume of data that is collected is immense, it is the rare categories that are often the most important in many high-impact domains, ranging from financial fraud detection in online transaction networks to emerging trend detection in social networks, from spam image detection on social media platforms to rare disease diagnosis in medical decision support systems. The unique challenges of rare category analysis include (1) the highly skewed class distribution; (2) the non-separable nature of the rare categories from the majority classes; (3) data and task heterogeneity; and (4) the time-evolving property of the input data sources. This survey reviews state-of-the-art techniques used in complex rare category analysis, where the majority classes have a smooth distribution while the minority classes exhibit the compactness property in the feature space or subspace. Rare category analysis aims to identify, characterize, represent, and interpret anomalies that not only show statistical significance but also exhibit interesting patterns (e.g., compactness, high-order structures, showing in a burst). We introduce our study, define the problem setting, and describe the unique challenges of complex rare category analysis. We then present a comprehensive review of recent advances that are designed for this problem setting, from rare category exploration without any label information to rare category exposition that characterizes rare examples with a compact representation, from the representation of rare patterns in a salient embedding space to the interpretation the prediction results and providing relevant clues for the end-users’ interpretation. Finally we discuss potential challenges and shed light on the future directions for complex rare category analysis. 1

Machine Learning In Fraud Detection and Prevention

Digital fraud has become a threat to businesses of all kinds. Under the IOT scenario, financial fraud refers to unauthorized mobile transactions using mobile platforms to fraudulently obtain funds through identity theft or credit card theft It has now become imperative for an organization to pay attention emphasize fraud and people suppression. Digitization has revolutionized the daily tasks we perform at the click of a button. In the real world, financial fraud detection under the IOT scenario requires advanced planning since financial fraud results in financial loss. Thus, we primarily analyzed financial fraud mechanisms using machine learning and deep learning techniques such as complex algorithms for fraud pattern detection. But fraudsters are also becoming smarter by the day and must be constantly monitored to prevent fraud and stay ahead of fraudsters. It is important to look at salient patterns that can help distinguish genuine behavior from fraudulent behavior. Client information such as Geo-location, authentication, session, and device IP address can be captured and monitored. The application of machine learning and artificial intelligence will play an ever-important role in detecting and identifying fraudulent patterns.

FINANCIAL FRAUD DETECTION AND MACHINE LEARNING ALGORITHM (UNSUPERVISED LEARNING): SYSTEMATIC LITERATURE REVIEW

This research aims to assess the usage of unsupervised learning in detecting financial fraud across various financial industries by identifying cognitive constructs, benefits, economic optimization, and challenges associated with fraud detection necessitating innovative approaches for effective detection. This study conducts Systematic Literature Review following PRISMA protocol for article selection of 27 journal articles published between 2010 and 2023, sourced from Scopus database. The analysis discloses that unsupervised learning has been implemented across diverse financial sectors, including online payments, insurance, and prominently in banking, especially for identifying anomalies in credit card transactions. K-Means is the most popular method used in unsupervised learning. Nevertheless, there are ongoing challenges that require solutions to ensure the efficacy of machine learning implementation, encompassing issues like class imbalance and the complexity of fraudulent activities. In theoretical terms, this research provides an understanding of cognitive concepts, benefits and applications, challenges, and practical recommendations in the use of unsupervised learning for financial fraud detection. This is useful for practical implementation, benefiting industry practitioners in selecting appropriate models with datasets that have the potential to enhance detection system accuracy and reduce financial losses due to fraud.

Fraud-Free Green Finance: Using Deep Learning to Preserve the Integrity of Financial Statements for Enhanced Capital Market Sustainability

In the era of green finance and sustainable energy markets, preserving the integrity of financial statements is paramount. This paper introduces an innovative approach to financial statement fraud detection through the application of deep learning techniques. We employ a Temporal Convolutional Network (TCN) to analyze stock market data and identify deceptive practices. Our study meticulously reviews related work, highlighting the critical intersection between financial statement fraud detection and the principles of green energy finance. The Materials and Methods section outlines our data sources, variable selection, and the TCN model's architecture, while the Results and Discussion section presents comprehensive evaluations and comparisons against traditional baselines. Our findings demonstrate the exceptional accuracy and reliability of the TCN model in detecting financial statement fraud, underscoring its potential to instill transparency in energy markets. In conclusion, our research contributes significantly to the promotion of financial integrity and sustainability, offering a powerful tool for investors and stakeholders committed to responsible and ethical investments within the framework of green finance.

Double-weight LDA extracting keywords for financial fraud detection system

Double-weight LDA extracting keywords for financial fraud detection system

Fraud Pentagon: Detection of Financial Statement Fraud in a Firm

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

Fraud Diamond In Financial Reporting Fraud Detection with Audit Committee as A Moderation

The purpose of this study was to determine the effect of fraud diamond using four proxies, namely Financial Target, Ineffective Monitoring, Auditor Change and Board of Directors Change on the detection of financial statement fraud and the influence of the audit committee relationship as a moderating variable. This study used a sample of 16 companies from the agricultural product sub-sector that were listed on the Indonesia Stock Exchange from 2017 to 2021. The data used is secondary data in the form of financial reports and annual reports of the sample companies. Hypothesis testing was carried out using a panel data linear regression model with eviews 12 software. The results of this study indicate that Financial Targets and Auditor Turnover have an effect on fraudulent financial reports, Ineffective Monitoring and replacement of directors have no effect on fraudulent financial statements. The audit committee was unable to moderate the influence of Financial Targets, Ineffective Monitoring, Auditor replacements, Directors replacement on fraudulent financial reporting