Fraud Identification Research Articles

Securing transactions: a hybrid dependable ensemble machine learning model using IHT-LR and grid search

Financial institutions and businesses face an ongoing challenge from fraudulent transactions, prompting the need for effective detection methods. Detecting credit card fraud is crucial for identifying and preventing unauthorized transactions. While credit card fraud incidents are relatively rare, they can result in substantial financial losses, particularly due to the high monetary value associated with fraudulent transactions. Timely detection of fraud enables investigators to take swift actions to mitigate further losses. However, the investigation process is often time-consuming, limiting the number of alerts that can be thoroughly examined each day. Therefore, the primary objective of a fraud detection model is to provide accurate alerts while minimizing false alarms and missed fraud cases. In this paper, we introduce a state-of-the-art hybrid ensemble (ENS) dependable machine learning (ML) model that intelligently combines multiple algorithms with proper weighted optimization using grid search, including decision tree (DT), random forest (RF), K-nearest neighbor (KNN), and multilayer perceptron (MLP), to enhance fraud identification. To address the data imbalance issue, we employ the instant hardness threshold (IHT) technique in conjunction with logistic regression (LR), surpassing conventional approaches. Our experiments are conducted on a publicly available credit card dataset comprising 284,807 transactions. The proposed model achieves impressive accuracy rates of 99.66%, 99.73%, 98.56%, and 99.79%, and a perfect 100% for the DT, RF, KNN, MLP and ENS models, respectively. The hybrid ensemble model outperforms existing works, establishing a new benchmark for detecting fraudulent transactions in high-frequency scenarios. The results highlight the effectiveness and reliability of our approach, demonstrating superior performance metrics and showcasing its exceptional potential for real-world fraud detection applications.

A new fusion neural network model and credit card fraud identification.

Credit card fraud identification is an important issue in risk prevention and control for banks and financial institutions. In order to establish an efficient credit card fraud identification model, this article studied the relevant factors that affect fraud identification. A credit card fraud identification model based on neural networks was constructed, and in-depth discussions and research were conducted. First, the layers of neural networks were deepened to improve the prediction accuracy of the model; second, this paper increase the hidden layer width of the neural network to improve the prediction accuracy of the model. This article proposes a new fusion neural network model by combining deep neural networks and wide neural networks, and applies the model to credit card fraud identification. The characteristic of this model is that the accuracy of prediction and F1 score are relatively high. Finally, use the random gradient descent method to train the model. On the test set, the proposed method has an accuracy of 96.44% and an F1 value of 96.17%, demonstrating good fraud recognition performance. After comparison, the method proposed in this paper is superior to machine learning models, ensemble learning models, and deep learning models.

Blockchain-Based Fraud Detection System for Healthcare Insurance Claims

Healthcare fraud is a huge concern that affects not only the financial viability of insurance companies but also the well-being of patients who may receive compromised care due to fraudulent acts. Addressing this issue demands novel solutions that can detect and prevent fraudulent conduct in healthcare insurance claims. The project intends to establish an automated fraud detection system using blockchain technology, which has advantages such as security, transparency, and data immutability. By leveraging blockchain's decentralized ledger, the system creates a tamper-proof platform for processing healthcare insurance claims, preventing fraudulent alterations and enhancing trust in the integrity of the claims process. Ethereum's blockchain platform and smart contracts play a critical role in ensuring the secure recording of transactions while preventing retroactive alterations. Moreover, an on-chain database is employed to manage relevant claim data, thereby safeguarding its integrity and ensuring accessibility. The decentralized nature of blockchain technology brings additional advantages by eliminating the need for intermediaries, thereby reducing administrative costs and streamlining the claim processing workflow. The adoption of methodologies such as Personal Extreme Programming (PXP) and Design Science Research Methodology (DSRM) fortifies the project's framework. PXP facilitates continuous improvement through incremental and iterative development, while DSRM ensures a structured approach to problem-solving, yielding reliable results. Through rigorous testing and validation, the automated fraud detection system enhances the efficiency and accuracy of fraud identification in healthcare insurance claims. By combining blockchain technology with methodological frameworks, this project offers a promising solution to combat healthcare fraud, safeguarding insurance systems' integrity and ensuring quality care for patients. Future iterations will focus on expanding the system's capabilities and refining its algorithms to counter the evolving fraudulent tactics prevalent in the healthcare industry.

Forensic auditing and the use of artificial intelligence: A bibliometric analysis and systematic review in Scopus between 2000 and 2024

A significant and successful approach to fraud detection includes artificial intelligence in forensic auditing. Forensic auditors can now respond quickly to suspicious circumstances and take preventative action before fraud spreads and causes further damage to the organization, all thanks to artificial intelligence that has enabled early fraud identification. This article analyzes forensic auditing and the use of artificial intelligence through a bibliometric analysis in Scopus and a systematic literature review. The samples were documents selected using Boolean operators with keywords in English (Forensic AND auditing, artificial AND Intelligence), analyzed in Excel and VOSviewer. This research points out that forensic auditing and the use of artificial intelligence have advanced, in the variety of topics covered, the prominence of perpetrators, and the accessibility of crucial data. Therefore, to maintain the quality and integrity of their work, forensic auditors must adapt to technological advances, training in the use of artificial intelligence, and collaborate with other specialists and professionals. Consequently, with its empirical basis, this bibliometric and systematic review critically evaluates the research, to clarify the empirical basis of current trends in this field and lays the groundwork for future research.

Proactive cyber fraud response: a comprehensive framework from detection to mitigation in banks

PurposeThis study addresses the ever-increasing cyber risks confronting the global banking sector, particularly in India, amid rapid technological advancements. The purpose of this study is to de velop an innovative cyber fraud (CF) response system that effectively controls cyber threats, prioritizes fraud, detects early warning signs (EWS) and suggests mitigation measures.Design/methodology/approachThe methodology involves a detailed literature review on fraud identification, assessment methods, prevention techniques and a theoretical model for fraud prevention. Machine learning-based data analysis, using self-organizing maps, is used to assess the severity of CF dynamically and in real-time.FindingsFindings reveal the multifaceted nature of CF, emphasizing the need for tailored control measures and a shift from reactive to proactive mitigation. The study introduces a paradigm shift by viewing each CF as a unique “fraud event,” incorporating EWS as a proactive intervention. This innovative approach distinguishes the study, allowing for the efficient prioritization of CFs.Practical implicationsThe practical implications of such a study lie in its potential to enhance the banking sector’s resilience to cyber threats, safeguarding stability, reputation and overall risk management.Originality/valueThe originality stems from proposing a comprehensive framework that combines machine learning, EWS and a proactive mitigation model, addressing critical gaps in existing cyber security systems.

Application of artificial intelligence Sensors based on random forest algorithm in financial recognition models

Due to the important role of financial information disclosed by enterprises in investor decision-making and economic policy formulation, the corporate market has set the latest standards and requirements for the quality of financial reports. In the current situation, financial recognition work is mainly completed manually, which is inefficient and prone to errors, and is not suitable for large-scale applications. Therefore, we conduct in-depth research on financial recognition models. Firstly, we analyzed the random forest algorithm and used the SMOTE algorithm to expand the sample data. We successfully constructed an optimized random forest algorithm model, which can better improve accuracy and reduce misjudgment rates. Secondly, combined with the current financial data analysis of artificial intelligence technology, the steps for data management and data extraction were designed. Finally, while establishing a financial recognition model, a channel for obtaining internet information and a program for processing this information were also established. After experimental verification, the random forest model has shown better fitting performance in the field of financial fraud identification, providing more reliable support for applications in this field. Therefore, the financial recognition model constructed using artificial intelligence technology based on random forest algorithm has significant value in both theoretical and practical aspects, effectively reducing corporate financial risks.

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.

Bagging Vs. Boosting in Ensemble Machine Learning? An Integrated Application to Fraud Risk Analysis in the Insurance Sector

ABSTRACT Addressing the pressing challenge of insurance fraud, which significantly impacts financial losses and trust within the insurance industry, this study introduces an innovative automated detection system utilizing ensemble machine learning (EML) algorithms. The approach encompasses four strategic phases: 1) Tackling data imbalance through diverse re-sampling methods (Over-sampling, Under-sampling, and Hybrid); 2) Optimizing feature selection (Filtering, Wrapping, and Embedding) to enhance model accuracy; 3) employing binary classification techniques (Bagging and Boosting) for effective fraud identification; and 4) applying explanatory model analysis (Shapley Additive Explanations, Break-down plot, and variable-importance Measure) to evaluate the influence of individual features on model performance. Our comprehensive analysis reveals that while not every re-sampling technique improves model performance, all feature selection methods markedly bolster predictive accuracy. Notably, the combination of the Gradient Boosting Machine (GBM) algorithm with NCR re-sampling and GBMVI feature selection emerges as the most effective configuration, offering superior fraud detection capabilities. This study not only advances the theoretical framework for combating insurance fraud through AI but also provides a practical blueprint for insurance companies aiming to incorporate advanced AI strategies into their fraud detection arsenals, thereby mitigating financial risks and fostering trust systems.

Digital twin for credit card fraud detection: opportunities, challenges, and fraud detection advancements

Credit cards are widely used for payments due to their convenience and broad acceptance. Their popularity comes with the critical challenge of safeguarding personal and payment information from fraud and unauthorized access. Robust security measures are crucial to maintaining trust and confidence among users. In response to this pressing issue, this paper focuses on credit card fraud detection, its challenges, and innovative solutions using digital twins and blockchain. This research highlights the importance of understanding and reducing credit card fraud to protect consumers and financial institutions. The study provides a detailed overview of credit card fraud analysis and categorizes its different types to clarify the threat landscape. It introduces a new digital twin approach to improve fraud detection. Digital twins are virtual replicas of physical systems that show promise for enhancing anomaly detection and behavioral analysis for more precise and timely fraud identification. In addition, the paper examines blockchain-enabled federated learning (BFL) as a decentralized method that uses blockchain’s security features to improve collaborative learning. By merging digital twins with federated learning (FL), the study presents a dynamic strategy for identifying known and emerging fraud patterns effectively. These advanced technologies represent a significant step forward in combating credit card fraud. Overall, the research not only focuses on creating more robust fraud detection systems but also emphasizes the importance of continuous innovation and adaptation to enhance financial security measures.

Data mining algorithm in the identification of accounting fraud by smart city information technology

The identification of accounting fraud is an important measure to safeguard the interests of stakeholders and ensure the long-term development of the company. The current traditional methods for identifying accounting fraud rely on manual review and judgment, lacking objectivity and accuracy. In order to improve the accuracy of accounting fraud identification, improve identification efficiency and objectivity, this article combines smart city information technology to conduct in-depth research on data mining algorithms for accounting fraud identification. This article first provides a brief overview of smart cities and information technology, then introduces the basic theory of accounting fraud identification, and finally implements accounting fraud identification through k-means clustering mining algorithm. The data is divided into k clusters, and abnormal clusters are identified by checking the characteristics and attributes of each cluster. Compared with traditional rule-based and pattern based methods, this approach can more flexibly adapt to different types and forms of fraud, and can discover unknown patterns of fraud. In the experiment, this article used electronic data collection, analysis, and retrieval systems on the websites of the Shanghai Stock Exchange and Shenzhen Stock Exchange to collect 641 annual reports and financial characteristics from 62 listed companies that engaged in financial statement fraud and 84 companies that were not reported to have financial statement fraud from 2012 to 2021 as test samples. The results were tested and analyzed from several aspects, including the number of misjudgments, misjudgment rate, and ROC curve. The final test results show that compared to traditional accounting fraud identification methods, the comprehensive misjudgment rate of data mining algorithms based on smart cities has decreased by 3 %. The conclusion indicates that data mining algorithms used in smart city information technology to identify accounting fraud can help improve the accuracy of accounting fraud, improve audit objectivity and effectiveness.

REVIEWING THE ROLE OF BIG DATA ANALYTICS IN FINANCIAL FRAUD DETECTION

Financial institutions grapple with the escalating nature of fraudulent activities, necessitating innovative and timely detection methods. The review underscores the transformative potential of Big Data Analytics, emphasizing its pivotal role in the ongoing fight against fraud. Delving into the specifics, the paper explores diverse data sources, such as transaction and user behavior data, alongside external data from sources like social media, employing machine learning algorithms and predictive modeling for anomaly detection and risk assessment. Real-time processing emerges as a critical component for swift and effective fraud identification. Critically addressing implementation challenges, including data quality assurance and privacy concerns, the paper showcases case studies of successful Big Data Analytics implementations, highlighting their positive impacts on fraud prevention and financial security. Looking ahead, the review anticipates the role of emerging technologies like blockchain and artificial intelligence in enhancing fraud prevention strategies, emphasizing integration with cybersecurity for robust defense against sophisticated attacks. The paper concludes with recommendations for financial institutions, advocating collaborative efforts and information sharing within the industry. In summary, the review underscores the transformative contributions of Big Data Analytics to financial fraud detection, shaping the future of fraud prevention strategies and fortifying the resilience of the global financial ecosystem..
 Keywords: Big, Data, Analytics, Finance, Fraud, Detection.

Enhancing DeepFake Detection: Leveraging Mesonet for Video Fraud Identification

Enhancing DeepFake Detection: Leveraging Mesonet for Video Fraud Identification

Real-Time Online Banking Fraud Detection Model by Unsupervised Learning Fusion

Digital trades and payments are becoming increasingly popular, as they typically entail monetary transactions. This not only makes electronic transactions more convenient for the end customer, but it also raises the likelihood of fraud. An adequate fraud detection system with a cutting-edge model is critical to minimizing fraud costs. Identifying fraud at the ideal time entails establishing and setting up ubiquitous systems to consume and analyze massive amounts of streaming data. Recent advances in data analytics methods and introducing open-source technology for big data storage and processing opened new options for detecting fraud. This study aims to tackle this critical issue by providing a newly real-time e-transaction fraud detection schema that consolidates the advantages of both unsupervised learners, including autoencoder and extended isolation forests, with cutting-edge big data gadgets such as Spark streaming and sparkling water. It addresses the shortage of non-fraudulent instances and handles the excessive dimension of the set of features. On two real-world transactional datasets, we assess our suggested technique. Compared with other current fraud identification systems, our methodology delivers an elevated accuracy yield of 99%. Furthermore, it outperforms state-of-the-art approaches in reliably identifying fraudulent samples. Doi: 10.28991/HIJ-2024-05-01-014 Full Text: PDF

Financial Fraud Identification Model of Listed Companies based on Time-Series Information

The aim of this research is to establish a high-precision financial fraud identification model for listed companies, which is mainly based on the financial indicators of time series. Support vector machine and K-means clustering algorithm are especially used in the research process. Firstly, local linear embedding is used to reduce the dimensionality of the selected financial indicators to extract the low-dimensional characteristics. Then the samples are classified into financial fraud and non-fraud by support vector machine, and the recognition model is constructed. At the same time, the research also uses K-means clustering algorithm to analyze the pattern of financial fraud. The experiment of dimensionality reduction proves that the model has a high effect on the processing of financial data, and the error between the data after dimensionality reduction and the original data is small. In addition, the clustering effect of the model also shows a clear pattern of fraud. In practical application, the accuracy rate of this model is as high as 94.89%, showing high accuracy and recall rate, and its F1 value is 87.08%, showing its feasibility and effectiveness in practice. The results highly prove that the performance of the financial fraud identification model proposed in this study is excellent, and it has a wide application prospect in the future.

Financial Anti-Fraud Based on Dual-Channel Graph Attention Network

This article addresses the pervasive issue of fraud in financial transactions by introducing the Graph Attention Network (GAN) into graph neural networks. The article integrates Node Attention Networks and Semantic Attention Networks to construct a Dual-Head Attention Network module, enabling a comprehensive analysis of complex relationships in user transaction data. This approach adeptly handles non-linear features and intricate data interaction relationships. The article incorporates a Gradient-Boosting Decision Tree (GBDT) to enhance fraud identification to create the GBDT–Dual-channel Graph Attention Network (GBDT-DGAN). In a bid to ensure user privacy, this article introduces blockchain technology, culminating in the development of a financial anti-fraud model that fuses blockchain with the GBDT-DGAN algorithm. Experimental verification demonstrates the model’s accuracy, reaching 93.82%, a notable improvement of at least 5.76% compared to baseline algorithms such as Convolutional Neural Networks. The recall and F1 values stand at 89.5% and 81.66%, respectively. Additionally, the model exhibits superior network data transmission security, maintaining a packet loss rate below 7%. Consequently, the proposed model significantly outperforms traditional approaches in financial fraud detection accuracy and ensures excellent network data transmission security, offering an efficient and secure solution for fraud detection in the financial domain.

Research on Detecting Auto Insurance Fraud

With the expansion of the insurance sector has come an increasing concern about insurance fraud. This issue has grown in prominence over time, necessitating the implementation of appropriate countermeasures. In response, this work conducts a thorough analysis of the available literature, with an emphasis on the incorporation of machine learning approaches for detecting fraudulent actions in the field of automobile insurance. By delving into the intricacies of the vehicle insurance fraud landscape, the research investigates the strategic deployment of expert systems and machine learning models to enhance fraud identification processes. Drawing from the wealth of high-quality auto insurance data spanning the last five years, this work explores the implementation of diverse machine learning algorithms, including Logistic Regression, Decision Tree, and Discriminative Analysis, in order to construct comprehensive predictive models. After the modeling was completed, comprehensive testing and analysis were conducted, and the results showed that the logistic regression model had the highest accuracy among these three models. Finally, the future research direction of automobile insurance fraud detection technology is discussed, and how to reasonably prevent automobile insurance fraud is proposed.

Teknologi Digital Dan Transformasi Internal Audit Terhadap Perlakuan Laporan Keuangan : Studi Literatur

This article discusses the impact of digital technology on the transformation of internal audit and its implications for the treatment of financial reports. As technology advances, companies are adopting digital solutions to improve the efficiency and effectiveness of their internal audit processes. This transformation not only includes the use of new tools and technologies, but also changes the paradigm and methodology of internal audit work. Digital technology provides greater capabilities in collecting, analyzing and understanding data in real-time. Internal audit can leverage advanced analytics technology, artificial intelligence, and natural language processing to identify risks, detect anomalies, and provide deeper insights into a company's finances. With the adoption of digital technology, internal audit transformation can increase audit accuracy and thoroughness, reduce the risk of human error, and enable fraud identification more effectively. In addition, technology also facilitates the implementation of more proactive and adaptive risk-based audit practices, allowing internal audit to focus on the most critical areas and provide significant added value. However, this transformation also brings new challenges, including the need to develop new skills among auditors, manage information security risks, and ensure compliance with regulations related to data privacy and security. This article details effective implementation strategies, as well as providing views on how organizations can optimize the potential of digital technology to improve the quality of financial reporting treatment. Thus, this article aims to provide in-depth insight into the role of digital technology in the transformation of internal audit and its impact on the treatment of financial statements, identify the associated benefits and challenges, and provide guidance for companies wishing to adopt such technology effectively.

A Thorough Review of Credit Card Fraud Identification Techniques: Current Trends and Future Directions

A Thorough Review of Credit Card Fraud Identification Techniques: Current Trends and Future Directions

A Research on Identification and Prevention of Corporate Financial Fraud Behaviour in the Context of High-Performance Commitment: Taking Aerospace Communications’ Merger and Acquisition of HiPAD Intelligent as an Example

In recent years, there are often high premium acquisitions based on high-performance commitment in corporate mergers and acquisitions. The performance commitment is intended to alleviate the information asymmetry between the two parties of mergers and acquisitions, but the pressure brought by the high-performance commitment often triggers the company’s financial fraud behaviour. This paper will use the case study method to select the classic case of Aerospace Communications merger and acquisition of the HiPAD, in-depth analysis of the merged party HiPAD of the financial counterfeiting means, and corresponding to the identification of signs of financial fraud research. This paper analyses the abnormal signs in the public disclosure information of HiPAD from the unique perspectives of financial information identification and non-financial information identification, parent company and subsidiaries, providing a reference direction for the financial fraud identification path. Finally, it puts forward a universal proposal for the prevention of corporate financial fraud in the context of the pressure of the performance commitment, which provides a feasible method for the similar mergers and acquisitions and high-performance commitment cases.

Research on medical insurance fraud identification method based on multi-source datasets

Research on medical insurance fraud identification method based on multi-source datasets