Fraudulent Activities Research Articles

Comparative Study of Machine Learning Techniques for Insurance Fraud Detection

Insurance fraud has been a constant presence in the realm of insurance. However, as strategies and methods for committing insurance fraud have evolved, the frequency and volume of such fraudulent activities have also increased. An example of this is vehicle insurance fraud, which involves collaborating to fabricate false or exaggerated claims related to property damage or personal injuries resulting from an accident. Machine learning techniques seems to be more beneficial and great way to address the fraud in the insurance industry. This paper comprehensively examines existing research through a systematic literature review. This review aims to identify previously attempted approaches and evaluate which machine learning algorithm is best suited for this specific problem. This paper proposes a methodology for identifying fraudulent insurance claims. This approach can significantly improve efficiency and cost savings for insurance companies in handling such cases. The most popular traditional machine learning algorithms used to identify insurance fraud in the auto industry were found to be support vector machine, logistic regression, and random forest.

A jogászkodás csapdájában

The author provides an overview of court cases concerning a series of fraudulent activities in Hungary, where an abuse of legal procedures led to a severely lengthy set of trials.

Using Machine Learning Models to Detect the Increasing Threats of Financial Fraud in the Cyberspace

In the dynamic landscape of the financial sector, the escalating menace of financial fraud presents pervasive implications for businesses and consumers alike. Particularly, detecting credit card fraud in real- time transactions has become a pivotal concern within the financial industry. This abstract delves into the critical role of data mining in addressing the complexities of credit card fraud detection, shedding light on the multifaceted challenges that confront this domain. The realm of financial business is increasingly besieged by the spectre of financial fraud, necessitating robust measures to combat its detrimental effects. As the sophistication and prevalence of fraudulent activities continue to evolve, the imperative of deploying effective strategies for fraud detection becomes more pronounced. Applying data mining techniques in this context is paramount in identifying and mitigating credit card fraud. Leveraging advanced data mining methodologies is essential for scrutinising live transactions and discerning anomalous patterns indicative of fraudulent behaviour. Credit card fraud detection poses formidable challenges, primarily attributable to two compelling factors. Firstly, the inherent dynamism of normal and fraudulent behavioural profiles engenders a perpetual need for adaptive and responsive detection mechanisms. Secondly, the highly imbalanced nature of credit card fraud data sets further complicates accurately identifying fraudulent activities, necessitating nuanced approaches to discern anomalies amidst voluminous transactional data effectively. In light of the foregoing, this abstract underscore the criticality of data mining in addressing the intricate landscape of credit card fraud detection, emphasising the need for agile and sophisticated methodologies to navigate the evolving nature of fraudulent behaviours and the skewed distribution of fraud-related data sets. By comprehensively elucidating these challenges, this abstract provides a foundational understanding of the nuanced complexities inherent in combatting financial fraud through the lens of data mining.

Integrating a machine learning-driven fraud detection system based on a risk management framework

This article explores the application of machine learning techniques, specifically focusing on ensemble methods like Random Forests, for detecting fraudulent activities in digital financial transactions. Highlighting the evolution from traditional statistical approaches to modern machine learning models, it underscores the effectiveness of Random Forests in handling the inherent challenges of imbalanced datasets typical in fraud detection scenarios. Using a Kaggle dataset of credit card transactions, the study optimizes Random Forest parameters through rigorous parameter tuning, achieving significant improvements in model performance metrics such as Area Under the Curve (AUC). The findings underscore the critical role of machine learning in enhancing fraud detection capabilities, emphasizing the ongoing evolution and future potential of these methodologies in financial risk management.

MEASURES TO COUNTERACT MODERN METHODS OF FRAUD AGAINST THE POPULATION

In the context of developing technologies, the risk of losing money through deception or abuse of trust increases significantly. In the modern world, scammers are finding more and more sophisticated ways to illegally seize other people's money. The article examines modern methods of committing fraud, analyzes measures to combat these crimes, puts forward proposals for updating and improving methods and measures aimed at countering modern methods of fraud. Methodology. Analysis and synthesis were chosen and used as theoretical methods of scientific research. Purpose. The work is aimed at studying and analyzing measures and methods of protection against fraudsters, identifying possible ways to legally solve problems. Results. Proposals for updating and improving methods and measures aimed at countering modern methods of fraud against the population. One of the most important measures to counteract the commission of fraudulent actions against the population, as we believe, is a whole range of various measures aimed at preventing such criminal actions. Preventive actions can be divided into several types: actions aimed at the population itself, work with citizens and actions that constitute technical protection. For example, special attention should be paid to informing and teaching financial literacy to the younger generation (students, schoolchildren), since in the near future this particular group of the population will become the most solvent.

Integrating machine learning and blockchain: Conceptual frameworks for real-time fraud detection and prevention

Integrating machine learning (ML) and blockchain technologies presents a groundbreaking approach to real-time fraud detection and prevention, addressing the growing complexity and sophistication of financial fraud schemes. This integration leverages the strengths of both technologies: the predictive power of ML algorithms and the transparency, security, and immutability of blockchain. Machine learning algorithms are proficient at analyzing large datasets, detecting patterns, and identifying anomalies that signify potential fraud. By employing supervised learning models such as logistic regression, decision trees, and neural networks, financial institutions can classify transactions and predict fraudulent activities with high accuracy. Unsupervised learning techniques, such as clustering and anomaly detection, are instrumental in discovering new fraud patterns without the need for labeled data, thus enhancing the detection of novel fraudulent behaviors. Blockchain technology, on the other hand, provides a decentralized and tamper-proof ledger that ensures data integrity and traceability. Transactions recorded on a blockchain are immutable and transparent, allowing for real-time monitoring and auditing. Smart contracts, self-executing contracts with the terms directly written into code, can be programmed to trigger alerts or actions when suspicious transactions are detected, further automating the fraud prevention process. The conceptual framework for integrating ML and blockchain involves several key components. First, data from financial transactions are continuously collected and stored on a blockchain, ensuring transparency and security. ML algorithms analyze this data in real time, identifying suspicious patterns and flagging potential fraud. When a potential fraud is detected, a smart contract is executed, which can instantly block the transaction, alert the relevant authorities, or initiate further verification processes. This integrated approach addresses several challenges in traditional fraud detection systems. The decentralized nature of blockchain eliminates single points of failure and reduces the risk of data tampering. The transparency of blockchain enhances the trustworthiness of the detection process, while the predictive capabilities of ML provide high accuracy and adaptability to new fraud tactics. Additionally, the real-time processing capabilities of both technologies ensure prompt detection and prevention of fraudulent activities. In conclusion, the integration of machine learning and blockchain offers a robust framework for real-time fraud detection and prevention. This synergy not only enhances the security and reliability of financial transactions but also paves the way for more advanced and automated compliance systems, ultimately strengthening the financial ecosystem against fraudulent threats.

Fraud Prevention Strategies and Financial Stability of Insurance Companies in Kenya

Insurance companies play a crucial role in reducing risks and uncertainties, making them a vital part of the economy. However, they are vulnerable to various types of fraudulent activities, such as false claims, premium misuse, and financial record manipulation. While efforts have been made to address these challenges, more investigation is needed to determine the effectiveness of fraud prevention methods in maintaining financial stability. This research project aims to bridge this gap by exploring the empirical connections between internal control frameworks, transaction monitoring processes, financial audit procedures, and the overall financial stability of insurance companies in the Kenyan market. The goal is to understand how these techniques can deter fraud, promote regulatory compliance, and strengthen the organizational structures in the industry. Additionally, the study examines established theories like agency theory, risk management theory, institutional theory, and game theory to clarify the theoretical basis supporting these tactics and their impact on the financial stability and longevity of insurance companies. The conclusion of the study offers valuable insights regarding the correlation between fraud prevention measures and financial stability within the insurance sector.

Promoting financial inclusion for SMEs: Leveraging AI and data analytics in the banking sector

Financial inclusion is crucial for the growth and sustainability of small and medium-sized enterprises (SMEs), which are significant contributors to economic development and job creation. However, traditional banking models often fall short in serving the unique needs of SMEs due to perceived high risks and insufficient credit history. Leveraging artificial intelligence (AI) and data analytics, the banking sector can transform its approach to financial inclusion, offering tailored financial products and services that cater specifically to SMEs. AI and data analytics enable banks to analyze vast amounts of data from diverse sources, including transactional histories, social media activity, and market trends. This holistic view of an SME's financial health and business potential allows for more accurate risk assessment and credit scoring. Machine learning algorithms can identify patterns and predict creditworthiness, enabling banks to extend credit to SMEs that may have been overlooked by traditional models. Additionally, AI-driven insights facilitate the development of customized financial products, such as flexible loan terms and dynamic interest rates, that align with the cash flow cycles and operational realities of SMEs. Automated processes and AI-powered chatbots enhance customer service, providing SMEs with timely support and financial advice, thereby improving their banking experience. Data analytics also play a critical role in fraud detection and prevention, ensuring the security of transactions and building trust among SME clients. By continuously monitoring and analyzing transaction data, banks can quickly identify and mitigate fraudulent activities, protecting SMEs from financial losses. Moreover, AI and data analytics support the creation of financial literacy programs tailored to the specific needs of SMEs, empowering business owners with the knowledge and tools to make informed financial decisions. This educational aspect is vital in fostering a sustainable financial ecosystem for SMEs. In conclusion, the integration of AI and data analytics in the banking sector holds significant promise for promoting financial inclusion among SMEs. By providing more accessible, customized, and secure financial services, banks can support the growth and success of SMEs, ultimately contributing to broader economic development and financial stability.

Deep learning in high-frequency trading: Conceptual challenges and solutions for real-time fraud detection

High-frequency trading (HFT) has transformed financial markets by enabling rapid execution of trades, exploiting market inefficiencies, and optimizing trading strategies. However, this speed and complexity also present significant challenges for real-time fraud detection. Deep learning, a subset of machine learning, offers promising solutions to these challenges through its ability to analyze large volumes of data and uncover intricate patterns. This review explores the conceptual challenges and solutions associated with deploying deep learning for fraud detection in HFT environments. One of the primary challenges in implementing deep learning for HFT fraud detection is the sheer volume and velocity of data. HFT systems generate vast amounts of transactional data in milliseconds, necessitating highly efficient and scalable deep learning models. Convolutional neural networks (CNNs) and recurrent neural networks (RNNs) are particularly suited for this task due to their ability to process and analyze sequential data efficiently. However, these models require substantial computational resources and sophisticated infrastructure to operate in real time. Another significant challenge is the need for high accuracy and low latency in fraud detection. False positives can lead to unnecessary interventions, while false negatives can result in undetected fraudulent activities. Deep learning models must be fine-tuned to balance these risks, employing techniques such as hyperparameter optimization and ensemble learning to enhance their predictive capabilities. Additionally, integrating real-time anomaly detection methods can help identify suspicious activities promptly, reducing the window of opportunity for fraudsters. Data quality and integrity also pose substantial challenges. HFT environments are susceptible to noise and outliers, which can distort model predictions. Ensuring high-quality data through rigorous preprocessing and anomaly filtering is crucial for the accuracy of deep learning models. Techniques such as data augmentation and normalization can further improve model robustness. To address these challenges, a hybrid approach combining deep learning with traditional statistical methods and rule-based systems can be effective. This approach leverages the strengths of each method, providing a comprehensive fraud detection framework that is both accurate and responsive. Additionally, ongoing model retraining and adaptation to evolving fraud patterns are essential to maintain the effectiveness of the system. In conclusion, while deep learning presents significant opportunities for enhancing real-time fraud detection in high-frequency trading, it also requires addressing challenges related to data volume, computational demands, accuracy, and data quality. By employing a hybrid approach and continually refining models, financial institutions can effectively mitigate fraud risks and safeguard their trading operations.

Utilizing machine learning algorithms to prevent financial fraud and ensure transaction security

Financial fraud poses a significant threat to the global economy, necessitating advanced measures for detection and prevention. This paper explores the application of machine learning techniques to enhance transaction security and combat financial fraud. It provides a comprehensive overview of machine learning algorithms, including supervised and unsupervised learning, neural networks, and anomaly detection. Each technique's application in identifying and preventing fraudulent activities is discussed, along with their advantages and limitations. Challenges in implementing machine learning for fraud detection, such as data quality, scalability, real-time processing, and model interpretability, are examined. Ethical and privacy concerns associated with using machine learning in financial transactions are also addressed. By highlighting these aspects, the paper aims to contribute to developing more effective and ethical machine learning-based fraud detection systems, ensuring robust transaction security and fostering trust in financial institutions.

Adaptive machine learning models: Concepts for real-time financial fraud prevention in dynamic environments

Adaptive machine learning models are revolutionizing real-time financial fraud prevention in dynamic environments, offering unparalleled accuracy and responsiveness to evolving fraud patterns. Financial institutions face constant threats from increasingly sophisticated fraud schemes that adapt and change over time. Traditional static models often fall short in addressing these rapidly shifting threats, necessitating the adoption of adaptive machine learning techniques. Adaptive machine learning models are designed to evolve continuously by learning from new data and adjusting to emerging fraud patterns. These models employ advanced algorithms, such as reinforcement learning, online learning, and deep learning, to maintain their effectiveness in detecting and preventing fraud. Reinforcement learning algorithms optimize detection strategies by receiving feedback from their actions, continually improving their decision-making processes. Online learning algorithms update models incrementally as new transaction data becomes available, ensuring that the models remain current and responsive. One of the key strengths of adaptive machine learning models is their ability to process vast amounts of data in real time. By leveraging technologies such as neural networks and ensemble learning, these models can analyze complex datasets, identify subtle anomalies, and detect fraudulent activities with high precision. Real-time data processing capabilities enable immediate detection and response to suspicious transactions, significantly reducing the risk of financial losses. Adaptive models also incorporate anomaly detection techniques to identify deviations from normal transaction behavior. By constantly learning from the latest data, these models can recognize previously unseen fraud patterns, providing a robust defense against novel threats. Additionally, the integration of explainable AI (XAI) techniques ensures that the decision-making processes of these models are transparent and interpretable, fostering trust and compliance with regulatory requirements. Implementing adaptive machine learning models for real-time fraud prevention involves addressing challenges such as data quality, computational efficiency, and model interpretability. Financial institutions must ensure the availability of high-quality data and invest in robust computational infrastructure to support real-time processing. Furthermore, adopting explainable AI techniques enhances model transparency and regulatory compliance. In conclusion, adaptive machine learning models offer a dynamic and effective solution for real-time financial fraud prevention. By continuously learning and adapting to new data, these models provide a resilient defense against evolving fraud schemes, enhancing the security and integrity of financial transactions. This adaptive approach not only mitigates financial risks but also strengthens the overall trustworthiness of financial systems.

Fort-Trust: Safeguarding online transaction by machine learning

In the digital age, the safety of online transactions has become an important situation for both customers and groups. The increasing sophistication of cyber-attacks necessitates the development of robust and sensible protection mechanisms. "Citadel-believe: Safeguarding online Transactions through device gaining knowledge of" proposes a complete solution leveraging machine studying techniques to decorate the safety of online financial transactions. This method aims to stumble on and mitigate fraudulent activities in real time, presenting a further layer of safety past conventional techniques. e-commerce has completely changed the way people shop, it has also made people more susceptible to online transaction fraud. This problem is addressed by the innovative framework Fort-Trust, which uses the XGBoost algorithm for fraud detection. Fort-Trust incorporates feature correlation analysis to solve a prevalent problem in this field: imbalanced datasets. This strategy aims to maximize detection accuracy while minimizing false positives. The high precision rate that XGBoost delivers, according to the results, is essential for lowering financial losses and increasing user confidence. All things considered, Fort-Trust strengthens the security and dependability of online transactions by providing a strong and useful solution for real-world fraud detection

Hybrid Digital Certificate Management System with QR Code and IoT Integrated on Hyperledger Fabric Blockchain

In contemporary society, management of physical documents such as educational certificates, identity proofs, vehicle registrations, and marriage certificates is an integral part of daily life. However, in present online world, there is a pressing need for digital transformation and the management of these documents. One significant challenge associated with this transformation is the susceptibility of original documents to replication or duplication. This vulner- ability is particularly concerning in the case of educational certificates, where fraud is prevalent. Fraudulent activities in the education sector can influence the proliferation of counterfeit educational and skill certificates, posing serious risks to society. For instance, individuals holding fraudulent degrees in professions such as engineering, medicine, law, and pharmacy may lack genuine competence, thereby posing substantial societal harm. To address the issue of certificate oversight and deter forgery, various approaches have been employed. Traditional methods typically involve the use of centralized databases or web servers for certificate storage, which introduces vulnerabilities because they represent single points of failure that leads to forgery and information loss. An optimal solution lies in the adoption of a Blockchain system that leverages a decentralized database structure to enhance data storage capacity and security. Blockchain technology has demonstrated disruptive potential and in- novative capabilities across multiple sectors because of its decentralized, transparent, and secure attributes. Its impact spans various domains, including banking, supply chain management, healthcare, education, and finance. Notably, in the education sector, Blockchain technology holds promise in enhancing security, transparency, and efficiency across different educational processes. In this study, we explore existing Blockchain oriented certificate management systems, critically analyze their limitations, and propose a novel hybrid educational certificate management model. The proposed model integrates Hyperledger Fabric, IoT, and 2D Barcode to develop a robust and secure framework for managing educational certificates.

Internet usage, access, and supervision among Ghanaian children: a cross-sectional study

Children’s access to and usage of the Internet has numerous benefits. However, their Internet access and use may pose risks to them, including cyberbullying, grooming, sexting, and exposure to dangerous materials. These risks may be exacerbated by inadequate supervision of children’s online activities. Therefore, this study examined the prevalence of Internet use, Internet access, and Internet use supervision among children aged 8–17 in Ghana. This study used secondary quantitative and qualitative data from the Ministry of Gender, Children, and Social Protection. The Statistical Package for the Social Sciences (SPSS) version 26 was used to analyze the quantitative data. In contrast, the qualitative data was analyzed using the constant comparison method with the aid of NVivo software version 10. The results showed that 90.5% of children had ever used the Internet. Smartphones (45.7), café facilities (21.7), and laptops/computers (21.6) were the most popular sources of Internet access. More than half of children (56.7) indicated they received little or no supervision when using their smartphones to access the Internet, which could lead to engagement in fraudulent activities, exposure to violent scenes online, and Internet addiction. The study found that most children had never used the Internet and received little or no supervision when accessing the Internet. It is, therefore, recommended that children need to be educated on the benefits and potential risks of excessive Internet use. Also, parents need to supervise and monitor their children’s Internet usage at home.

The role of artificial intelligence in auditing and fraud detection in accounting information systems: moderating role of natural language processing

Purpose This study aims to investigate the moderating role of natural language processing natural language processing (NLP) on the relationship between AI-empowered AIS (data gathering, data analysis, risk assessment, detection, prevention and Investigation) and auditing and fraud detection. Design/methodology/approach Quantitative methodology was adapted through a questionnaire. In total, 221 individuals represented the population of the study, and SPSS was used to screen primary data. The study indicated the acceptance of the hypothesis that “Artificial Intelligence in AIS has a statistically significant influence on auditing and fraud detection,” showing a strong correlation between auditing and fraud detection. The study concluded that NLP moderates the relationship between AI in AIS and auditing and fraud detection. Findings The study’s implications lie in its contribution to the development of theoretical models that explore the complementary attributes of AI and NLP in detecting financial fraud. Research limitations/implications A cross-sectional design is a limitation. Practical implications NLP is a useful tool for developing more efficient methods for detecting fraudulent activities and audit risks. Originality/value The study’s originality stems from its focus on the use of AI-empowered AIS, a relatively new technology that has the potential to significantly impact auditing and fraud detection processes within the accounting field.

Effectiveness of internal controls mechanisms in preventing and detecting fraud

This review examines the effectiveness of internal control mechanisms in preventing and detecting fraud within organizations. Internal control mechanisms are essential components of corporate governance, designed to safeguard assets, ensure the accuracy of financial records, and promote operational efficiency. The study begins by defining internal controls and their critical role in fraud prevention and detection, highlighting the significance of a robust control environment. The components of the internal control environment, risk assessment, control activities, information and communication, and monitoring activities are discussed to provide a comprehensive understanding of their structure and function. Preventive controls, such as segregation of duties, authorization processes, and access controls, are analyzed to assess their role in minimizing the risk of fraud occurrence. Case studies and examples of organizations with effective preventive measures are presented to illustrate best practices. The challenges and limitations faced by organizations in implementing preventive controls are also explored. Detective controls, including reconciliation processes, internal audits, and whistleblower hotlines, are evaluated for their ability to identify and expose fraudulent activities. The review reviews case studies demonstrating the success of detective controls in uncovering fraud and discusses the obstacles organizations encounter in maintaining effective detection mechanisms. The evaluation section of the study focuses on metrics and indicators used to assess the effectiveness of internal controls, such as the reduction in fraud incidents and increased detection rates. Tools and techniques for evaluation, including internal and external audits, are examined to understand their role in ensuring control effectiveness. The review concludes by offering best practices for enhancing internal control mechanisms, emphasizing continuous monitoring, employee training, and the adoption of advanced technologies. The critical role of management in fostering a culture of ethical behavior and supporting internal audit functions is underscored. Recommendations for organizations and directions for future research are provided to guide ongoing efforts in fraud prevention and detection. Keywords: Fraud Prevention, Internal Control, Detective Control.

Internal Controls, Frauds in the Nigerian Banking Industry

The study examined the management of fraud and internal controls in the Nigerian banking industry. Two research hypotheses were raised. The study employed a descriptive survey method as the research design. Data used for this study were collected mainly from both primary sources. The data collected through the use of questionnaires were analyzed using the Pearson Chi-square contingency test with the SPSS statistical tool (SPSS version 22). The findings revealed that in most banking industries today, fraud is seen as a normal thing that cannot be avoided, prevented or even detected. Proper steps have been totally ignored. Fraudulent activities affect the organization, the employees, their families and subsequently the economy. It can be deduced that there are control measures that can greatly help if applied properly and effectively. This can help put fraud under control and even detect all fraudulent activities. The study recommended that banks should ensure that there should be appropriate authorization and approval by the opposite person of all transactions of the operations of the bank. Banks should make sure there are adequate supervisory controls by the management. These are the controls exercised by management outside the day-to-day routine of the system.

Salmon origin traceability based on hyperspectral imaging data fusion strategy and improved deep learning method

Salmon is subject to significant fraudulent activities due to its high economic value, making it crucial to accurately determine its geographical origin for trade, consumers, and food safety. This study aims to explore the spectral and textural features in Hyperspectral Imaging (HSI) data of salmons and investigate the potential of deep learning methods and information fusion strategy in salmon origin traceability. Additionally, an improved dung beetle optimization algorithm optimized Bidirectional Gated Recurrent Unit (BiGRU) deep learning model (CNN-BiGRU) was constructed to handle high-dimensional data accurately in discerning the origin of salmon. On the other hand, an ensemble learning model was built by stacking three base models (Random Forest, LightGBM, and Gradient Boosting Decision Tree), and a comprehensive evaluation of the predictive performance between the ensemble learning model and the base models was conducted. The results indicate that the classification performance of the ensemble learning model consistently outperforms the base models. However, compared with traditional machine learning methods, deep learning models demonstrate superior performance in handling high-dimensional data, particularly exhibiting robustness in handling fused data. Notably, the MSADBO–CNN–BiGRU model stands out among all algorithms, showing an improvement of 0.8%–7% in accuracy on the test set compared to the pre-optimized CNN model, with the best model achieving a test set accuracy of 99.5%. Overall, this study demonstrates that combining deep learning with information fusion strategy can rapidly and accurately identify the geographical origin of salmon, providing methods and insights for future traceability of agricultural products.

Improving the internal control system in the digital economy

Subject. This article examines the problems faced by the internal control systems of economic entities in the context of the digital economy, and it searches for potential solutions to improve the efficiency and adaptability of these systems. Objectives. The article aims to substantiate the areas for improving the internal control system in the context of digitalization. Methods. For the study, we used a systems approach, comparison, systematization, and data generalization. Results. Based on the analysis of the problems associated with the improvement of control procedures in the context of the digital economy, the article offers recommendations for the phased implementation of digital transformation in the internal control system of a commercial organization, and it identifies digital technologies that can help in the successful implementation of each of the identified stages. Conclusions. By implementing digital transformation, organizations can streamline internal control processes by automating manual and repetitive tasks, thereby increasing efficiency and reducing the likelihood of errors. Using advanced analytics, organizations can make data-driven decisions, detect fraudulent activity, and streamline internal controls in real time.

AI’s Bipolar Effect on Mitigating and Motivating Frauds

In the time of digital innovation, Artificial Intelligence (AI) stands at the forefront, signaling new capabilities in fraud management but also new vulnerabilities. This paper aims to dissect AI's paradoxical influence on fraud, portraying its roles in both promoting and mitigating fraudulent activities. The research seeks to bridge the gap in understanding the dual nature of AI, highlighting the need for ethical and regulatory frameworks to traverse the complications AI introduces into fraud detection and prevention. Utilising secondary data from Google News and academic databases with the keyword 'AI fraud,' this study adopts a keyword-based analysis to sift through the most relevant literature. The approach is designed to capture a comprehensive snapshot of the current discourse, underlining the bipolar impact of AI on fraud. The analysis reveals AI's significant potential in enhancing fraud detection systems through rapid data analysis and pattern recognition. However, AI technologies can be exploited to facilitate sophisticated fraud schemes. The study underscores an urgent need for evolving practices and policies that counteract AI's potential for misuse, weighing in the emerging concept of self-regulatory AI systems as a promising direction for future research. This paper contributes insights into the dualistic role of AI in fraud, adding depth to the discourse on its implications for security, ethical considerations, and regulatory challenges. It advocates for a balanced perspective on AI's capabilities.