Gotowe bibliografie tematyczne / Fraud Analytics

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Gotowa bibliografia na temat „Fraud Analytics”

Autor: Grafiati

Data publikacji: 2 czerwca 2025

Data aktualizacji: 31 lipca 2025

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Artykuły w czasopismach na temat "Fraud Analytics"

Elizabeth, Oommen. "Role of Big Data in Regulating and Combating Financial Fraud in India." Young Researcher S14, no. 1B (2025): 256–61. https://doi.org/10.5281/zenodo.14873761.

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Financial fraud is a significant concern for the Indian economy, with the country's banking sector reporting frauds worth over ₹1.85 lakh crore in 2020-21. Big data analytics has emerged as a critical tool for detecting and preventing financial fraud. The implementation of big data analytics within India's financial sector has the potential to yield substantial annual cost savings through enhanced fraud detection and prevention mechanisms. This technological advancement serves to safeguard both consumers and businesses while simultaneously reinforcing the banking

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Shakeel, Amer, Burhan Rasheed, Zohair Farooq Malik, and Syed Taha Fraz Haider. "Attributes of Forensic Auditors and Detection of Occupational Fraud with the Moderating Role of Audit Committee." Journal of Social Sciences Advancement 6, no. 1 (2025): 10–18. https://doi.org/10.52223/jssa25-060102-119.

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Frauds committed by the employees have become a threat to the sustainability of the organizations. This study aims to investigate the relationship between the attributes of forensic auditors, such as independence, IT skills, data analytics skills, professional skepticism, experience, and fraud detection. The data was collected through questionnaires from people working in organizations' accounting, finance, and audit functions. Researchers used the PLS-SEM technique to test the significance of the relationship between studied variables. The results of this study showed that independence, IT sk

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Herlinia, Sella, and Rika Lidyah. "Implementasi Big Data Analytics Dalam Meminimalisir Fraud." Jurnal Ilmu Manajemen dan Akuntansi Terapan (JIMAT) 14, no. 2 (2024): 226–33. http://dx.doi.org/10.36694/jimat.v14i2.495.

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The high number of fraud cases that occur in Indonesia has caused large amounts of losses to be suffered by the country. Thus, finding effective methods to detect fraud is still the focus of many parties, especially the Government. Therefore this research was carried out. This research aims to analyze the implementation of big data to minimize froud. This research uses the Literature Review method. Contains reviews, summaries and the author's opinion about several sources in the library, the results obtained from this research were collected from previous research, the use of big data

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poojitha, Mallarapu, and Beedipalli Santhosh Reddy. "Fraud Auditor: A Visual Analytics Approach for Collusive Fraud in Health Insurance." International Journal of Research Publication and Reviews 6, no. 5 (2025): 11596–99. https://doi.org/10.55248/gengpi.6.0525.18108.

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Chibuzor Njoku, Genevieve Okafor, Ehisuoria E. Akhuemonkhan, Ifeoma Naibe, and Aniel K. Diala. "Leveraging data analytics for fraud detection: The future of financial risk mitigation and regulatory compliance." Computer Science & IT Research Journal 6, no. 2 (2025): 86–93. https://doi.org/10.51594/csitrj.v6i2.1859.

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The increasing complexity of financial fraud schemes has necessitated the adoption of advanced data analytics, AI-driven fraud detection models, and forensic accounting tools to strengthen corporate fraud prevention and regulatory compliance. Traditional fraud detection techniques have proven inadequate in identifying sophisticated financial crimes, prompting organizations to integrate predictive analytics, machine learning algorithms, and real-time transaction monitoring systems to mitigate fraud risks. This paper examines how data analytics enhances financial risk mitigation, the role of AI

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Celestin, M., and A. K. Mishra. "How Data Analytics is Revolutionizing Forensic Accounting Investigations: A Deep Dive into Fraud Detection Techniques." Insight Journal of National Open College 2, no. 1 (2025): 31–50. https://doi.org/10.5281/zenodo.15365611.

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Forensic accounting has long relied on traditional auditing techniques, but the integration of data analytics has revolutionized fraud detection, enhancing accuracy and efficiency. This study explores the role of data analytics in forensic accounting investigations over the past five years, assessing its impact on fraud detection methodologies. Using secondary data analysis, the study examines forensic reports, financial fraud case studies, and regulatory documents from 2020 to 2024. Key findings indicate a significant increase in fraud detection success, rising from 68% in 2020 to 88% in 2024

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Mohammad, Nur, Md Ahsan Ullah Imran, Mani Prabha, Sadia Sharmin, and Rabeya Khatoon. "COMBATING BANKING FRAUD WITH IT: INTEGRATING MACHINE LEARNING AND DATA ANALYTICS." American Journal of Management and Economics Innovations 6, no. 7 (2024): 39–56. http://dx.doi.org/10.37547/tajmei/volume06issue07-04.

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Banking fraud poses a significant threat to financial institutions, customers, and the stability of the financial system. Traditional fraud detection methods, which rely heavily on rule-based systems, have proven inadequate against increasingly sophisticated fraud techniques. This paper explores the integration of Information Technology (IT), specifically Machine Learning (ML) and Data Analytics, in combating banking fraud. Through a comprehensive review of existing literature and case studies, advancements in fraud detection methodologies are highlighted, emphasizing the effectiveness of vari

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Anggraini, Leriza Desitama, Imelda Saluza, and Faradillah Faradillah. "Utilizing Data Analytics To Identify Fraud Potential: An Internal Auditor's Perspective." Accounting and Finance Studies 5, no. 1 (2025): 037–52. https://doi.org/10.47153/afs51.12922025.

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Research Aims: This research aims to analyze the influence of Data Analytics on the detection of potential fraud. Design/methodology/approach: The methodology used is Structural Equation Modeling-Partial Least Squares (SEM-PLS) with data collected through questionnaires distributed to respondents, namely internal auditors who work in South Sumatra Province and Bangka Belitung Province. Research Findings: The research results show that Descriptive Analytics and Diagnostic Analytics have a significant influence on fraud detection and prevention, while Predictive Analytics and Prescriptive Analyt

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Abhishek, A. Sampath. "Predictive Analytics with Machine Learning for Fraud Detection." International Journal for Research in Applied Science and Engineering Technology 9, no. 11 (2021): 1518–20. http://dx.doi.org/10.22214/ijraset.2021.39046.

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Abstract: The popularity of online shopping is growing day by day. In financial year 2021, over 40 billion digital transactions worth more than a quadrillion Indian rupees were recorded across the country. As the number of credit card users rise world- wide, the opportunities for attackers to steal credit card details and subsequently, commit fraud are also increasing. Since humans tend to exhibit specific behavioristic profiles, every cardholder can be represented by a set of patterns containing information about the typical purchase category, the time since the last purchase, the amount of m

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N.A., Padmalatha. "E-Commerce Fraud Detection Tools." Shanlax International Journal of Management 7, S1 (2020): 24–32. https://doi.org/10.5281/zenodo.3737716.

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India is a cash based society. However, as per the reports of NASSCOM, India  is expected to have Internet users base of about 730 million by the end of 2020.  It has been noted that by 2020, Indian e-commerce industry is expected to reach $34 billion, with 200 million individuals transacting online, growing 3X over 2015. This spread of e-Commerce has led to the rise of several niche players who largely specialize their products around a specific theme including fraud detection tools. Experts predict online credit card fraud to increase to $32 billion by 2020. In order to combat tech

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Rozprawy doktorskie na temat "Fraud Analytics"

Phillips, Thurman B., and Raymond J. Lanclos. "Data analytics in procurement fraud prevention." Thesis, Monterey, California: Naval Postgraduate School, 2014. http://hdl.handle.net/10945/42708.

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Approved for public release; distribution is unlimited The purpose of this research project is to explore the viability of detecting anomalies through using data analytics software as a tool in procurement fraud prevention and to analyze its potential policy implications on federal procurement stakeholders. According to a survey conducted in 2012 by the Association of Certified Fraud Examiners, organizations lose an estimated 5% of their revenues to fraud each year. In order to relate this estimate to the Department of Defense (DOD), this estimated percentage was applied to the requested DO

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Jofre, Alegria Maria Paz. "Fighting Accounting Fraud through Forensic Analytics." Thesis, The University of Sydney, 2017. http://hdl.handle.net/2123/17826.

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Accounting Fraud is one of the most harmful financial crimes as it often results in massive corporate collapses, commonly silenced by powerful high-status executives and managers. Accounting fraud represents a significant threat to the financial system stability due to the resulting diminishing of the market confidence and trust of regulatory authorities. Its catastrophic consequences expose how vulnerable and unprotected the community is in regards to this matter, since most damage is inflicted to investors, employees, customers and government. Accounting fraud is defined as the calculated m

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Du, Pon Adam Watanabe. "Competition, Cost Analytics, and Offsetting Strategies: Pressures and Opportunities on the Fraud Triangle." Diss., Virginia Tech, 2021. http://hdl.handle.net/10919/102937.

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This study introduces industry competition factors to fraud models to examine how competition associates with fraud risk. I argue that industry competition eclipses many firm-level determinants in their association with fraud risk, and that the cost of poor information elevates fraud risk as competition increases. I find that fraud risk is higher for firms in industries with 1) more substitutable products and services, 2) greater threats of new entry, and 3) larger incumbent pools of competitors, and that substitution exceeds every firm-level variable except size in its relevance with fraud ri

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Evett, Chantal. "What are the Potential Impacts of Big Data, Artificial Intelligence and Machine Learning on the Auditing Profession?" Scholarship @ Claremont, 2017. http://scholarship.claremont.edu/cmc_theses/1642.

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To maintain public confidence in the financial system, it is essential that most financial fraud is prevented and that incidents of fraud are detected and punished. The responsibility of uncovering creatively implemented fraud is placed, in a large part, on auditors. Recent advancements in technology are helping auditors turn the tide against fraudsters. Big Data, made possible by the proliferation, widespread availability and amalgamation of diverse digital data sets, has become an important driver of technological change. Big Data analytics are already transforming the traditional audit. Sam

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Jones, Keith Lamar. "Improving fraud risk assessments through analytical procedures." Diss., The University of Arizona, 2004. http://hdl.handle.net/10150/280556.

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This study incorporates concepts from accounting and criminology literatures to develop a model of financial statement variables that provides researchers and auditors with information about the likelihood of fraudulent financial reporting. This study is also one of the first to test whether the predictive ability of fraud indicators has changed over time. Game theory suggests that if fraud firms consistently manifest similar characteristics then auditors will isolate those fraud indicators and react to them. The results show that accruals, market-to-book ratio, and lack of a Big Four auditor

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Ortiz, Angel. "Strategies to Reduce Occupational Fraud in Small Restaurants." ScholarWorks, 2018. https://scholarworks.waldenu.edu/dissertations/5241.

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Occupational fraud is a growing business risk that is causing greater financial losses in small businesses than large businesses. Business owners lose approximately 5% of their revenues due to occupational fraud. The purpose of this multiple case study was to explore the strategies used by some business owners of small restaurants to reduce occupational fraud. The fraud triangle theory was the conceptual framework for this study. Three small restaurant owners from Puerto Rico participated in face-to-face, semistructured interviews to reveal their successful strategies to minimize fraud. The da

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Madureira, Noé Loureiro. "Aplicabilidade da lei de Benford na análise de um conjunto de dados eleitorais." Universidade do Estado do Rio de Janeiro, 2012. http://www.bdtd.uerj.br/tde_busca/arquivo.php?codArquivo=6169.

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Essa pesquisa teve como objetivo analisar a aplicabilidade de uma metodologia de investigação das disfunções existentes no conjunto de dados eleitorais de uma eleição para cargo majoritário no Brasil. O desenvolvimento e a rápida expansão da teoria descoberta por Newcomb-Benford, conhecida como Lei de Benford, bastante difundida por Mark J. Nigrini, Ph.D. no meio contábil, com a utilização de testes estatísticos, despertou o interesse por esta pesquisa. Vislumbrou-se a possibilidade de reduzir o risco de fraude eleitoral através da utilização da análise digital. Entretanto, é necessário adotar

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Muftah, Mustafa Ramadan. "Corporate criminal liability : an analytical study of the application of the criminal law to companies and to their directing management." Thesis, University of Aberdeen, 1998. http://digitool.abdn.ac.uk/R?func=search-advanced-go&find_code1=WSN&request1=AAIU603189.

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The aim of this thesis is to analyse the concept of corporate criminal liability in Britain and elsewhere when comparison is needed. The concept of holding a corporation criminally liable is not new, it has been known for a long time, but the development in the last century and especially in the last quarter of this century is the main reason to turn and focus the public's attention towards blaming corporations for a quite wide range of society's ills. As a result of decisions by corporate executives or managers, and because corporation's negligence and pursuit of profits at any cost, may caus

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Adnan, Gunawan. "Women and the glorious Qurʼān an analytical study of women related verses of Sūra An-Nisaʼ /". Göttingen : Univ.-Verl. Göttingen [u.a.], 2004. http://d-nb.info/99124933X/34.

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Vieira, Marcus KlÃredis Monteiro. "A ExperiÃncia do Inconsciente para o Psicanalista em FormaÃÃo, segundo Freud." Universidade Federal do CearÃ, 2010. http://www.teses.ufc.br/tde_busca/arquivo.php?codArquivo=5956.

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FundaÃÃo de Amparo Ã Pesquisa do Estado do CearÃ O objetivo de nossa pesquisa Ã acompanhar o desenvolvimento das formulaÃÃes freudianas referentes Ã experiÃncia do inconsciente â auto-anÃlise, interpretaÃÃo mÃtua e anÃlise pessoal - para o psicanalista em formaÃÃo. Com este intuito, iniciamos nosso trabalho com o exame bibliogrÃfico dos aspectos clÃnicos, teÃricos e institucionais da anÃlise de formaÃÃo em trÃs autores: Ferenczi, Balint e Lacan. ApÃs termos situado o problema sob as perspectivas desses autores, realizamos a pesquisa dos textos de Freud que abordam o assunto. Dividimos a abo

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Książki na temat "Fraud Analytics"

Vona, Leonard W. Fraud Data Analytics Methodology. John Wiley & Sons, Inc., 2017. http://dx.doi.org/10.1002/9781119270331.

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Baesens, Bart, Véronique Van Vlasselaer, and Wouter Verbeke. Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques. John Wiley & Sons, Inc, 2015. http://dx.doi.org/10.1002/9781119146841.

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Westphal, Christopher R. Data mining for intelligence, fraud & criminal detection: Advanced analytics & information sharing technologies. CRC Press, 2008.

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Kazakova, Nataliya. Financial security of the company. INFRA-M Academic Publishing LLC., 2023. http://dx.doi.org/10.12737/1908969.

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The textbook provides theoretical and practical training of business analysts on the financial security of companies. Considers the regulatory legal and methodological basis for the diagnosis of bankruptcy of organizations, as well as corporate fraud as a type of economic crimes; analytical tools for assessing the level of financial security based on a risk-oriented approach, the basics of building an internal financial security control system, including monitoring of the company's business processes affecting its financial security, as well as methods for assessing the risks of corporate frau

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P, Levine Michael, ed. The analytic Freud: Philosophy and psychoanalysis. Routledge, 2000.

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Accountants, Institute of Management, ed. The role of analytical procedures in detecting management fraud. Institute of Management Accountants, 1993.

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1918-, King Pearl, Steiner Riccardo, and British Psycho-Analytical Society, eds. The Freud-Klein controversies, 1941-45. Tavistock/Routledge, 1991.

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Bruder-Bezzel, Almuth, Elke Metzner, and Martin Schimkus. Die Gründung der Internationalen Psychoanalytischen Vereinigung durch Freud und Jung. Psychosozial-Verlag, 2011.

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Cutrofello, Andrew. Imagining otherwise: Metapsychology and the analytic a posteriori. Northwestern University Press, 1997.

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Roger, Hobdell, ed. Freud, Jung, Klein-- the fenceless field: Essays on psychoanalysis and analytical psychology. Routledge, 1995.

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Części książek na temat "Fraud Analytics"

Araz, Özgür M., and David L. Olson. "Fraud Detection." In Risk and Predictive Analytics in Business with R. Chapman and Hall/CRC, 2025. https://doi.org/10.1201/9781003562399-8.

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Dong, Huijian. "Data Analytics in Fraud Detection." In Data Analytics in Finance. Auerbach Publications, 2025. https://doi.org/10.1201/9781003620198-12.

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Hammersburg, Carl. "Fraud, Waste, and Abuse." In A Practical Guide to Analytics for Governments. John Wiley & Sons, Inc., 2017. http://dx.doi.org/10.1002/9781119362517.ch9.

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Husain, Mohd Shahid, Mohammad Zunnun Khan, and Tamanna Siddiqui. "Big Data Analytics for Fraud Detection." In Big Data Concepts, Technologies, and Applications. Auerbach Publications, 2023. http://dx.doi.org/10.1201/9781003441595-13.

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Alzahrani, Ahmad, and Samira Sadaoui. "Clustering and Labeling Auction Fraud Data." In Data Management, Analytics and Innovation. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-32-9949-8_20.

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Koitzsch, Kerry. "A Bayesian Analysis Component: Identifying Credit Card Fraud." In Pro Hadoop Data Analytics. Apress, 2016. http://dx.doi.org/10.1007/978-1-4842-1910-2_12.

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Trikha, Archana, and Sandip M. Khant. "Queue Classification for Fraud Types: Banking Domain." In Data Management, Analytics and Innovation. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1274-8_20.

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Priscilla, C. Victoria, and D. Padma Prabha. "Credit Card Fraud Detection: A Systematic Review." In Learning and Analytics in Intelligent Systems. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-38501-9_29.

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Pomerleau, Pierre-Luc, and Mathieu Auger-Perreault. "Fraud Risk Management: Using Fraud Analytics to Combat External and Insider Threats." In Encyclopedia of Security and Emergency Management. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-69891-5_296-1.

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Pomerleau, Pierre-Luc, and Mathieu Auger-Perreault. "Fraud Risk Management: Using Fraud Analytics to Combat External and Insider Threats." In Encyclopedia of Security and Emergency Management. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-319-70488-3_296.

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Streszczenia konferencji na temat "Fraud Analytics"

Sivakumar, V., Akash Kumar Verma, Piyush Kumar, Nitesh Saw, and V. Hari Krishnan. "Fraud Detection in Financial Transaction Using GNN." In 2025 International Conference on Visual Analytics and Data Visualization (ICVADV). IEEE, 2025. https://doi.org/10.1109/icvadv63329.2025.10960835.

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Khare, Pranav, Sahil Arora, and Sandeep Gupta. "Predictive Analytics in Digital ID Verification for Fraud Prevention." In 2024 4th Asian Conference on Innovation in Technology (ASIANCON). IEEE, 2024. https://doi.org/10.1109/asiancon62057.2024.10838197.

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Yehya, Batoul Abo, and Nazih Salhab. "Telecommunications Fraud Machine Learning-based Detection." In 2023 4th International Conference on Data Analytics for Business and Industry (ICDABI). IEEE, 2023. http://dx.doi.org/10.1109/icdabi60145.2023.10629612.

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Hasudungan, Yehezkeil, Fitra Putri Oganda, Ora Plane Maria Daeli, Raihan, and Nesti Anggraini Santoso. "Big Data Analytics for Proactive Financial Fraud Detection and Prevention." In 2024 2nd International Conference on Technology Innovation and Its Applications (ICTIIA). IEEE, 2024. https://doi.org/10.1109/ictiia61827.2024.10761131.

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Kaushik, Priyanka, Saurabh Pratap Singh Rathore, Anand Singh Bisen, and Rachna Rathore. "Enhancing Insurance Claim Fraud Detection Through Advanced Data Analytics Techniques." In 2024 IEEE Region 10 Symposium (TENSYMP). IEEE, 2024. http://dx.doi.org/10.1109/tensymp61132.2024.10752284.

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Korkanti, Sukanth. "Enhancing Financial Fraud Detection Using LLMs and Advanced Data Analytics." In 2024 2nd International Conference on Self Sustainable Artificial Intelligence Systems (ICSSAS). IEEE, 2024. https://doi.org/10.1109/icssas64001.2024.10760895.

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R.Preethi, S.E.Viswapriya, and M.Saraswathi. "AI-Powered GST Analytics: Detecting Fraud Using Machine Learning Approach." In 2025 2nd International Conference on Trends in Engineering Systems and Technologies (ICTEST). IEEE, 2025. https://doi.org/10.1109/ictest64710.2025.11042499.

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Gancheva, Veska, George Popov, Ognyan Nakov, Kamelia Raynova, and Antoaneta Popova. "Customs Fraud Detection Method Based on Artificial Intelligence and Data Analytics." In 2024 4th International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME). IEEE, 2024. https://doi.org/10.1109/iceccme62383.2024.10796800.

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Akashpalaniappa, A., and S. Nithya Roopa. "AI-Enhanced Predictive Analytics for Fraud Detection in Smart Grid Systems." In 2025 3rd International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA). IEEE, 2025. https://doi.org/10.1109/icaeca63854.2025.11012186.

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Alghasra, Abdulla Yaseen, Dawla Almulla, Mohammed Abbas, and Minwir Al-Shammari. "Machine Learning for Detecting Financial Statement Fraud: A Bibliometric Analysis." In 2024 5th International Conference on Data Analytics for Business and Industry (ICDABI). IEEE, 2024. https://doi.org/10.1109/icdabi63787.2024.10800738.

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Raporty organizacyjne na temat "Fraud Analytics"

Phillips, Thurman B., and Raymond J. Lanclos III. Data Analytics in Procurement Fraud Prevention. Defense Technical Information Center, 2014. http://dx.doi.org/10.21236/ada626749.

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Pasupuleti, Murali Krishna. Automated Smart Contracts: AI-powered Blockchain Technologies for Secure and Intelligent Decentralized Governance. National Education Services, 2025. https://doi.org/10.62311/nesx/rrv425.

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Abstract: Automated smart contracts represent a paradigm shift in decentralized governance by integrating artificial intelligence (AI) with blockchain technologies to enhance security, scalability, and adaptability. Traditional smart contracts, while enabling trustless and automated transactions, often lack the flexibility to adapt to dynamic regulatory frameworks, evolving economic conditions, and real-time security threats. AI-powered smart contracts leverage machine learning, reinforcement learning, and predictive analytics to optimize contract execution, detect fraudulent transactions, and

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Wetzel, Deborah, Fátima Cartaxo, Ricardo Poppi, and Mariano Lafuente. Data-driven Digital Innovation to Strengthen Integrity: TCU’s Payroll Oversight in Brazil. Inter-American Development Bank, 2022. http://dx.doi.org/10.18235/0004549.

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This technical note documents the Continuous Payroll Audit Initiative within Brazils Court of Accounts (Tribunal de Contas da Unio, or TCU), presents its lessons learned, and makes recommendations that could improve its results. This initiative uses digital tools such as data analytics to proactively identify and address incorrect and/or illicit payments in the federal civil service and pension payroll. As of 2021, the initiative had led to savings close to $R 2 billion (US$515 million). The study demonstrates that a small team using the right technology can be very effective in rooting out ir

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Volkova, Nataliia P., Nina O. Rizun, and Maryna V. Nehrey. Data science: opportunities to transform education. [б. в.], 2019. http://dx.doi.org/10.31812/123456789/3241.

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The article concerns the issue of data science tools implementation, including the text mining and natural language processing algorithms for increasing the value of high education for development modern and technologically flexible society. Data science is the field of study that involves tools, algorithms, and knowledge of math and statistics to discover knowledge from the raw data. Data science is developing fast and penetrating all spheres of life. More people understand the importance of the science of data and the need for implementation in everyday life. Data science is used in business

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