Log in

Relevant bibliographies by topics / Fraud Analytics / Journal articles

To see the other types of publications on this topic, follow the link: Fraud Analytics.

Journal articles on the topic 'Fraud Analytics'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the top 50 journal articles for your research on the topic 'Fraud Analytics.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.

1

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.

Full text

Abstract:

<em>F</em><em>inancial fraud is a significant concern for the Indian economy, with the country's banking sector reporting frauds worth over </em><em>₹</em><em>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

APA, Harvard, Vancouver, ISO, and other styles

2

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

3

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

4

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

5

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

6

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

7

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

8

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

9

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

10

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

11

SATHYA, Dr S. S. "UPI Payment Fraud Detection Using Machine Learning." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem46296.

Full text

Abstract:

Abstract—The UPI fraud detection system is designed to improve security and reliability. Their purpose is to protect the users from all types of fraudulent activities while conducting digital payment transactions. This paper intends to make use of superior machine learning algorithms and data analytics to study the transaction patterns and recognize the anomalies that indicate possible fraud. Second, the study intends to develop a very strong system, which will identify and detect various types of UPI frauds, including phishing, identity theft, and unauthorized transactions. The paper will als

APA, Harvard, Vancouver, ISO, and other styles

12

Tang, Jiali, and Khondkar E. Karim. "Financial fraud detection and big data analytics – implications on auditors’ use of fraud brainstorming session." Managerial Auditing Journal 34, no. 3 (2019): 324–37. http://dx.doi.org/10.1108/maj-01-2018-1767.

Full text

Abstract:

PurposeThis paper aims to discuss the application of Big Data analytics to the brainstorming session in the current auditing standards.Design/methodology/approachThe authors review the literature related to fraud, brainstorming sessions and Big Data, and propose a model that auditors can follow during the brainstorming sessions by applying Big Data analytics at different steps.FindingsThe existing audit practice aimed at identifying the fraud risk factors needs enhancement, due to the inefficient use of unstructured data. The brainstorming session provides a useful setting for such concern as

APA, Harvard, Vancouver, ISO, and other styles

13

Elsa Natali, Gilbert Rely, and Pratiwi Nila Sari. "PENGARUH e-AUDIT, TEKNOLOGI AUDIT BERBASIS AI, DAN BIG DATA ANALYTICS TERHADAP DETEKSI FRAUD (STUDI EMPIRIS PADA BADAN PEMERIKSA KEUANGAN REPUBLIK INDONESIA)." Jurnal Akuntansi, Keuangan, Perpajakan dan Tata Kelola Perusahaan 2, no. 3 (2025): 772–82. https://doi.org/10.70248/jakpt.v2i3.1942.

Full text

Abstract:

Penelitian ini bertujuan untuk menguji pengaruh e-Audit, teknologi audit berbasis AI, dan big data analytics terhadap deteksi fraud pada Badan Pemeriksa Keuangan Republik Indonesia (BPK RI), menggunakan pendekatan kuantitatif dengan data yang dikumpulkan melalui kuesioner Skala Likert. Populasi penelitian adalah auditor pada Kantor Pusat BPK RI, pengambilan sampel menggunakan metode purposive sampling sebanyak 100 responden. Variabel independen e-Audit, teknologi audit berbasis AI, dan big data analytics, sedangkan variabel dependen deteksi fraud. Metode analisis data yang digunakan adalah uji

APA, Harvard, Vancouver, ISO, and other styles

14

Adetunji Paul Adejumo and Chinonso Peter Ogburie. "Forensic accounting in financial fraud detection: Trends and challenges." International Journal of Science and Research Archive 14, no. 3 (2025): 1219–32. https://doi.org/10.30574/ijsra.2025.14.3.0815.

Full text

Abstract:

Forensic accounting plays a pivotal role in detecting and preventing financial fraud, blending investigative skills with accounting expertise to uncover financial discrepancies. As corporate fraud schemes grow more sophisticated, forensic accountants utilize advanced techniques such as data analytics, artificial intelligence (AI), and blockchain for fraud detection. This paper explores current trends and challenges in forensic accounting, highlighting its increasing significance in financial crime prevention. Recent advancements in forensic accounting include the use of big data analytics and

APA, Harvard, Vancouver, ISO, and other styles

15

Jannah, Firdatul, Anara Indrany Nanda Ayu Anissa, Wanda Maulida, and Novita Novita. "The Use of Big Data Analytics in Detecting Academic Fraud." Asia Pacific Fraud Journal 7, no. 2 (2022): 173. http://dx.doi.org/10.21532/apfjournal.v7i2.261.

Full text

Abstract:

This study aims to find out the effect of using big data analytics on the detection of academic fraud so that it can provide improvements and create significant changes, especially in reducing the level of academic fraud among students. The variables used in this research are big data analytics as the independent variable and academic fraud as the dependent variable. This study uses primary data obtained from ques-tionnaires distributed to Trilogy University students. The sample is 258 students from all study programs at Trilogy University class 2017 - 2020. The data processing and analysis me

APA, Harvard, Vancouver, ISO, and other styles

16

Rakibul Hasan Chowdhury. "Utilizing business analytics to combat financial fraud and enhance economic integrity." International Journal of Science and Research Archive 14, no. 1 (2025): 134–45. https://doi.org/10.30574/ijsra.2025.14.1.0022.

Full text

Abstract:

Financial fraud poses a significant threat to economic stability, with traditional detection methods often struggling to keep pace with increasingly sophisticated schemes. This paper explores the role of business analytics in enhancing fraud detection and maintaining economic integrity. Utilizing advanced techniques such as machine learning, anomaly detection, and clustering, business analytics offers a proactive approach to identifying fraudulent patterns and mitigating financial risks. The research discusses the development of a comprehensive fraud detection model that emphasizes transparenc

APA, Harvard, Vancouver, ISO, and other styles

17

Ramadhan, Dona. "Analysis of Performance Anomaly and Fraudster Profile for Fraud Prevention and Detection." Asia Pacific Fraud Journal 8, no. 2 (2023): 341. http://dx.doi.org/10.21532/apfjournal.v8i2.309.

Full text

Abstract:

The rapid development of technology provides us with a lot of data that can be used for various purposes, such as fraud risk management. Data analytics should be the basis for anti-fraud activities related to prevention and detection processes. This study aims to elaborate on the data analytics used in developing fraud red flags based on historical reports. By applying anomaly data analytics and demographic profiles of fraudsters, this study finds that performance anomalies contribute 68% to fraud, while 3 to 10 years of service without career advancement can trigger motivation to commit fraud

APA, Harvard, Vancouver, ISO, and other styles

18

Ramkumar, M., R. Supriya, K. Chaithanya, J. Veena, and A. SnehaLatha. "“Credit Card Fraud” Detection Using Data Analytics A Comparative Analysis." 1 8, no. 1 (2022): 24–29. http://dx.doi.org/10.46632/jemm/8/1/4.

Full text

Abstract:

Fraud events take place frequently which results in a huge financial loss. Fraud detections are dynamic and are not easy to identity. Data mining plays a vital role in detection of “Credit card fraud” done in fraudulent online transactions. Fraudsters use latest advanced methods which is an advantage. This process becomes challenging based on two major reasons -firstly, the profiles of users keep changing constantly and secondly, the datasets required for this are highly confusing. The overall performance of “Credit card fraud” detections is improved by sampling approach on the dataset. This r

APA, Harvard, Vancouver, ISO, and other styles

19

Mohammed Sadhik Shaik. "AI-Driven Fraud Detection: Enhancing Claims Analytics with Real-Time Streaming and Behavioral Biometrics." Economic Sciences 21, no. 1 (2025): 860–71. https://doi.org/10.69889/y3t75402.

Full text

Abstract:

Scammers and frauds have always been challenging and at the same time the most undetected threat to the insurer world. We will present the experimental work we have done on our novel pattern discovery framework for fraud detection, which is based on our examination of a historical claims data repository in ClaimCenter, together with real-time data mining techniques to detect emergent fraudulent trends. AI also improves existing rule-based engines by allowing them to incorporate anomaly detection into their existing systems, and to actively respond in order to prevent future fraud attacks. Usin

APA, Harvard, Vancouver, ISO, and other styles

20

Thakkar, Himanshu, Gudoshava Chrispen Fanuel, Saptarshi Datta, Priyam Bhadra, and Siddharth Baburao Dabhade. "Optimizing Internal Audit Practices for Combatting Occupational Fraud: A Study of Data Analytic Tool Integration in Zimbabwean Listed Companies." International Research Journal of Multidisciplinary Scope 06, no. 01 (2025): 22–36. https://doi.org/10.47857/irjms.2025.v06i01.02164.

Full text

Abstract:

Various types of fraud exist in almost every country, classified as corruption, asset misappropriation, and financial statement fraud. Occupational fraud, a mixture of corruption and asset misappropriation, has been mostly committed within companies, significantly impacting their economic stability. Interview and questionnaire techniques were employed to collect data from the companies listed on the Zimbabwe stock exchange. Primary data has been collected from 44 respondents through questionnaires. It is found that companies in Zimbabwe are widely using Excel for internal audits. Internal audi

APA, Harvard, Vancouver, ISO, and other styles

21

Bhasin, Madan Lal. "Challenge of mitigating bank frauds by judicious mix of technology: Experience of a developing country." Economics, Management and Sustainability 1, no. 1 (2016): 23–41. https://doi.org/10.14254/jems.2016.1-1.3.

Full text

Abstract:

Banks are the engines that drive the operations in the financial sector, money markets and growth of an economy. With the rapidly growing banking industry in India, frauds in banks are also increasing fast, and fraudsters have started using innovative methods. A questionnaire-based survey was conducted in 2013-14 among 345 bank employees to know their perception towards bank frauds, degree of their compliance level, and integration of technology to detect, control and prevent frauds. This study provides discussion of the attitudes, strategies, and the technology that bank specialists will need

APA, Harvard, Vancouver, ISO, and other styles

22

Agus Bandiyono. "Fraud Detection: Religion In The Workplace Big Data Analytics." Jurnal Akuntansi 27, no. 2 (2023): 380–400. http://dx.doi.org/10.24912/ja.v27i2.1515.

Full text

Abstract:

Researchers believe everyone wants to carry out religious teachings that are adhered to in the workplace, one of which is to act honestly. From an organizational point of view, big data analytics is used to suppress fraud which still occurs frequently, so this study aims to determine the effect of religion in the workplace and big data analytics on fraud detection. The research was conducted at Shariah People’s Credit Bank located in Banten Province, with a sample of 40 respondents. The data source was a questionnaire filled out by respondents. This study showed that religion in the workplace

APA, Harvard, Vancouver, ISO, and other styles

23

Novita, Novita, and Anara Indrany Nanda Ayu Anissa. "The role of data analytics for detecting indications of fraud in the public sector." International Journal of Research in Business and Social Science (2147- 4478) 11, no. 7 (2022): 218–25. http://dx.doi.org/10.20525/ijrbs.v11i7.2113.

Full text

Abstract:

Technological developments play an important role in the audit process, one of which is the use of data analytics that are useful to assist auditors in analyzing data, collecting audit evidence, predicting risks that occur and will occur, and other things. The use of data analytics is also applied by public sector auditors to maintain accountability and responsibility for state finances. This study aims to examine the effect of using data analytics on indications of fraud for public sector examiners in Indonesia. Testing and data analysis techniques used STATA version 14, which processed answe

APA, Harvard, Vancouver, ISO, and other styles

24

Wang, Yongguan. "Application of Big Data Technology in Mobile Payment Security." Journal of Research in Social Science and Humanities 2, no. 12 (2023): 18–23. http://dx.doi.org/10.56397/jrssh.2023.12.04.

Full text

Abstract:

The rapid growth in mobile technologies, particularly mobile payment platforms, has led to increased risks of fraudulent activities. This paper investigates how big data analytics can enhance fraud detection mechanisms in mobile payment systems. The research question explored is: “How do big data analytics enhance fraud detection mechanisms in mobile payment platforms?” The study adopts a quantitative approach to understand the correlation and impact of big data analytics on fraud detection mechanisms, analyzing detailed records of financial transactions and publicly available databases. The p

APA, Harvard, Vancouver, ISO, and other styles

25

Kurniawati, Kurniawati, and Laura Silvany Hivianto. "Financial Statement Fraud Detection: Synergy Artificial Intelligence with Auditor Characteristics." Balance Vocation Accounting Journal 9, no. 1 (2025): 49. https://doi.org/10.31000/bvaj.v9i1.14054.

Full text

Abstract:

The objective of this research is to analysis the effect of auditor characteristics that represented by professional scepticism and auditor experiences toward financial statement fraud detection through artificial intelligence in data analytic. This study builds upon previous research where respondents were auditors who had used big data analytics and incorporate artificial intelligence, specifically big data analytic as mediator between auditor characteristics and the detection of financial statement fraud. Quantitative research was used in this research by obtaining primary data through 66 r

APA, Harvard, Vancouver, ISO, and other styles

26

Thakkar, Dr Himanshu, Dr Siddharth Dabhade, Ms Gopika Gopan, and Ms Anshu Singh. "Study on the Application of Forensic Analytics in Early-Stage Occupational Fraud Detection." GNLU Journal for Law and Economics VII, no. I (2024): 47–74. http://dx.doi.org/10.69893/gjle.2024.000062.

Full text

Abstract:

Occupational fraud is the most prevalent threat affecting developed and developing countries. According to the Association of Certified Fraud Examiners, India has the highest occupational fraud rate among Southern Asian countries. As technology advances, criminals are looking for innovative ways to commit crimes. The effects of fraud on companies include loss of reputation, weakening of investors’ confidence, reduction of profit, and lowering moral values of employees. The study aims to evaluate the most recent preventative measures organizations have implemented to reduce occupational fraud.

APA, Harvard, Vancouver, ISO, and other styles

27

Esther, .A. Makandah, Emmanuel Aniebonam Ebuka, Blossom Adesuwa Okpeseyi Similoluwa, and Ololade Waheed Oyindamola. "AI-Driven Predictive Analytics for Fraud Detection in Healthcare: Developing a Proactive Approach to Identify and Prevent Fraudulent Activities." International Journal of Innovative Science and Research Technology (IJISRT) 10, no. 1 (2025): 1521–29. https://doi.org/10.5281/zenodo.14769423.

Full text

Abstract:

The financial stability and operational effectiveness of healthcare systems around the world are threatened by the widespread problem of healthcare fraud. Conventional fraud detection systems, which rely on human investigations and retroactive audits, are unable to adequately meet the complexity and expansion of modern fraud schemes which have evolved over the years. The aim of this research is to examine the potential of AI-driven predictive analytics in preventing healthcare fraud, focusing on the development of proactive initiatives to identify and prevent healthcare-related fraudulent acti

APA, Harvard, Vancouver, ISO, and other styles

28

Gupta, Meenu, Pradeep Kumar Aggarwal, and Rekha Gupta. "Revitalizing the Forensic Accounting: An Exploratory Study on Mitigating the Financial Risk using Data Analytics." International Journal of Experimental Research and Review 41, Spl Vol (2024): 227–38. http://dx.doi.org/10.52756/ijerr.2024.v41spl.019.

Full text

Abstract:

Risk mitigation and fraud prevention in the present times has more focus on digital metadata, wherein forensic accountants are required to make use of robust IT techniques and tools. Data Analytics has got huge implications for forensic accounting. With the ever-increasing electronic element of frauds in the present age of digitalization, and complexity of financial transactions and instruments, the challenges are growing multifold for the forensic accounting profession. The use of data analytics techniques, which enables the processing of large data in almost no time, simplify the task of for

APA, Harvard, Vancouver, ISO, and other styles

29

Philip Olaseni Shoetan, Adedoyin Tolulope Oyewole, Chinwe Chinazo Okoye, and Onyeka Chrisanctus Ofodile. "REVIEWING THE ROLE OF BIG DATA ANALYTICS IN FINANCIAL FRAUD DETECTION." Finance & Accounting Research Journal 6, no. 3 (2024): 384–94. http://dx.doi.org/10.51594/farj.v6i3.899.

Full text

Abstract:

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 comp

APA, Harvard, Vancouver, ISO, and other styles

30

Ezekiel Onyekachukwu Udeh, Prisca Amajuoyi, Kudirat Bukola Adeusi, and Anwulika Ogechukwu Scott. "The role of big data in detecting and preventing financial fraud in digital transactions." World Journal of Advanced Research and Reviews 22, no. 2 (2024): 1746–60. http://dx.doi.org/10.30574/wjarr.2024.22.2.1575.

Full text

Abstract:

In the era of digital transactions, the proliferation of financial fraud poses significant challenges to the security and integrity of financial systems worldwide. Amidst this landscape, the role of big data has emerged as a critical tool for detecting and preventing financial fraud in digital transactions. This Review explores the multifaceted role of big data in combating financial fraud, highlighting its capabilities in identifying fraudulent patterns, enhancing risk assessment models, and enabling real-time fraud detection mechanisms. Big data analytics leverage vast volumes of structured

APA, Harvard, Vancouver, ISO, and other styles

31

Ezekiel, Onyekachukwu Udeh, Amajuoyi Prisca, Bukola Adeusi Kudirat, and Ogechukwu Scott Anwulika. "The role of big data in detecting and preventing financial fraud in digital transactions." World Journal of Advanced Research and Reviews 22, no. 2 (2024): 1746–60. https://doi.org/10.5281/zenodo.14709647.

Full text

Abstract:

In the era of digital transactions, the proliferation of financial fraud poses significant challenges to the security and integrity of financial systems worldwide. Amidst this landscape, the role of big data has emerged as a critical tool for detecting and preventing financial fraud in digital transactions. This Review explores the multifaceted role of big data in combating financial fraud, highlighting its capabilities in identifying fraudulent patterns, enhancing risk assessment models, and enabling real-time fraud detection mechanisms. Big data analytics leverage vast volumes of structured

APA, Harvard, Vancouver, ISO, and other styles

32

Perols, Johan L., Robert M. Bowen, Carsten Zimmermann, and Basamba Samba. "Finding Needles in a Haystack: Using Data Analytics to Improve Fraud Prediction." Accounting Review 92, no. 2 (2016): 221–45. http://dx.doi.org/10.2308/accr-51562.

Full text

Abstract:

ABSTRACT Developing models to detect financial statement fraud involves challenges related to (1) the rarity of fraud observations, (2) the relative abundance of explanatory variables identified in the prior literature, and (3) the broad underlying definition of fraud. Following the emerging data analytics literature, we introduce and systematically evaluate three data analytics preprocessing methods to address these challenges. Results from evaluating actual cases of financial statement fraud suggest that two of these methods improve fraud prediction performance by approximately 10 percent re

APA, Harvard, Vancouver, ISO, and other styles

33

Courage Idemudia, Edith Ebele Agu, and Shadrack Obeng. "Analyzing how data analytics is used in detecting and preventing fraudulent health insurance claims." International Journal of Frontiers in Science and Technology Research 7, no. 1 (2024): 048–56. http://dx.doi.org/10.53294/ijfstr.2024.7.1.0045.

Full text

Abstract:

Health insurance fraud poses significant financial and operational challenges, necessitating the implementation of advanced data analytics for effective detection and prevention. This review explores the various techniques employed in data analytics to identify and mitigate fraudulent health insurance claims. Descriptive analytics aids in uncovering patterns and anomalies in historical claims data, while predictive analytics leverages statistical models and machine learning to forecast potential fraud. Advanced techniques, including machine learning and artificial intelligence, facilitate real

APA, Harvard, Vancouver, ISO, and other styles

34

Vuppala, Sashi Kiran. "Predictive Analytics for Fraud Detection in Reinsurance Claims: Enhancing Early Detection and Decision-Making Through Data Intelligence." International Scientific Journal of Engineering and Management 04, no. 02 (2025): 1–7. https://doi.org/10.55041/isjem02259.

Full text

Abstract:

Fraudulent claims pose a significant threat to the financial stability of the reinsurance industry, necessitating more proactive and intelligent detection mechanisms. This paper explores the application of predictive analytics to identify and mitigate fraudulent activities in reinsurance claims. By leveraging machine learning models, historical claims data, and anomaly detection techniques, predictive analytics can uncover subtle patterns and indicators of potential fraud that traditional methods often miss. The study demonstrates how predictive models enable early identification of high-risk

APA, Harvard, Vancouver, ISO, and other styles

35

Gupta, Pankaj. "Securing Tomorrow: The Intersection of AI, Data, and Analytics in Fraud Prevention." Asian Journal of Research in Computer Science 17, no. 3 (2024): 75–92. http://dx.doi.org/10.9734/ajrcos/2024/v17i3425.

Full text

Abstract:

Aim: This research investigates the interconnections among Data Analytics, Artificial Intelligence, and other cutting-edge technologies to enhance comprehension of fraud prevention. The advantages of integrating machine learning and data analytics into artificial intelligence systems for industry-wide fraud detection and prevention are examined in this study. Study Design: My approach involved conducting an extensive examination of existing literature and analysing numerous case studies to gather information on the role of artificial intelligence, data, and analytics in fraud prevention. Place

APA, Harvard, Vancouver, ISO, and other styles

36

Adetumi Adewumi, Somto Emmanuel Ewim, Ngodoo Joy Sam-Bulya, and Olajumoke Bolatito Ajani. "Enhancing financial fraud detection using adaptive machine learning models and business analytics." International Journal of Scientific Research Updates 8, no. 2 (2024): 012–21. http://dx.doi.org/10.53430/ijsru.2024.8.2.0054.

Full text

Abstract:

Financial fraud continues to be a critical threat to businesses and economies worldwide, necessitating advanced detection techniques. This paper reviews the role of adaptive machine learning (ML) models and business analytics in enhancing fraud detection systems. Traditional fraud detection methods often fall short in addressing the complexity and evolving nature of fraudulent activities, making adaptive ML models, such as decision trees and neural networks, more effective in identifying subtle patterns in large datasets. Organizations can refine ML models by integrating business analytics, en

APA, Harvard, Vancouver, ISO, and other styles

37

Benjamin Samson Ayinla, Onyeka Franca Asuzu, Ndubuisi Leonard Ndubuisi, Chinedu Ugochukwu Ike, Akoh Atadoga, and Rhoda Adura Adeleye. "Utilizing data analytics for fraud detection in accounting: A review and case studies." International Journal of Science and Research Archive 11, no. 1 (2024): 1348–63. http://dx.doi.org/10.30574/ijsra.2024.11.1.0221.

Full text

Abstract:

This research paper offers a comprehensive exploration of the evolving landscape of fraud detection strategies within the accounting sector, driven by the integration of data analytics, machine learning, and big data technologies. The study aims to investigate, analyze, and provide insights into the practical application, challenges, and implications of these advanced technologies in fraud detection. Through an extensive literature review, a range of case studies, and a comparative analysis of methodologies, this paper delves into the key aspects of data-driven fraud detection. The literature

APA, Harvard, Vancouver, ISO, and other styles

38

Malkoochi, Ramchander. "Confidential Computing for Privacy-Preserving Fraud Analytics." European Journal of Computer Science and Information Technology 13, no. 24 (2025): 115–228. https://doi.org/10.37745/ejcsit.2013/vol13n24115228.

Full text

Abstract:

Confidential computing represents a transformative paradigm in fraud analytics, providing robust protection for sensitive financial data throughout the processing lifecycle. By leveraging Trusted Execution Environments (TEEs) such as Intel SGX and AMD SEV, financial institutions can analyze transaction patterns, detect anomalies, and collaborate across organizational boundaries while maintaining data confidentiality. The technology addresses the fundamental tension between effective fraud detection and privacy protection through hardware-based isolation mechanisms that secure data even during

APA, Harvard, Vancouver, ISO, and other styles

39

Oluwatosin Ilori, Nelly Tochi Nwosu, and Henry Nwapali Ndidi Naiho. "Advanced data analytics in internal audits: A conceptual framework for comprehensive risk assessment and fraud detection." Finance & Accounting Research Journal 6, no. 6 (2024): 931–52. http://dx.doi.org/10.51594/farj.v6i6.1213.

Full text

Abstract:

In the face of increasing complexity and rapid technological advancements, traditional internal audit methods are becoming inadequate for comprehensive risk assessment and effective fraud detection. Advanced data analytics offers a transformative approach that enhances the effectiveness of internal audits. This concept paper presents a framework for integrating advanced data analytics into internal audit processes, aiming to provide more robust risk management and improved fraud detection capabilities. Integrate diverse data sources, including financial, operational, and external data, to prov

APA, Harvard, Vancouver, ISO, and other styles

40

Simanjuntak, Rebeca boru, and Rita Nian Jubata Mare. "Analisis faktor yang memengaruhi kemampuan auditor dalam mendeteksi kecurangan: Kajian literatur sistematis." Journal of Accounting and Digital Finance 5, no. 2 (2025): 159–67. https://doi.org/10.53088/jadfi.v5i2.1799.

Full text

Abstract:

This study identifies and analyzes factors influencing auditors' fraud detection capabilities through a systematic literature review following PRISMA protocol. A comprehensive review was conducted on 23 primary studies (2013-2025) from 856 initially identified articles through systematic screening across leading academic databases. Thematic analysis identified two primary categories affecting fraud detection capabilities. Internal factors include professional skepticism, which correlates positively with fraud detection effectiveness, specific audit experience, knowledge of fraud schemes, analy

APA, Harvard, Vancouver, ISO, and other styles

41

Uchhana, Nayan, Ravi Ranjan, Shashank Sharma, Deepak Agrawal, and Anurag Punde. "Literature Review of Different Machine Learning Algorithms for Credit Card Fraud Detection." International Journal of Innovative Technology and Exploring Engineering 10, no. 6 (2021): 101–8. http://dx.doi.org/10.35940/ijitee.c8400.0410621.

Full text

Abstract:

Every year fraud cost generated in the economy is more than $4 trillion internationally. This is unsurprising, as the return on investment for fraud can be massive. Cybercrime specialists estimate that an investment of 1 million dollars into fraud or attack can net up to $100 million. Financial institutions such as commercial and investment banking operations are increasingly being targeted. And we know that the only way to fight fraud effectively is through the use of advanced technology. The answer lies in relying on advanced analytics and enterprisewide data storage capabilities that suppor

APA, Harvard, Vancouver, ISO, and other styles

42

Nayan, Uchhana, Ranjan Ravi, Sharma Shashank, Agrawal Deepak, and Punde Anurag. "Literature Review of Different Machine Learning Algorithms for Credit Card Fraud Detection." International Journal of Innovative Technology and Exploring Engineering (IJITEE) 10, no. 6 (2021): 101–8. https://doi.org/10.35940/ijitee.C8400.0410621.

Full text

Abstract:

Every year fraud cost generated in the economy is more than $4 trillion internationally. This is unsurprising, as the return on investment for fraud can be massive. Cybercrime specialists estimate that an investment of 1 million dollars into fraud or attack can net up to $100 million. Financial institutions such as commercial and investment banking operations are increasingly being targeted. And we know that the only way to fight fraud effectively is through the use of advanced technology. The answer lies in relying on advanced analytics and enterprisewide data storage capabilities that suppor

APA, Harvard, Vancouver, ISO, and other styles

43

Tripathi, Rajeev, and Smita Tripathi. "Identifying Fraud Detection Techniques Using Text Analytics Processing." ADHYAYAN: A JOURNAL OF MANAGEMENT SCIENCES 13, no. 01 (2023): 5–8. http://dx.doi.org/10.21567/adhyayan.v13i1.02.

Full text

Abstract:

To determine the fraud detection model, to illustrate how the fraud detection model is created, and to start the data model with any classifier, data mining technology is used in the fraud detection process. As e-commerce continues to grow, the associated internet hoax is still a very appealing source of cash for scammers. Because of the severe financial damage that this counterfeit activity does to retailers, online fraud detection is essential. Concerned with scam detection is the need to quickly seize fraudulent actions in addition to containing them. This significance is essential to reduc

APA, Harvard, Vancouver, ISO, and other styles

44

Rajkumar, Govindaswamy Subbian. "Technology Driven Intelligent Risk & Fraud Assessment in Insurance." International Journal of Innovative Science and Research Technology (IJISRT) 10, no. 2 (2025): 686–93. https://doi.org/10.5281/zenodo.14928754.

Full text

Abstract:

Technology Driven Intelligent Risk & Fraud Assessment in Insurance focuses on leveraging artificial intelligence (AI), machine learning (ML), blockchain, and predictive analytics to improve risk assessment and combat fraud. The study highlights the role of AI-driven predictive analytics, deep learning algorithms, blockchain for transparency, and automation to enhance accuracy, reduce fraudulent activities, and streamline insurance workflows. The approach analyzed real-world case study demonstrated the successful integration of these technologies into Guidewire ClaimCenter and PolicyCenter,

APA, Harvard, Vancouver, ISO, and other styles

45

Haddab, Daniella Maya. "Detecting banking frauds with analytics and machine learning." Business & IT XIII, no. 1 (2023): 90–96. http://dx.doi.org/10.14311/bit.2023.01.11.

Full text

Abstract:

Bank fraud is the bodily loss of a Bank or maybe the loss of very sensitive info. For detection, there are lots of machine learning algorithms which can be used. The study shows many algorithms which could be used for deciding transactions as fraud or perhaps real. The information set employed in Bank fraud Detection was utilized in the research. The SMOTE method was used for oversampling, since the dataset was incredibly imbalanced. Moreover, include choice was performed, and the set was divided into two parts, test data and instruction information. The algorithms used in this study were Logi

APA, Harvard, Vancouver, ISO, and other styles

46

Pamisetty, Vamsee. "Big Data and Predictive Analytics in Government Finance: Transforming Fraud Detection and Fiscal Oversight." International Journal of Engineering and Computer Science 10, no. 12 (2021): 25731–55. https://doi.org/10.18535/ijecs.v10i12.4680.

Full text

Abstract:

Governments around the world are experimenting with Big Data and predictive analytics. They deploy various software applications like predictive policing, fraud detection, and capacity demand prediction while at the same time developing and investing in broader data analytical infrastructure and analytical skill sets. Implementing Big Data and predictive analytics can be a challenging endeavor, however, as these analytics often rely on open-source algorithms that are unsupervised and black-boxed. Equally challenging is how government institutions endeavor to use a waterfall approach from explo

APA, Harvard, Vancouver, ISO, and other styles

47

Moradi, Mohsen, and Seyed Mohammad Fateminejad. "Sharing and Analyzing Data to Reduce Insurance Fraud." Journal of Management and Accounting Studies 5, no. 03 (2019): 96–100. http://dx.doi.org/10.24200/jmas.vol5iss03pp96-100.

Full text

Abstract:

Insurance fraud is a multi-billion-dollar problem. Fraudulent practices occur frequently and often repeatedly. Fraud can be detected and prevented if appropriate data is collected, analyzed and shared among insurance companies.Methodology:Appropriate decision support and analytics can be developed to routinize fraud detection. Creating these decision support capabilities involves addressing managerial, technological, and data ownership issues.This article examines these issues in the context of using new data sources and predictive analytics to both reduce insurance fraud and improve customer

APA, Harvard, Vancouver, ISO, and other styles

48

Dzomira, Shewangu. "Digital forensic technologies as e-fraud risk mitigation tools in the banking industry: Evidence from Zimbabwe." Risk Governance and Control: Financial Markets and Institutions 4, no. 2 (2014): 116–24. http://dx.doi.org/10.22495/rgcv4i2c1art4.

Full text

Abstract:

The paper investigates digital analytical tools and technologies used in electronic fraud prevention and detection, used in the banking industry. The paper is based on a descriptive study which studied digital forensics and cyber fraud phenomenon using content analysis. To obtain the data questionnaires and interviews were administered to the selected informants from 22 banks. Convenience and judgemental sampling techniques were used. It was found out that fraud detection and prevention tools and technologies would be most effective way of combating e-fraud if they can be utilized. It is concl

APA, Harvard, Vancouver, ISO, and other styles

49

Samuel Sarpong Baah, Harold Tobias Adu-Twum, Samuel Owusu Adjei, Godwin Ampadu, Awofadeju Martins O, and Beryl Fonkem. "Leveraging big data analytics to combat emerging financial fraud schemes in the USA: A literature review and practical implications." World Journal of Advanced Research and Reviews 24, no. 1 (2024): 017–43. http://dx.doi.org/10.30574/wjarr.2024.24.1.2999.

Full text

Abstract:

The rise of emerging financial fraud schemes in the USA has presented significant challenges for financial institutions, regulators, and law enforcement agencies. As fraud tactics become increasingly sophisticated, traditional methods of detection and prevention are proving insufficient. This literature review explores the role of big data analytics as a critical tool in combating financial fraud, focusing on its practical applications and effectiveness. By leveraging machine learning, predictive analytics, and artificial intelligence, big data analytics enables real-time monitoring and detect

APA, Harvard, Vancouver, ISO, and other styles

50

Samuel, Sarpong Baah, Tobias Adu-Twum Harold, Owusu Adjei Samuel, Ampadu Godwin, Martins O. Awofadeju, and Fonkem Beryl. "Leveraging big data analytics to combat emerging financial fraud schemes in the USA: A literature review and practical implications." World Journal of Advanced Research and Reviews 24, no. 1 (2024): 017–43. https://doi.org/10.5281/zenodo.15004048.

Full text

Abstract:

The rise of emerging financial fraud schemes in the USA has presented significant challenges for financial institutions, regulators, and law enforcement agencies. As fraud tactics become increasingly sophisticated, traditional methods of detection and prevention are proving insufficient. This literature review explores the role of big data analytics as a critical tool in combating financial fraud, focusing on its practical applications and effectiveness. By leveraging machine learning, predictive analytics, and artificial intelligence, big data analytics enables real-time monitoring and detect

APA, Harvard, Vancouver, ISO, and other styles

We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!