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A Risk Intelligence Framework for Detecting and Preventing Financial Fraud in Digital Marketplaces

Emmanuel Damilare Balogun Kolade Olusola Ogunsola Adebanji Samuel Ogunmokun

Subject area: Science,Engineering and Technology  ·  Area of research: Risk Intelligence Framework

Abstract

The rise of digital marketplaces has significantly increased the risk of financial fraud, necessitating the development of advanced risk intelligence frameworks to detect and mitigate fraudulent activities effectively. Traditional fraud prevention methods have proven inadequate against evolving threats such as payment fraud, identity theft, chargeback fraud, and synthetic identity fraud. This paper comprehensively analyzes fraud typologies, key fraud techniques, regulatory considerations, and the role of artificial intelligence (AI), machine learning, and blockchain technology in fraud detection. A robust risk intelligence framework is proposed, emphasizing data-driven risk assessment, behavioral analytics, anomaly detection algorithms, real-time fraud monitoring, and blockchain-based transaction transparency. The study explores the implementation strategies and challenges associated with adopting such a framework, including data privacy concerns, regulatory compliance complexities, ethical considerations in AI-driven fraud detection, and cross-border fraud enforcement challenges. Furthermore, this paper offers strategic recommendations for policymakers and industry stakeholders, advocating for standardized fraud prevention regulations, cross-industry intelligence-sharing initiatives, and privacy-preserving fraud detection models. Future advancements in quantum-resistant fraud detection, AI-driven RegTech solutions, and decentralized authentication methods are also discussed. Financial institutions and digital marketplace operators can build resilient, transparent, and adaptive fraud prevention systems by addressing these challenges and leveraging cutting-edge technologies.

Keywords

Financial Fraud Detection, Risk Intelligence Framework, AI-Powered Fraud Prevention, Blockchain and Transaction Security

References

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How to cite this paper

Emmanuel Damilare Balogun, Kolade Olusola Ogunsola, Adebanji Samuel Ogunmokun "A Risk Intelligence Framework for Detecting and Preventing Financial Fraud in Digital Marketplaces" Iconic Research And Engineering Journals Volume 4 Issue 8 2021 Page 134-149
Emmanuel Damilare Balogun, Kolade Olusola Ogunsola, Adebanji Samuel Ogunmokun "A Risk Intelligence Framework for Detecting and Preventing Financial Fraud in Digital Marketplaces" Iconic Research And Engineering Journals, vol. 4, no. 8, Feb. 2021
Emmanuel Damilare Balogun, Kolade Olusola Ogunsola, Adebanji Samuel Ogunmokun (2021). A Risk Intelligence Framework for Detecting and Preventing Financial Fraud in Digital Marketplaces. Iconic Research And Engineering Journals, 4(8).
Emmanuel Damilare Balogun, Kolade Olusola Ogunsola, Adebanji Samuel Ogunmokun "A Risk Intelligence Framework for Detecting and Preventing Financial Fraud in Digital Marketplaces" Iconic Research And Engineering Journals, vol. 4, no. 8, Feb. 2021.
@article{1702600,
      author = {Emmanuel Damilare Balogun, Kolade Olusola Ogunsola, Adebanji Samuel Ogunmokun},
      title = {A Risk Intelligence Framework for Detecting and Preventing Financial Fraud in Digital Marketplaces},
      journal = {Iconic Research And Engineering Journals},
      year = {2021},
      volume = {4},
      number = {8},
      pages = {134-149},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1702600.pdf},
      abstract = {The rise of digital marketplaces has significantly increased the risk of financial fraud, necessitating the development of advanced risk intelligence frameworks to detect and mitigate fraudulent activities effectively. Traditional fraud prevention methods have proven inadequate against evolving threats such as payment fraud, identity theft, chargeback fraud, and synthetic identity fraud. This paper comprehensively analyzes fraud typologies, key fraud techniques, regulatory considerations, and the role of artificial intelligence (AI), machine learning, and blockchain technology in fraud detection. A robust risk intelligence framework is proposed, emphasizing data-driven risk assessment, behavioral analytics, anomaly detection algorithms, real-time fraud monitoring, and blockchain-based transaction transparency. The study explores the implementation strategies and challenges associated with adopting such a framework, including data privacy concerns, regulatory compliance complexities, ethical considerations in AI-driven fraud detection, and cross-border fraud enforcement challenges. Furthermore, this paper offers strategic recommendations for policymakers and industry stakeholders, advocating for standardized fraud prevention regulations, cross-industry intelligence-sharing initiatives, and privacy-preserving fraud detection models. Future advancements in quantum-resistant fraud detection, AI-driven RegTech solutions, and decentralized authentication methods are also discussed. Financial institutions and digital marketplace operators can build resilient, transparent, and adaptive fraud prevention systems by addressing these challenges and leveraging cutting-edge technologies.},
      keywords = {Financial Fraud Detection, Risk Intelligence Framework, AI-Powered Fraud Prevention, Blockchain and Transaction Security},
      month = {February},
  }