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Reseach Article

Real-Time Analysis and Decision on Fraud Detection using Pega

by Praveen Kumar Tammana
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 186 - Number 3
Year of Publication: 2024
Authors: Praveen Kumar Tammana

Praveen Kumar Tammana . Real-Time Analysis and Decision on Fraud Detection using Pega. International Journal of Computer Applications. 186, 3 ( Jan 2024), 14-21. DOI=10.5120/ijca2024923362

@article{ 10.5120/ijca2024923362,
author = { Praveen Kumar Tammana },
title = { Real-Time Analysis and Decision on Fraud Detection using Pega },
journal = { International Journal of Computer Applications },
issue_date = { Jan 2024 },
volume = { 186 },
number = { 3 },
month = { Jan },
year = { 2024 },
issn = { 0975-8887 },
pages = { 14-21 },
numpages = {9},
url = { },
doi = { 10.5120/ijca2024923362 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2024-02-07T01:29:36.679939+05:30
%A Praveen Kumar Tammana
%T Real-Time Analysis and Decision on Fraud Detection using Pega
%J International Journal of Computer Applications
%@ 0975-8887
%V 186
%N 3
%P 14-21
%D 2024
%I Foundation of Computer Science (FCS), NY, USA

In the rapidly evolving digital landscape, the threat of financial fraud has escalated, posing significant challenges for businesses and financial institutions. Traditional methods of fraud detection often lag behind the sophisticated tactics employed by fraudsters, leading to increased financial losses and compromised customer trust. This paper delves into the transformative potential of Pega Process AI in revolutionizing fraud detection through real-time analysis and decisioning. Pega Process AI, with its advanced artificial intelligence and machine learning capabilities, offers a proactive and efficient approach to identifying and mitigating fraudulent activities. This study explores the mechanism by which Pega Process AI processes vast volumes of transactional data in real-time, employing predictive analytics, dynamic rule adjustment, and context-aware decisioning to detect and prevent fraud. By highlighting the system's ability to adapt to evolving fraud patterns and integrate seamlessly with existing transaction systems, the paper underscores the enhanced accuracy, reduced false positives, and operational efficiency afforded by this technology. The implications of implementing Pega Process AI in real-world scenarios are examined, showcasing its effectiveness in safeguarding assets while ensuring a positive customer experience. This research contributes to the understanding of how real-time AI-driven systems like Pega Process AI are pivotal in the modern fight against financial fraud, marking a significant leap over traditional fraud detection methodologies.

  1. N. Senam and C. Okonji, “The use of social media platforms as awareness creation tools for the hepatitis B virus in Lagos State,” International Journal of Research and Innovation in Social Science, vol. 05, no. 02, pp. 04–12, 2021. doi:10.47772/ijriss.2021.5201
  2. D. Banulescu‐Radu and M. Yankol‐Schalck, “Practical guideline to efficiently detect insurance fraud in the era of Machine Learning: A household insurance case,” Journal of Risk and Insurance, pp. 517–538, 2023. doi:10.1111/jori.12452
  3. S. Kodate, R. Chiba, S. Kimura, and N. Masuda, “Detecting problematic transactions in a consumer-to-consumer e-commerce network,” Applied Network Science, vol. 5, no. 1, 2020. doi:10.1007/s41109-020-00330-x
  4. M. Taher, H. Guermah, and M. Nassar, “MCDM method for financial fraud detection,” Proceedings of the 4th International Conference on Big Data and Internet of Things, 2019. doi:10.1145/3372938.3372949
  5. B. Benedek, C. Ciumas, and B. Z. Nagy, “On the cost-efficiency of automobile insurance fraud detection methods: A meta-analysis,” Global Business Review, pp. 325–340, 2023. doi:10.1177/09721509231158194
  6. W. Xu, S. Wang, D. Zhang, and B. Yang, “Random rough subspace based neural network ensemble for Insurance Fraud Detection,” 2011 Fourth International Joint Conference on Computational Sciences and Optimization, 2011. doi:10.1109/cso.2011.213
  7. A. Gepp, J. H. Wilson, K. Kumar, and S. Bhattacharya, “A comparative analysis of decision trees vis-`a-vis other computational data mining techniques in Automotive Insurance Fraud Detection,” Journal of Data Science, vol. 10, no. 3, pp. 537–561, 2021. doi:10.6339/jds.201207_10(3).0010
  8. J. M. Pérez, J. Muguerza, O. Arbelaitz, I. Gurrutxaga, and J. I. Martín, “Consolidated Tree Classifier learning in a car insurance fraud detection domain with class imbalance,” Pattern Recognition and Data Mining, pp. 381–389, 2005. doi:10.1007/11551188_41
  9. M. Artı́s, M. Ayuso, and M. Guillén, “Modelling different types of automobile insurance fraud behaviour in the Spanish market,” Insurance: Mathematics and Economics, vol. 24, no. 1–2, pp. 67–81, 1999. doi:10.1016/s0167-6687(98)00038-9
  10. Insurance fraud — FBI, (accessed Jan. 16, 2024).
  11. “Pega claims optimization,” Pega, (accessed Jan. 17, 2024).
  12. “AI-powered decisioning to elevate every outcome,” Pega, (accessed Jan. 17, 2024).
  13. “Optimize process automation with Pega process AI,” Pega, (accessed Jan. 17, 2024).
Index Terms

Computer Science
Information Sciences


Real-Time Analysis Fraud Detection Decision Making Pega Platform Predictive Analytics