This paper is published in Volume-11, Issue-2, 2025
Area
Big Data Analytics
Author
Shaba Khatoon, Ankita Srivastava, Dr. Shish Ahmad
Org/Univ
Integral University, Lucknow, India
Pub. Date
16 April, 2025
Paper ID
V11I2-1151
Publisher
Keywords
Artificial Intelligence (AI), Intelligent Document Processing (IDP), Machine Learning (ML), Natural Language Processing (NLP), Fraud Detection, Risk Assessment, Financial Technology (FinTech), Regulatory Compliance, Cybersecurity, Automation in Healthcare, AI in Insurance.

Citationsacebook

IEEE
Shaba Khatoon, Ankita Srivastava, Dr. Shish Ahmad . Big Data Analytics for Real-Time Fraud Detection in Insurance Claims, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.

APA
Shaba Khatoon, Ankita Srivastava, Dr. Shish Ahmad (2025). Big Data Analytics for Real-Time Fraud Detection in Insurance Claims. International Journal of Advance Research, Ideas and Innovations in Technology, 11(2) www.IJARIIT.com.

MLA
Shaba Khatoon, Ankita Srivastava, Dr. Shish Ahmad . "Big Data Analytics for Real-Time Fraud Detection in Insurance Claims." International Journal of Advance Research, Ideas and Innovations in Technology 11.2 (2025). www.IJARIIT.com.

Abstract

The integration of Artificial Intelligence (AI) and Big Data Analytics is revolutionizing industries by optimizing efficiency, accuracy, and security. In healthcare and insurance, AI-driven intelligent Document Processing (IDP) automates workflows such as claims automation, medical data extraction, and regulatory compliance management. By utilizing Machine Learning (ML), Natural Language Processing (NLP), and Optical Character Recognition (OCR), IDP accelerates document classification, data validation, and anomaly detection, reducing errors by 90% and cutting processing time by 80%. In the financial sector, AI enhances fraud analytics, risk modeling, and compliance monitoring. Advanced deep learning architectures, pattern recognition, and predictive analytics improve credit risk assessment and real-time fraud mitigation. AI-powered anomaly detection techniques identify suspicious transactions, reducing cybersecurity threats and financial fraud losses.