Real-Time AI-Based Analytics for Healthcare and Finance through Secure API Integration Using Deep Learning and CNN

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Published: 2023-09-11
DOI: https://doi.org/10.15662/IJRPETM.2023.0605009
Keywords:
AI analytics, real-time data processing, healthcare data, financial analytics, CNN, secure API integration, anomaly detection

Main Article Content

S.Saravana Kumar
Department of Computer Science and Engineering, CMR University, Bengaluru, India

Abstract

The rapid growth of healthcare and financial data streams demands real-time analytics frameworks that are both intelligent and secure. This paper presents a real-time AI-based analytics framework for healthcare and financial systems through secure API integration using deep learning and Convolutional Neural Networks (CNN). The proposed platform leverages cloud-based architectures and secure API mechanisms to enable seamless, low-latency data ingestion, processing, and model deployment across heterogeneous data sources. Deep learning models, particularly CNN-based architectures, are employed to extract high-level features, identify patterns, and detect anomalies in real-time healthcare and financial datasets. To ensure data confidentiality, integrity, and compliance, the framework incorporates authentication, encryption, and access control within the API layer. Experimental evaluation demonstrates improved analytical accuracy, reduced response latency, and enhanced system scalability compared to traditional batch-processing and non-AI approaches. The results highlight the effectiveness of secure API–driven AI analytics in supporting timely clinical decision-making and financial risk mitigation. This study provides a scalable and secure foundation for deploying AI-powered real-time analytics in data-sensitive healthcare and financial environments.

Article Details

Issue

Vol. 6 No. 5 (2023): International Journal of Research Publications in Engineering, Technology and Management

Section

Articles

How to Cite

Real-Time AI-Based Analytics for Healthcare and Finance through Secure API Integration Using Deep Learning and CNN. (2023). International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 6(5), 9337-9342. https://doi.org/10.15662/IJRPETM.2023.0605009

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