Deep learning-enhanced accessibility compliance automation for web-based insurance platforms
Quality Engineering Associate Manager, Accenture, Aubrey, Texas.
Research Article
World Journal of Advanced Research and Reviews, 2024, 21(02), 2125-2132
Publication history:
Received on 02 January 2024; revised on 21 February 2024; accepted on 27 February 2024
Abstract:
Digital accessibility compliance in insurance platforms presents complex challenges requiring sophisticated technical solutions to ensure WCAG 2.1 AA compliance while maintaining optimal user experience. This paper presents an innovative deep learning-enhanced automation framework for comprehensive accessibility testing and compliance verification in web-based insurance applications. The proposed system utilizes computer vision techniques combined with natural language processing to automatically identify accessibility violations, suggest remediation strategies, and validate compliance across diverse user interaction scenarios. Our approach employs Convolutional Neural Networks (CNNs) for visual element analysis, Recurrent Neural Networks (RNNs) for content structure evaluation, and transformer models for semantic understanding of accessibility requirements. The framework integrates with popular assistive technologies including NVDA, JAWS, and Dragon NaturallySpeaking to simulate real-world usage patterns and validate compliance effectiveness. Implementation results from Guidewire PolicyCenter and ClaimCenter applications demonstrate 89% accuracy in automated accessibility violation detection, 76% reduction in manual accessibility testing effort, and 94% compliance achievement rate. The system incorporates advanced image processing algorithms to analyze color contrast ratios, visual hierarchy, and interactive element accessibility. Machine learning models are trained on extensive datasets comprising accessibility patterns, user behavior analytics, and assistive technology interaction logs. The research addresses critical gaps in automated accessibility testing including dynamic content evaluation, complex user workflow accessibility, and multi-modal interaction validation. Performance benchmarking against commercial accessibility testing tools shows superior detection rates for complex violations and reduced false positive occurrences. The proposed solution provides actionable insights for development teams through intelligent reporting mechanisms and integration with popular development workflows, significantly improving accessibility compliance efficiency in enterprise insurance applications.
Keywords:
Deep Learning; Web Accessibility; WCAG Compliance; Insurance Platforms; Automated Testing; Assistive Technology
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Copyright information:
Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0