Ai-Enhanced Web Accessibility Testing for Financial Web Applications: A Design Framework with Explainable Ai

Manu Prasad Prakash Bhavan Siva Prasad^1,

¹Independent Researcher, United States of America

Cite this paper:
Manu Prasad Prakash Bhavan Siva Prasad. Ai-Enhanced Web Accessibility Testing for Financial Web Applications: A Design Framework with Explainable Ai. American Journal of Software Engineering. 2025; 8(1):1-7. doi: 10.12691/ajse-8-1-1

Abstract

Web accessibility stands as a fundamental requirement for inclusive financial services because people with disabilities need to access online banking and trading platforms and financial services. The W3C (2025) maintains WCAG 2.1 as a global standard which provides web designers with accessible design guidelines while U.S. and EU regulators enforce compliance through ADA guidance and the 2024 EU Accessibility Act . The majority of websites contain accessibility barriers because research shows that 95% of 1,000,000 websites fail to meet accessibility standards and financial websites become targets for legal actions and regulatory investigations because of their inaccessible design . The process of testing web applications for accessibility through manual methods and rule-based systems (WAVE and axe) proves to be time-consuming and restricted in its capabilities. The research introduces FinAccAI as an AI system which performs automated accessibility testing for financial web applications. The system design implements rule-based verification for WCAG requirements including alt-text and label presence alongside machine learning and deep learning content analysis. The system uses a conceptual algorithm to extract HTML data and perform CNN/NLP-based visual analysis of charts before identifying accessibility problems. The system includes an Explainable AI (XAI) framework which generates human-friendly explanations for each detected violation through LIME/SHAP feature attribution methods . The research evaluates three testing methods (rule-based and ML and DL) through their ability to detect problems and their scope of detection while previous research demonstrates AI systems can decrease human work requirements . The AI-enhanced testing method demonstrates potential to achieve expert-level results according to our conceptual assessment which draws from research on finance site auditing .The system requires additional research to address model bias and generative errors which affect its performance while maintaining open-source model availability for reproducibility. The proposed system creates essential components for automated accessibility testing in financial services which helps in both regulatory compliance as well as business development. The research demonstrates that AI-based accessibility testing identifies more accessibility problems than traditional tools while delivering fast and dependable compliance verification results. The FinAccAI system enables large-scale intelligent accessibility audits which support national digital inclusion initiatives while helping institutions meet regulations and minimizing their exposure to risks and expanding financial services to 10 million American users. The research creates essential foundations for upcoming AI-based accessibility testing research while establishing open accessible solutions which will benefit the U.S. financial and technological sectors.

Keywords:
Accessibility ADA AI Enhanced Explainable AI (XAI) Accessibility Testing FinAccAI

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