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Toward Accessible Mobile Money: A Voice-Driven, Biometrically Secured USSD Automation Framework for Visually Impaired Users

arXiv Security Archived Jun 01, 2026 ✓ Full text saved

arXiv:2605.31375v1 Announce Type: new Abstract: Financial inclusion has expanded significantly across Africa through mobile money services delivered primarily via USSD technology. However, visually impaired individuals continue to face accessibility and security barriers when conducting financial transactions. Current USSD systems are not designed for non-visual interaction, forcing users to rely on third-party assistance even for PIN entry, thereby increasing fraud exposure and reducing transac

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    Computer Science > Cryptography and Security [Submitted on 29 May 2026] Toward Accessible Mobile Money: A Voice-Driven, Biometrically Secured USSD Automation Framework for Visually Impaired Users Sunday Ajayi, Babatunde Eric Olatunji, Eric Umuhoza Financial inclusion has expanded significantly across Africa through mobile money services delivered primarily via USSD technology. However, visually impaired individuals continue to face accessibility and security barriers when conducting financial transactions. Current USSD systems are not designed for non-visual interaction, forcing users to rely on third-party assistance even for PIN entry, thereby increasing fraud exposure and reducing transaction confidence. Although alternative assistive technologies such as screen readers exist, they are not compatible with USSD operations, often causing sessions to time out before the user can complete a transaction. This paper presents an Android-based intelligent middleware that automates USSD transactions, integrates biometric-secured PIN injection, and introduces a privacy-preserving screen-dimming mechanism: Blackout Mode. The system leverages Android Accessibility Services, hardware-backed Keystore security, and on-device natural language parsing to enable independent, secure voice-based mobile money access. We show that the proposed solution improves task success rates from 65-75% to more than 90% and reduces transaction completion time from 40-60 seconds to 12-15 seconds, while also improving perceived security. Subjects: Cryptography and Security (cs.CR); Human-Computer Interaction (cs.HC) Cite as: arXiv:2605.31375 [cs.CR]   (or arXiv:2605.31375v1 [cs.CR] for this version)   https://doi.org/10.48550/arXiv.2605.31375 Focus to learn more Submission history From: Eric Umuhoza [view email] [v1] Fri, 29 May 2026 14:44:55 UTC (683 KB) Access Paper: HTML (experimental) view license Current browse context: cs.CR < prev   |   next > new | recent | 2026-05 Change to browse by: cs cs.HC References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)
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    arXiv Security
    Category
    ◬ AI & Machine Learning
    Published
    Jun 01, 2026
    Archived
    Jun 01, 2026
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