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<p>Artificial Intelligence (AI) is increasingly used in financial services; video identification is a&#xa0;key application. While the technology promises efficiency and compliance, customer acceptance remains critical. Despite extensive research on digital banking adoption, little is known about how customers perceive AI-assisted identification. We address this gap through a&#xa0;systematic literature review and twelve guided expert interviews (customers, technology specialists, and digital support roles). Findings show that perceived usefulness, in particular time independence (24/7), location independence, and process efficiency, is the strongest driver, followed by trust and ease of use. Data protection and security act as necessary conditions for acceptance; sustainability was expected but did not actively drive adoption. Acceptance improves with transparent explanations (explainability), visible privacy safeguards (e.g., encryption, retention periods), and human-in-the-loop via digital support (chat/hotline). We derive actionable design guidelines for banks and fintechs to implement customer-centric, trust-enhancing, and value-driven AI-assisted onboarding.</p>

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KI-gestützte Videoidentifikation im Finanzsektor: Treiber der Akzeptanz und praxisnahe Implikationen

  • 期刊论文

  • 发表日期：

    2026年1月14日

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  • Safaâ Houna,
  • Károly Szóka

摘要

Artificial Intelligence (AI) is increasingly used in financial services; video identification is a key application. While the technology promises efficiency and compliance, customer acceptance remains critical. Despite extensive research on digital banking adoption, little is known about how customers perceive AI-assisted identification. We address this gap through a systematic literature review and twelve guided expert interviews (customers, technology specialists, and digital support roles). Findings show that perceived usefulness, in particular time independence (24/7), location independence, and process efficiency, is the strongest driver, followed by trust and ease of use. Data protection and security act as necessary conditions for acceptance; sustainability was expected but did not actively drive adoption. Acceptance improves with transparent explanations (explainability), visible privacy safeguards (e.g., encryption, retention periods), and human-in-the-loop via digital support (chat/hotline). We derive actionable design guidelines for banks and fintechs to implement customer-centric, trust-enhancing, and value-driven AI-assisted onboarding.