Advancing AI-Enhanced Financial Security: A Review of Facial, Voice, and Medical Biometrics for Identity Verification

Chen, Z; Dong, AX; Peters, GW; Chan, J; Huang, W; Lin, C-C

Advancing AI-Enhanced Financial Security: A Review of Facial, Voice, and Medical Biometrics for Identity Verification

Chen, Z Dong, AX Peters, GW Chan, J Huang, W Lin, C-C

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Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Conference Proceeding
Citation:: 2025 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2025, 00, pp. 6753-6759
Issue Date:: 2025-10-08

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Field	Value	Language
dc.contributor.author	Chen, Z
dc.contributor.author	Dong, AX
dc.contributor.author	Peters, GW
dc.contributor.author	Chan, J
dc.contributor.author	Huang, W
dc.contributor.author	Lin, C-C
dc.date	2025-10-05
dc.date.accessioned	2026-02-15T05:04:42Z
dc.date.available	2026-02-15T05:04:42Z
dc.date.issued	2025-10-08
dc.identifier.citation	2025 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2025, 00, pp. 6753-6759
dc.identifier.uri	http://hdl.handle.net/10453/193493
dc.description.abstract	Identity verification is a critical component of financial data and systems security and privacy preservation, and is required by regulatory guidelines to ensure compliance with regulatory requirements and to aid in fraud prevention. With advances in artificial intelligence (AI), deep learning, and statistical methods, financial institutions are increasingly adopting multifactor authentication (MFA) that incorporates biometric-based approaches to perform authentication of identities for access to accounts, records or data and for verification of decision making and confirmation of actions. This paper presents a systematic review of current methodologies that utilize facial recognition, voice biometrics, and other data for identity verification in financial institutions. We explore the effectiveness and challenges associated with these approaches, highlighting recent developments in AI-driven models, deep learning architectures, and statistical techniques. In addition, we discuss the integration of multimodal biometric data and the decision and access systems that are developed for MFA approaches to improve security and accuracy. This review offers insights into the future of biometric identity verification in the financial sector. Our findings suggest that the integration of multi-modal data in financial applications could serve as a valuable avenue for future research and practical applications. In addition, investigating the role of biometric authentication in back-end systems is an important area worth further exploration.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof	2025 IEEE International Conference on Systems, Man, and Cybernetics (SMC)
dc.relation.ispartof	2025 IEEE International Conference on Systems, Man, and Cybernetics (SMC)
dc.relation.isbasedon	10.1109/smc58881.2025.11342510
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Advancing AI-Enhanced Financial Security: A Review of Facial, Voice, and Medical Biometrics for Identity Verification
dc.type	Conference Proceeding
utslib.citation.volume	00
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Provost
pubs.organisational-group	University of Technology Sydney/Provost/TD School
utslib.copyright.status	open_access	*
dc.date.updated	2026-02-15T05:04:40Z
pubs.finish-date	2025-10-08
pubs.publication-status	Published
pubs.start-date	2025-10-05
pubs.volume	00

Abstract:

Identity verification is a critical component of financial data and systems security and privacy preservation, and is required by regulatory guidelines to ensure compliance with regulatory requirements and to aid in fraud prevention. With advances in artificial intelligence (AI), deep learning, and statistical methods, financial institutions are increasingly adopting multifactor authentication (MFA) that incorporates biometric-based approaches to perform authentication of identities for access to accounts, records or data and for verification of decision making and confirmation of actions. This paper presents a systematic review of current methodologies that utilize facial recognition, voice biometrics, and other data for identity verification in financial institutions. We explore the effectiveness and challenges associated with these approaches, highlighting recent developments in AI-driven models, deep learning architectures, and statistical techniques. In addition, we discuss the integration of multimodal biometric data and the decision and access systems that are developed for MFA approaches to improve security and accuracy. This review offers insights into the future of biometric identity verification in the financial sector. Our findings suggest that the integration of multi-modal data in financial applications could serve as a valuable avenue for future research and practical applications. In addition, investigating the role of biometric authentication in back-end systems is an important area worth further exploration.

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http://hdl.handle.net/10453/193493