Banking Security and Customer Experience: Cloud Computing-Enabled KYC in the Digital Era using ANN Model

The research work investigates the innovative notion of IoT-enabled Know Your Customer (KYC) and the revolutionary possibilities of Internet of Things (IoT) in the banking sector. Artificial Neural Network (ANN) models may discover complicated data correlations, making customer verification more accurate and efficient. Banks may automate KYC processes using cloud-based ANN models to speed up client onboarding and transaction approvals in the digital age. The demand for better protection measures, quicker client onboarding, and constant compliance monitoring is rising as the banking industry moves online. Financial institutions may provide a more modern and streamlined client experience by adding IoT devices and technology into the KYC procedure. The system will look at how IoT-based biometric authentication techniques, including face recognition, voice analysis, and geolocation verification, may provide strong identity verification while protecting client data. In this article, the system explores how to give clients more agency via the IoT by integrating wearable devices for safe banking interactions and remote KYC. It also emphasizes the importance of IoT in real-time compliance monitoring, which helps financial institutions spot and stop fraudulent acts as soon as it occurs. This study foresees a future in which an IoT-driven KYC transformation improves banks security, customer experiences, and regulatory compliance efficiency.

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Publication Details

Published
2024-08-29
DOI
https://doi.org/10.1109/iceect61758.2024.10739221
Citations
4
Primary Topic
Big Data and Business Intelligence
Type
article
Field-Weighted Citation Impact
2.11
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article

Banking Security and Customer Experience: Cloud Computing-Enabled KYC in the Digital Era using ANN Model

K. Sangeethalakshmi, D. Muthukumaran, R. Mahalakshmi, A. Nithya et al.
4 citations
Big Data and Business Intelligence
2.11
article

Banking Security and Customer Experience: Cloud Computing-Enabled KYC in the Digital Era using ANN Model

K. Sangeethalakshmi, D. Muthukumaran, R. Mahalakshmi, A. Nithya, Mageshkumar Naarayanasamy Varadarajan, C. Srinivasan
article en
4 citations

Abstract

The research work investigates the innovative notion of IoT-enabled Know Your Customer (KYC) and the revolutionary possibilities of Internet of Things (IoT) in the banking sector. Artificial Neural Network (ANN) models may discover complicated data correlations, making customer verification more accurate and efficient. Banks may automate KYC processes using cloud-based ANN models to speed up client onboarding and transaction approvals in the digital age. The demand for better protection measures, quicker client onboarding, and constant compliance monitoring is rising as the banking industry moves online. Financial institutions may provide a more modern and streamlined client experience by adding IoT devices and technology into the KYC procedure. The system will look at how IoT-based biometric authentication techniques, including face recognition, voice analysis, and geolocation verification, may provide strong identity verification while protecting client data. In this article, the system explores how to give clients more agency via the IoT by integrating wearable devices for safe banking interactions and remote KYC. It also emphasizes the importance of IoT in real-time compliance monitoring, which helps financial institutions spot and stop fraudulent acts as soon as it occurs. This study foresees a future in which an IoT-driven KYC transformation improves banks security, customer experiences, and regulatory compliance efficiency.

Capital One (United States) (US), Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology (IN), Vinayaka Missions University (IN), St. Joseph's Institute of Technology (IN), Saveetha University (IN)
Openalex Percentile: Top 10%
Big Data and Business Intelligence
2.11
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Banking Security and Customer Experience: Cloud Computing-Enabled KYC in the Digital Era using ANN Model — K. Sangeethalakshmi, D. Muthukumaran, et al. · (2024) | TGRS Research Map | TGRS