Article: Data element perspective: green credit risk assessment using a multi-layer deep neural network Journal: International Journal of Information and Communication Technology (IJICT) 2025 Vol.26 No.36 pp.72 - 86 Abstract: Against the backdrop of accelerating 'double carbon' goals, green credit has become a key tool for channelling funds into low-carbon sectors, requiring sophisticated risk assessment models. Conventional approaches, limited by single-dimensional data and poor dynamic adaptability, fail to address green projects' multi-faceted risks (long investment cycles, rapid technological changes, strong policy dependency). This study proposes a novel green credit risk assessment framework from a data element perspective, using a multi-layer deep neural network (MLDNN). It integrates multi-source heterogeneous data, employs a three-tier neural architecture with an attention mechanism, and uses an adaptive learning rate algorithm. Empirical results from a provincial bank show the model achieves 92.57% risk identification accuracy, 11.2% higher than traditional BP neural networks, with notably improved generalisation in small-sample scenarios. Inderscience Publishers - linking academia, business and industry through research

Title: Data element perspective: green credit risk assessment using a multi-layer deep neural network

Authors: Lei Gu; Tao Liu

Addresses: Department of Economics and Management, Xi'an University of Architecture and Technology Huaqing College, Xi'an, 710043, China ' Department of Economics and Management, Xi'an University of Architecture and Technology Huaqing College, Xi'an, 710043, China

Abstract: Against the backdrop of accelerating 'double carbon' goals, green credit has become a key tool for channelling funds into low-carbon sectors, requiring sophisticated risk assessment models. Conventional approaches, limited by single-dimensional data and poor dynamic adaptability, fail to address green projects' multi-faceted risks (long investment cycles, rapid technological changes, strong policy dependency). This study proposes a novel green credit risk assessment framework from a data element perspective, using a multi-layer deep neural network (MLDNN). It integrates multi-source heterogeneous data, employs a three-tier neural architecture with an attention mechanism, and uses an adaptive learning rate algorithm. Empirical results from a provincial bank show the model achieves 92.57% risk identification accuracy, 11.2% higher than traditional BP neural networks, with notably improved generalisation in small-sample scenarios.

Keywords: green credit; risk assessment; data elements; multi-layer deep neural network; MLDNN; attention mechanism.

DOI: 10.1504/IJICT.2025.149056

International Journal of Information and Communication Technology, 2025 Vol.26 No.36, pp.72 - 86

Received: 11 Jul 2025
Accepted: 29 Aug 2025
Published online: 10 Oct 2025 *

Title: Data element perspective: green credit risk assessment using a multi-layer deep neural network

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