Exploring a new business model for lending processes in the banking sector using Blockchain technology: An Italian case study

Giuseppina Iacoviello & Elena Bruno

The International Journal of Digital Accounting Research2023https://doi.org/10.4192/1577-8517-v23_3article
ABDC B
Weight
0.45

What the paper says

Blockchain is a decentralized information technology (IT) architecture that has garnered significant attention across various sectors of the global economy. In the banking sector, blockchain was initially used for cryptocurrency trading and later expanded to encompass smart contracts, peer-to-peer transactions, and other banking services. In recent years, blockchain technology (BT) has been applied to streamline less standardized credit processes and to successfully support mortgage credit through decentralized recording on ledgers. Employing a qualitative research approach, this paper proposes a novel business model for small banks that utilizes new-generation information technologies to enhance loan profitability. While previous research has linked BT to lending processes, this study is the first to propose a BT application for reshaping traditional banking practices, especially for commercial banks. The research findings demonstrate that blockchain implementation offers advantages in containing information asymmetries, managing credit rationing, and driving business innovation.

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https://doi.org/https://doi.org/10.4192/1577-8517-v23_3

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@article{giuseppina2023,
  title        = {{Exploring a new business model for lending processes in the banking sector using Blockchain technology: An Italian case study}},
  author       = {Giuseppina Iacoviello & Elena Bruno},
  journal      = {The International Journal of Digital Accounting Research},
  year         = {2023},
  doi          = {https://doi.org/https://doi.org/10.4192/1577-8517-v23_3},
}

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Evidence weight

0.45

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.27 × 0.4 = 0.11
M · momentum0.80 × 0.15 = 0.12
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.