Invoice Discounting: A Blockchain-Based Approach

Abstract

Invoice discounting is a market with a double-digit potential growth rate over the next years in Europe and worldwide. The main benefit of invoice discounting is the acceleration of cash flow from customers to suppliers: suppliers get advance payments from the bank rather than waiting for the customers to pay. Hence, thanks to the quick availability of capital, businesses can invest in expansion and growth. More specifically, one of the most relevant problems today is how to provide better and faster invoice discounting services while preventing double spending and maintaining risk low. The blockchain frameworks have the potential to provide the right solution and thus to revolutionize the invoice discounting process. The benefits for suppliers, customers and financial institutions are related to the increased transparency added to the whole discounting process and the following risk reduction for the banks due to the capability to enhance the entire process and to reduce the double spending. In our paper, we introduce a blockchain-based invoice discounting system, called Distributed Ledger Invoice, and we propose a novel assessment method for evaluating currently available blockchain solutions for the invoice discounting scenario. Moreover, we also discuss two main issues regarding the information accessibility and the interoperability. In particular, since blockchain is still an emerging technology interoperability is a key factor for blockchain's adoption in inter-banking processes, where different blockchain solutions might be used. In this work we propose a decoupling layer, based on the Attribute-Based Access Control language, to unify the access control to reserved information across heterogeneous blockchains.

A critical review on blockchain assessment initiatives: A technology evolution viewpoint

“…Taking into account results shown in Table 2, although there are differences among them, the Technical factors presented in Scriber 36 are included as a whole in previous studies 13,38,39 . With regard to Technical factors not covered in Scriber, 36 the presence of Scalability is more generalized among the initiatives while Maintainability is just considered in Wang et al 35 and Kharitonov 41 .…”

“…Cost is the first criterion and, although is part of most of assessment methods in computing, in this case is just present in four out of the nine initiatives. Regulation and laws is present in all initiatives, except in Scriber 36 and Maranhão et al 37 Moreover, Efficiency is a factor just covered in Wang et al 35 while Governance (although includes also Technical aspects as well as Regulations and laws ) is covered in other works 37–39,41 . Finally, Business model adaptation is present in three initiatives, namely, Betzwieser et al 38 Kharitonov, 41 and Allessie 42 .…”

“…Counting the features of the different assessment factors, one can witness that the most complete effort is Betzwieser et al 38 which includes 16 factors, followed by Fabrizio et al 39 and Allessie 42 with a sum of 14 factors. In that, the weakest approach is Kharitonov 41 with 5 factors and Wang et al 35 with 9.…”

“…However, it is worth noting that the model is based on the assessment of a given technology and not its applicability in a given organization. In the work of Fabrizio et al 39 authors propose the Blockchain Assessment Model (BAM) derived from previous works on blockchain taxonomies 45 and propose seven criteria to choose a specific blockchain, namely, Ecosystem Governance, Administrative Accountability, Confidentiality of Exchanged Information, Scalability and Performance, Transactions' Speed and Volume, Crash Tolerance, and, finally, Assets' Management. This approach was tested in the invoice discounting (banking) arena.…”

Evaluation of the Benefits Generated by Sustainability 4.0: A Study of the Perception of Banking Sector Customers

“…Difficulty in use by older customers [23][24][25][26] Blockchain A faster, cheaper, and more efficient service; risk reduction; interoperability Limited information about data use; the granularity of privacy data [26][27][28][29] Big Data More efficient and faster digital services Privacy and data security [19,30] Source: The authors (2024).…”