Financial Twin Chain, a Platform to Support Financial Sustainability in Supply Chains

• URL: http://arxiv.org/abs/2503.15980v1
• Date: Thu, 20 Mar 2025 09:24:18 GMT
• Title: Financial Twin Chain, a Platform to Support Financial Sustainability in Supply Chains
• Authors: Giuseppe Galante, Christiancarmine Esposito, Pietro Catalano, Salvatore Moscariello, Pasquale Perillo, Pietro D'Ambrosio, Angelo Ciaramella, Michele Di Capua,
• Abstract summary: Financial sustainability of a generic supply chain is a complex problem.<n>We propose a software platform that employs key enabling technologies including AI, blockchain, knowledge graph, and others.<n>This platform allows for the involvement of external entities that can help stakeholders or the whole supply chain to solve financial sustainability problems.
• Score: 0.01918079587074032
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The financial sustainability of a generic supply chain is a complex problem, which can be addressed through detailed monitoring of financial operations deriving from stakeholder interrelationships and consequent analysis of these financial data to compute the relative economic indicators. This allows the identification of specific fintech tools that can be selected to mitigate financial risks. The intention is to retrieve the financial transactions and private information of stakeholders involved in the supply chain to construct a knowledge base and a digital twin representation that can be used to visualize, analyze, and mitigate the issues associated with the financial sustainability of the chain. We propose a software platform that employs key enabling technologies, including AI, blockchain, knowledge graph, and others, opportunely coordinated to address the financial sustainability problem affecting single stakeholders and the entire supply chain. This platform allows for the involvement of external entities that can help stakeholders or the whole supply chain to solve financial sustainability problems through economic interventions. Moreover, introducing these entities enables stakeholders less well-positioned in the market to access financial services offered by credit institutions, utilising the supply chain's internal information as evidence of its reliability. To validate the proposed idea, a case study will be presented analyzing the financial instrument of securitization.

Related papers

• An Analytics-Driven Approach to Enhancing Supply Chain Visibility with Graph Neural Networks and Federated Learning [52.79646338275159]
  We propose a novel approach that integrates Federated Learning (FL) and Graph Convolutional Neural Networks (GCNs) to enhance supply chain visibility.<n>FL enables collaborative model training across countries by facilitating information sharing without requiring raw data exchange.<n>GCNs empower the framework to capture intricate relational patterns within knowledge graphs, enabling accurate link prediction to uncover hidden connections.
  arXiv  Detail & Related papers  (2025-03-10T12:15:45Z)
• Technical Insights on Blockchain's Role in Financial Systems [0.0]
  This research provides a critical analysis regarding the way blockchain is being implemented in the financial industry.<n>It highlights its vital role in promoting green finance, guaranteeing compliance with regulations, improving supply chain finance, boosting decentralized finance (DeFi), and strengthening the Internet of Things (IoT)
  arXiv  Detail & Related papers  (2024-12-06T05:08:14Z)
• Robust Graph Neural Networks for Stability Analysis in Dynamic Networks [16.077138803931295]
  This paper explores the economic risk identification algorithm based on the graph neural network (GNN) algorithm. It aims to provide financial institutions and regulators with more intelligent technical tools to help maintain the security and stability of the financial market.
  arXiv  Detail & Related papers  (2024-10-29T06:11:36Z)
• A Survey on Blockchain-based Supply Chain Finance with Progress and Future directions [1.320904960556043]
  Supply Chain Finance is an important tool to activate the capital flow in the supply chain. Data-proof, forgery-proof, cryptography, consensus verification, and decentralization fits well with Supply Chain Finance. What technology brings to Supply Chain Finance is not only to alleviate the problems of information asymmetry, credit disassembly, and financing cost, but also to improve Supply Chain Finance operations through smart contracts.
  arXiv  Detail & Related papers  (2024-08-14T15:08:51Z)
• Enhancing Supply Chain Visibility with Knowledge Graphs and Large Language Models [49.898152180805454]
  This paper presents a novel framework leveraging Knowledge Graphs (KGs) and Large Language Models (LLMs) to enhance supply chain visibility. Our zero-shot, LLM-driven approach automates the extraction of supply chain information from diverse public sources. With high accuracy in NER and RE tasks, it provides an effective tool for understanding complex, multi-tiered supply networks.
  arXiv  Detail & Related papers  (2024-08-05T17:11:29Z)
• Enhancing Financial Inclusion and Regulatory Challenges: A Critical Analysis of Digital Banks and Alternative Lenders Through Digital Platforms, Machine Learning, and Large Language Models Integration [0.0]
  This paper explores the dual impact of digital banks and alternative lenders on financial inclusion and the regulatory challenges posed by their business models. It discusses the integration of digital platforms, machine learning (ML), and Large Language Models (LLMs) in enhancing financial services accessibility for underserved populations.
  arXiv  Detail & Related papers  (2024-04-18T05:00:53Z)
• AlphaFin: Benchmarking Financial Analysis with Retrieval-Augmented Stock-Chain Framework [48.3060010653088]
  We release AlphaFin datasets, combining traditional research datasets, real-time financial data, and handwritten chain-of-thought (CoT) data. We then use AlphaFin datasets to benchmark a state-of-the-art method, called Stock-Chain, for effectively tackling the financial analysis task.
  arXiv  Detail & Related papers  (2024-03-19T09:45:33Z)
• Towards Financially Inclusive Credit Products Through Financial Time Series Clustering [10.06218778776515]
  Financial inclusion increases consumer spending and consequently business development. Customer segmentation based on consumer transaction data is a well-known strategy used to promote financial inclusion. We present a novel time series clustering algorithm that allows institutions to understand the financial behaviour of their customers.
  arXiv  Detail & Related papers  (2024-02-16T20:40:30Z)
• PIXIU: A Large Language Model, Instruction Data and Evaluation Benchmark for Finance [63.51545277822702]
  PIXIU is a comprehensive framework including the first financial large language model (LLMs) based on fine-tuning LLaMA with instruction data. We propose FinMA by fine-tuning LLaMA with the constructed dataset to be able to follow instructions for various financial tasks. We conduct a detailed analysis of FinMA and several existing LLMs, uncovering their strengths and weaknesses in handling critical financial tasks.
  arXiv  Detail & Related papers  (2023-06-08T14:20:29Z)
• FinQA: A Dataset of Numerical Reasoning over Financial Data [52.7249610894623]
  We focus on answering deep questions over financial data, aiming to automate the analysis of a large corpus of financial documents. We propose a new large-scale dataset, FinQA, with Question-Answering pairs over Financial reports, written by financial experts. The results demonstrate that popular, large, pre-trained models fall far short of expert humans in acquiring finance knowledge.
  arXiv  Detail & Related papers  (2021-09-01T00:08:14Z)
• Gaussian process imputation of multiple financial series [71.08576457371433]
  Multiple time series such as financial indicators, stock prices and exchange rates are strongly coupled due to their dependence on the latent state of the market. We focus on learning the relationships among financial time series by modelling them through a multi-output Gaussian process.
  arXiv  Detail & Related papers  (2020-02-11T19:18:18Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.