Construction of Artificial Intelligence Application Model for Supply Chain Financial Risk Assessment

Luo, Shuai; Min, Xin; Zhao, Jialu

doi:10.1155/2022/4194576

Cited by 4 publications

(2 citation statements)

References 27 publications

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“…This involves the systematic review and analysis of the necessity and urgency of applying AI technology to identify financial difficulties in supply chains. Moreover, the construction of an AI application model for supply chain financial risk assessment is a significant development in this domain (Luo et al, 2022). The use of AI and ML in various sectors, including supply chain management, is expected to continue growing in importance in the coming years (Naved, 2022).…”

Section: Future Trends and Developmentsmentioning

confidence: 99%

Reviewing the use of big data in supply chain optimization in the USA

Israel Osejie Okoduwa,

Bankole Ibrahim Ashiwaju,

Jeremiah Olawumi Arowoogun

et al. 2024

World J. Adv. Res. Rev.

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This paper provides a succinct overview of the review conducted on the utilization of Big Data in supply chain optimization within the United States. The advent of Big Data analytics has revolutionized traditional supply chain management, offering unprecedented opportunities for enhancing efficiency, reducing costs, and improving overall operational performance. This comprehensive review examines the current state of Big Data applications in supply chain optimization, focusing specifically on the context of the United States. The review begins by outlining the fundamental role of Big Data in transforming supply chain processes. It delves into various aspects, including data collection, processing, and analysis, emphasizing their impact on decision-making and strategic planning. Notably, the study highlights real-world examples of successful Big Data implementations within the U.S. supply chain landscape, showcasing how companies have leveraged data-driven insights to streamline operations and gain a competitive edge. Furthermore, the paper explores the challenges associated with the adoption of Big Data in supply chain management. It addresses concerns related to data security, privacy, and the need for advanced technological infrastructure. Additionally, the review considers the organizational and cultural shifts required for successful integration, emphasizing the importance of a holistic approach to implementation. The paper concludes by pointing towards future trends and potential developments in the use of Big Data for supply chain optimization in the U.S. It underscores the evolving nature of technology and its continued impact on reshaping supply chain strategies. The insights derived from this review contribute to a deeper understanding of the current landscape, offering valuable implications for practitioners, researchers, and policymakers engaged in optimizing supply chain processes through the harnessing of Big Data analytics.

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Section: Future Trends and Developmentsmentioning

confidence: 99%

Reviewing the use of big data in supply chain optimization in the USA

Israel Osejie Okoduwa,

Bankole Ibrahim Ashiwaju,

Jeremiah Olawumi Arowoogun

et al. 2024

World J. Adv. Res. Rev.

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“…Recommended to deal with class imbalances. [38] Dynamic mutation particle swarm optimization algorithm (AdaBoost-DPSO-SVM)…”

Section: ] Rb-xgboost Algorithm Efficientmentioning

confidence: 99%

Modelos para la evaluación de riego crediticio en el ámbito de la tecnología financiera: una revisión

Tadeo Espinoza,

Coral Ygnacio

2023

TecnoL.

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Esta revisión analiza una selección de artículos científicos sobre la implantación de sistemas de evaluación del riesgo de crédito para identificar las soluciones existentes, las más acertadas y las limitaciones y problemas en su desarrollo. Se adoptó la declaración PRISMA del siguiente modo: se formularon las preguntas de investigación, se definieron los criterios de inclusión, se seleccionaron las palabras clave y se diseñó la cadena de búsqueda. Por último, se calcularon varios estadísticos descriptivos de los artículos seleccionados. En los estudios seleccionados se identificaron 31 soluciones, entre métodos, modelos y algoritmos. Algunos de los modelos más utilizados se basan en técnicas de Inteligencia Artificial (IA), especialmente Redes Neuronales y Bosques Aleatorios. Se concluyó que las Redes Neuronales son las soluciones más eficientes, con precisiones medias superiores al 90 %, pero su desarrollo puede tener limitaciones. Estas soluciones deben implementarse teniendo en cuenta el contexto en el que se van a emplear.

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Digital transformation and integration of artificial intelligence in financial institutions

Mohsen,

Hamdan,

Shoaib

2024

JFRA

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Purpose Integrating artificial intelligence (AI) into various industries, including the financial sector, has transformed them. This paper aims to examine the influence of integrating AI, including machine learning, process automation, predictive analytics and chatbots, on financial institutions and explores its various aspects and areas. The study aims to determine the impact of AI integration on financial services, products and customer experience. Design/methodology/approach The research study uses quantitative and qualitative methods, as well as secondary data analysis. It investigates four AI subfields: machine learning, process automation, predictive analytics and chatbots. Findings The research findings indicate that integrating AI, particularly in machine learning and chatbot subfields, holds promise and high strategic potential for financial institutions. These subfields can contribute significantly to enhancing financial services and customer experience. However, the significance of predictive analytics integration and process automation is relatively lower. Although these subfields retain their usefulness, they might necessitate alternative workflows and tools that incorporate human involvement. Overall, AI integration minimizes human interactions and errors in financial institutions. Originality/value The research study contributes original insights by exploring the specific subfields of AI within the financial industry and assessing their strategic significance. It provides recommendations for financial institutions to adopt AI integration partially in multiple phases, measure and evaluate the impact of the transformation and structure internal units and expertise to strategize adoption and change.

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Construction of Artificial Intelligence Application Model for Supply Chain Financial Risk Assessment

Cited by 4 publications

References 27 publications

Reviewing the use of big data in supply chain optimization in the USA

Reviewing the use of big data in supply chain optimization in the USA

Modelos para la evaluación de riego crediticio en el ámbito de la tecnología financiera: una revisión

Digital transformation and integration of artificial intelligence in financial institutions

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