A Hybrid XGBoost-MLP Model for Credit Risk Assessment on Digital Supply Chain Finance

Li, Yixuan; Stasinakis, Charalampos; Yeo, Wee Meng

doi:10.3390/forecast4010011

Cited by 23 publications

(12 citation statements)

References 71 publications

(85 reference statements)

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“…Li et al (2022) introduced a supply chain management (SCM) with electronic information technology that is advanced and has become increasingly digital [19]. Financial institutions currently confront more efficiency and risk due to the abundance and complexity of audit information.…”

Section: Literature Reviewmentioning

confidence: 99%

A Machine Learning Algorithm for Supplier Credit Risk Assessment Based on Supply Chain Management

Wei

2022

International Transactions on Electrical Energy Systems

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In today’s complex and ever-changing world, a distribution network in lending impact analysis is an evaluation of a client’s procedures, rules, and financial well-being to evaluate as considerable risk and it provides to the contracting company. A creditor’s capability to pay the current lender’s obligations is considered while doing a lender’s threat assessment. Traditionally, it refers to the concern that the borrower may not be able to collect the sequence and interest. The challenges in lenders’ threat assessment are a lack of adequate data storage and retrieval, problematic delays caused by a lack of access to the relevant data at the right time, extended lead times that lead their shipments at risk, and demand for speedier deliveries. This paper introduces a machine learning-based linear regression algorithm (ML-LRA) for supplier credit risk (SCR) assessment based on supply chain management (SCM) in credit risk frameworks that depend significantly on modeling ML. Regression models are logistical constraints that can be used to simulate the impacts of multiple variables on a customer’s creditworthiness. The chain of distribution forecasting tool assesses specific decisions based on assumptions in variability. As a result of the findings in this study, it can be assumed that ML-LR approaches have a significant role in a variety of business processes such as supplier selection, risk prediction along with the supply chain, and demand and sales estimation. Finally, the study’s consequences for the most critical constraints and obstacles are examined to enhance the supply chain management system and ensure overall system sustainability.

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Section: Literature Reviewmentioning

confidence: 99%

A Machine Learning Algorithm for Supplier Credit Risk Assessment Based on Supply Chain Management

Wei

2022

International Transactions on Electrical Energy Systems

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“…Generally speaking, logistics distribution mainly includes the collection and distribution of goods, the assembly of vehiclemounted goods, and the definition of distribution channels. The latter two parts are not only important contents of logistics distribution vehicle planning but also important technologies of logistics distribution [8], as shown in Figure 1.…”

Section: Introductionmentioning

confidence: 99%

Research on Emergency Logistics Vehicle Route Scheduling and Optimization Method Based on Multi-Intelligent Decision System

Liang

2022

Journal of Sensors

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Logistics distribution vehicle planning is an important issue in logistics transportation activities, and it is also a research hotspot in theoretical circles at home and abroad. At present, many studies have focused on establishing vehicle planning models and optimizing vehicle planning in different environments and have achieved rich results. As an important part of transportation production process, the efficiency of logistics distribution is very important to the whole production process. Especially for emergency logistics, every minute is very critical for emergency situations such as disaster relief. In order to improve the efficiency of emergency logistics, this paper applies multiagent technology to emergency logistics and puts forward an integrated modeling method of enterprise macromodeling, business process mesomodeling, and micromodel design. Using the agent-oriented system development method, an emergency logistics distribution vehicle planning model system is established. The development process of multiagent automatic trading system is described. The results show that it is feasible and effective to use multi-intelligent fuselage technology for emergency logistics distribution vehicle planning and decision-making. The algorithm proposed in this paper has advantages over the container order sequence processing scheme, and the total cost of order acceptance decreases sharply in the initial stage, which shows the practical convergence of the algorithm. The adjacency search method and Tabu search method deal with the calculation of total labor cost, and the Tabu neighborhood search algorithm obtains better results with lower labor cost.

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“…This approach is probably the reason why for our data set, which has vastly different levels of coverage across the multidimensional phase space, GB vastly over-performs various linear and polynomial algorithms (Section 3). Such superior performance of XGB is well established for financing models (Horemuz 2018), and has opened the path for numerous applications in that sector (Li et al 2022).…”

Section: Methodsmentioning

confidence: 99%

Ion-driven Instabilities in the Inner Heliosphere. II. Classification and Multidimensional Mapping

Martinović

Klein

2023

ApJ

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Linear theory is a well-developed framework for characterizing instabilities in weakly collisional plasmas, such as the solar wind. In the previous installment of this series, we analyzed ∼1.5M proton and α particle velocity distribution functions (VDFs) observed by Helios I and II to determine the statistical properties of the standard instability parameters such as the growth rate, frequency, the direction of wave propagation, and the power emitted or absorbed by each component, as well as to characterize their behavior with respect to the distance from the Sun and collisional processing. In this work, we use this comprehensive set of instability calculations to train a machine-learning algorithm consisting of three interlaced components that: (1) predict if an interval is unstable from observed VDF parameters; (2) predict the instability properties for a given unstable VDF; and (3) classify the type of the unstable mode. We use these methods to map the properties in multidimensional phase space to find that the parallel-propagating, proton-core-induced ion cyclotron mode dominates the young solar wind, while the oblique fast magnetosonic mode regulates the proton beam drift in the collisionally old plasma.

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A Hybrid XGBoost-MLP Model for Credit Risk Assessment on Digital Supply Chain Finance

Cited by 23 publications

References 71 publications

A Machine Learning Algorithm for Supplier Credit Risk Assessment Based on Supply Chain Management

A Machine Learning Algorithm for Supplier Credit Risk Assessment Based on Supply Chain Management

Research on Emergency Logistics Vehicle Route Scheduling and Optimization Method Based on Multi-Intelligent Decision System

Ion-driven Instabilities in the Inner Heliosphere. II. Classification and Multidimensional Mapping

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