Logistics financial risk assessment based on decision tree algorithm model
Pages 1095 - 1104
Abstract
In order to solve the financial risk problem of small and medium-sized logistics enterprises, a log algorithm is used to measure the financial risk of logistics. Combined with the actual situation of logistics finance, through the analysis of randomly selected logistics finance data, quantitatively extract important financial risk indicators, build a credit rating model based on decision tree C4.5 algorithm, and conduct empirical research on the model. The empirical results show that the quantitative financial risk indicator extraction method combined with decision tree C4.5 algorithm credit rating model has high accuracy in the evaluation of logistics finance risk. Therefore, based on the existing logistics financial risk assessment methods and models, the decision tree analysis technology is applied to analyze and make decisions on the credit status of credit applicants, and select companies with good credit, so as to reduce the financial risk of logistics enterprises and improve the efficiency of financial risk management.
References
[1]
L. Li, S. Dai, Z. Cao, J. Hong, S. Jiang, K. Yang, Using improved gradient-boosted decision tree algorithm based on kalman filter (gbdt-kf) in time series prediction, The Journal of Supercomputing 76 (9) (2020) 6887–6900.
[2]
Jianfei Wang, Cuiqing Jiang, Lina Zhou, Zhao Wang, Representing and discovering heterogeneous interactions for financial risk assessment of SMEs, Expert Syst. Appl. 247 (2024).
[3]
G. Pandhitas, B. Sutrisna, S. Wibowo, A. Adisasmita, B.R. Wreksoatmodjo, Decision tree clinical algorithm for screening of mild cognitive impairment in the elderly in primary health care: development, test of accuracy, and time-effectiveness analysis, Neuroepidemiology 54 (3) (2020) 1–8.
[4]
Ayinla, I.B. Akinola, An improved collaborative pruning using ant colony optimization and pessimistic technique of c5.0 decision tree algorithm, International Journal of Computer Science and Information Security 18 (12) (2021) 111–123.
[5]
Yung-Chia Chang, Kuei-Hu Chang, Guan-Jhih Wu, Application of eXtreme gradient boosting trees in the construction of credit risk assessment models for financial institutions, Appl. Soft Comput. 73 (2018) 914–920.
[6]
Vadipina Amarnadh, Nageswara Rao Moparthi, Prediction and assessment of credit risk using an adaptive Binarized spiking marine predators' neural network in financial sector, Multim. Tools Appl. 83 (16) (2024) 48761–48797.
[7]
Nikola Veljanovski, Marko Cepin, Event tree-based risk and financial assessment for power plants, Reliab. Eng. Syst. Saf. 247 (2024).
[8]
K. Hendarto, N. Anastasia, S.R. Basana, The effect of financial literacy, financial risk tolerance, and financial socialization agents on stock investment decision in the millennial generation, Petra International Journal of Business Studies 4 (1) (2021) 11–22.
[9]
A. Wang, Z. He, Y. Wang, Risk assessment of logistics finance enterprises based on bp neural network and fuzzy mathematical model, Journal of Intelligent and Fuzzy Systems 39 (4) (2020) 5915–5925.
[10]
M. Sukor, P. Nasehi, E. Koh, Financial risk attitudes, demographic profiles, and behavioural traits: do they interrelate?, Asian Journal of Accounting Perspectives 14 (1) (2021) 27–43.
[11]
Bernardete Ribeiro, Ning Chen, Financial credit risk assessment via learning-based hashing, Intell. Decis. Technol. 11 (2) (2017) 177–186.
[12]
Hammed, S. Jumoke, An implementation of decision tree algorithm augmented with regression analysis for fraud detection in credit card, International Journal of Computer Science and Information Security 18 (2) (2020) 79–88.
[13]
Miguel Vieira, Helena Paulo, Tânia Pinto-Varela, Ana Paula F.D. Barbosa-Póvoa, Assessment of financial risk in the design and scheduling of multipurpose plants under demand uncertainty, Int. J. Prod. Res. 59 (20) (2021) 6125–6145.
[14]
X. Li, Research on the influencing factors of internet financial risk and its prevention mechanism, Modern Economics & Management Forum 3 (1) (2022) 20–24.
[15]
S. Yu, X. Li, H. Wang, X. Zhang, S. Chen, C_cart: an instance confidence-based decision tree algorithm for classification, Intelligent Data Analysis 25 (4) (2021) 929–948.
[16]
Jing Duan, Financial system modeling using deep neural networks (DNNs) for effective risk assessment and prediction, J. Frankl. Inst. 356 (8) (2019) 4716–4731.
[17]
L. Fontoura, H. Lins, A.S. Bertuleza, A.G. D'Assuncao, A.G. Neto, Synthesis of multiband frequency selective surfaces using machine learning with the decision tree algorithm, IEEE Access (99) (2021) 1. 1.
[18]
Qi Zhang, Jue Wang, Aiguo Lu, Shouyang Wang, Jian Ma. An improved SMO algorithm for financial credit risk assessment - Evidence from China's banking, Neurocomputing 272 (2018) 314–325.
[19]
B. Kesari, B. Kesari, Sharing the economic torment due to covid –19 and pertinent behavioural biases on financial risk resilience ability of security market investors, Psychology 58 (2) (2021) 1073–1091.
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Published: 07 January 2025
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