An ensemble machine learning approach for forecasting credit risk of agricultural SMEs’ investments in agriculture 4.0 through supply chain finance

Belhadi, Amine; Kamble, Sachin S.; Mani, Venkatesh; Benkhati, Imane; Touriki, Fatima Ezahra

doi:10.1007/s10479-021-04366-9

Cited by 40 publications

(26 citation statements)

References 71 publications

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“…5 We argue that for big data samples such as in our case, censoring or truncating efficient observations (with DEA scores greater than or equal to 1) from the prediction model may result in missing information, and their predictive power is accordingly weaker. We therefore support the ML literature (e.g., Belhadi et al, 2021;Tsai & Chen, 2010;Zhu et al, 2021) in confirming that the ML approach is superior to the econometric approach, and that our hybrid DEA-ML model is more efficient and more accurate than the traditional ones. Consequently, we conclude that LASSO regression is the best model of the ML approaches for predicting the efficiency of Vietnamese MSMEs.…”

Section: Second-stage Analytics: Predicting the Vietnamese Msmes' Per...supporting

confidence: 85%

“…Regarding the DEA approach, one could apply the variable returns to scale assumption (Banker, 1984), the cost/profit measures (Ngo et al, 2019b;Pilar et al, 2018), the fuzzy approach (Boubaker et al, 2020;Nandy & Singh, 2021), or the Euclidean distance (Hammami et al, 2020) to measure the DEA efficiency in different settings. Regarding ML analytics, the ensemble approach (Belhadi et al, 2021) and other hybrid ML combinations (e.g., combining LASSO and NN) could also be used. Finally, the extension of this CSW-RA-DEA to other industries with big data, such as banking and finance (Tsai & Chen, 2010), healthcare (Misiunas et al, 2016), agriculture (Nandy & Singh, 2021), or energy (Khezrimotlagh et al, 2019), could help increase our understanding of the role of DEA in such fields.…”

Section: Discussionmentioning

confidence: 99%

“…In particular, Zhu (2020) suggested that in big data situations, one needs to look at the possibility of combining DEA with other ML techniques, such as RF, support vector machines, and artificial neural networks. According to Tsai and Chen (2010) and Belhadi et al (2021), among others, such hybrid combinations are superior to single models. For instance, Lee and Cai (2020) proposed using the least absolute shrinkage and selection operator (LASSO) for variable selection in DEA with small simulated datasets.…”

Section: The Integration Of Dea Ra and MLmentioning

confidence: 99%

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Predicting the performance of MSMEs: a hybrid DEA-machine learning approach

Boubaker

Ngo

et al. 2023

Ann Oper Res

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Micro, small and medium enterprises (MSMEs) dominate the business landscape and create more than half of employment worldwide. How we can apply big data analytical tools such as machine learning to examine the performance of MSMEs has become an important question to provide quicker results and recommend better and more reliable solutions that improve performance. This paper proposes a novel method for estimating a common set of weights (CSW) based on regression analysis for data envelopment analysis (DEA) as an important analytical and operational research technique, which (i) allows for measurement evaluations and ranking comparisons of the MSMEs, and (ii) helps overcome the time-consuming non-convexity issues of other CSW DEA methodologies. Our hybrid approach used several econometric and machine learning techniques (such as Tobit, least absolute shrinkage and selection operator, and Random Forest regression) to empirically explain and predict the performance of more than 5400 Vietnamese MSMEs (2010‒2016), and showed that the machine learning techniques are more efficient and accurate than the econometric ones. Our study, therefore, sheds new light on the two-stage DEA literature, especially in terms of predicting performance in the era of big data to strengthen the role of analytics in business and management.

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Section: Second-stage Analytics: Predicting the Vietnamese Msmes' Per...supporting

confidence: 85%

Section: Discussionmentioning

confidence: 99%

Section: The Integration Of Dea Ra and MLmentioning

confidence: 99%

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Predicting the performance of MSMEs: a hybrid DEA-machine learning approach

Boubaker

Ngo

et al. 2023

Ann Oper Res

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“…Machine learning has been used in various financial applications, such as behavioral prediction, price modeling, algorithmic trading, portfolio management, fraud detection, customer churn, investor sentiment analysis, and credit risk prediction (Dixon et al 2020;Renault 2020;Belhadi et al 2021). Bankruptcy prediction, cryptocurrency volatility prediction, clustering causes of death in insurance-related data (Bett et al 2022), predict farmers' uptake of crop insurance (Mare et al 2022), and business sustainability have also been modeled using machine learning techniques (Bouri et al 2021a;Barboza et al 2017;Kipkogei et al 2021).…”

Section: Applications Of Machine Learning In Related Areasmentioning

confidence: 99%

Optimizing Pension Participation in Kenya through Predictive Modeling: A Comparative Analysis of Tree-Based Machine Learning Algorithms and Logistic Regression Classifier

Yego

Kasozi

Nkurunzinza

2023

Risks

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Pension plans play a vital role in the economy by impacting savings, consumption, and investment allocation. Despite declining mortality rates and increasing life expectancy, pension enrollment remains low, affecting the long-term financial stability and well-being of populations. To address this issue, this study was conducted to explore the potential of predictive modeling techniques in improving pension participation. The study utilized three tree-based machine learning algorithms and a logistic regression classifier to analyze data from a nationally representative 2019 Kenya FinAccess Household Survey. The results indicated that ensemble tree-based models, particularly the random forest model, were the most effective in predicting pension enrollment. The study identified the key factors that influenced enrollment, such as National Health Insurance Fund (NHIF) usage, monthly income, and bank usage. The findings suggest that collaboration among the NHIF, banks, and pension providers is necessary to increase pension uptake, along with increased financial education for citizens. The study provides valuable insight for promoting and optimizing pension participation.

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“…The form of supply chain management requires special consideration (Sharma, Shishodia, Kamble, Gunasekaran, & Belhadi, 2020;Belhadi, Kamble, Mani, Benkhati, & Touriki, 2021). The need for an approach in the corn supply chain in Central Java Province, which is expected to provide an overview of the availability of corn supply as a consideration for the management of the corn supply chain in delivering products from producers to consumers.…”

Section: Introductionmentioning

confidence: 99%

Corn Supply Chain in Central Java Province: Marketing Channel Efficiency and Chain Institutional Performance Approach

Zuhri

Rahayu

Kusnandar

et al. 2023

JICP

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Increasing corn production requires a supply chain to channel production from farmers to consumers. The supply chain of corn as a raw material for animal feed in Central Java Province is not yet well managed because each farmer or business is still carried out individually. The aim of the study was to analyze the supply chain picture based on the Food Supply Chain Network and the efficiency of the corn agribusiness supply chain in Central Java. Sampling techniques from farmers using proportional random sampling and snowball sampling methods will lead researchers to the next informant or to related institutions to the final consumer. Data analysis uses the FSCN, Marketing Margin, Farmer's Share and DEA methods. There are five corn agribusiness marketing channels in Central Java Province. Marketing margin and farmer's share with the Farmer-Cooperative-Consumer chain being the most efficient. Farmer-Village Trader-SubDistrict Trader-District Trader-Consumer marketing channel being the least efficient. Technical efficiency at the farmer level was 2%, at the village trader level 40%, at the subdistrict and district trader levels 37.5%, at the cooperative level 66.67%, at the animal feed company level 40% and at the poultry farmer level 25%.

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An ensemble machine learning approach for forecasting credit risk of agricultural SMEs’ investments in agriculture 4.0 through supply chain finance

Cited by 40 publications

References 71 publications

Predicting the performance of MSMEs: a hybrid DEA-machine learning approach

Predicting the performance of MSMEs: a hybrid DEA-machine learning approach

Optimizing Pension Participation in Kenya through Predictive Modeling: A Comparative Analysis of Tree-Based Machine Learning Algorithms and Logistic Regression Classifier

Corn Supply Chain in Central Java Province: Marketing Channel Efficiency and Chain Institutional Performance Approach

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