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

Abstract

Credit risk imposes itself as a significant barrier of agriculture 4.0 investments in the supply chain finance (SCF) especially for Small and Medium-sized Enterprises. Therefore, it is important for financial service providers (FSPs) to differentiate between low- and high-quality SMEs to accurately forecast the credit risk. This study proposes a novel hybrid ensemble machine learning approach to forecast the credit risk associated with SMEs’ agriculture 4.0 investments in SCF. Two core approaches were used, i.e., Rotation Forest algorithm and Logit Boosting algorithm. Key variables influencing the credit risk of agriculture 4.0 investments in SMEs were identified and evaluated using data collected from 216 agricultural SMEs, 195 Leading Enterprises and 104 FSPs operating in African agriculture sector. Besides the classical measures of credit risk assessment without involving SCF, the findings indicate that current ratio, financial leverage, profit margin on sales and growth rate of the agricultural SME are the upmost important variables that SCF actors need to focus on, in order to accurately and optimistically forecast and alleviate credit risk. The output of our study provides useful guidelines for SMEs, as it highlights the conditions under which they would be seen as creditworthy by FSPs. On the other hand, this study encourages the wide application of SCF in financing agriculture 4.0 investments. Due to the model’s performance, credit risk forecasting accuracy is improved, which results in future savings and credit risk mitigation in agriculture 4.0 investments of SMEs in SCF.

Graphic abstract

Appendix: RotF-LB algorithm

Let E be an ensemble of leaners, initially empty, and Z the number of boost samples

For j = 1 to Z do

Begin

The input variables are randomly grouped.

For each group of input variables:

• Consider a dataset formed by this input variables.

• Eliminate from the dataset all examples from a proper subset of the classes.

• Eliminate from the dataset a subset of the examples.

• Apply PCA with the remaining dataset.

• Consider the components of PCA as a new set of variables.

• 1. Starting with equal weights$\omega_{i}$ = $\frac{1}{N}$, i = 1, …, N, Function F(x) = 0 and sample probability estimates p($x_{i}$) = 0.5

• 2. Repeat form = 1 to Z

  • a. Compute the working response and weights

    $$ Z_{i} = \frac{{y_{i} - p\left( {x_{i} } \right)}}{{p\left( {x_{i} } \right)\left( {1 - p\left( {x_{i} } \right)} \right)}}\, {\text{and}}\,\omega_{i} = \frac{{p\left( {x_{i} } \right)}}{{1 - p\left( {x_{i} } \right)}} $$

    (6)

  • b. Fit the decision function $f_{m}$(x) by a weighted least-squares regression of ${\text{z}}_{{\text{i}}}$ to $x_{i}$ using weights $\omega_{i}$.

  • c. Update both F(x) and p(x)

    $$ F\left( x \right) = F\left( x \right) + 0.5f_{m} \left( x \right){\text{and}}\,p\left( x \right) = \frac{{e^{F\left( x \right)} }}{{e^{F\left( x \right)} + e^{ - F\left( x \right)} }} = \left( {1 + e^{ - 2F\left( x \right)} } \right)^{ - 1} $$

    (7)

Output classifier = the most often predicted class of E.

End