Agriculture microfinance risk control based on credit score model in China

Yao, Fengge; Wang, Jing

doi:10.1109/iciii.2013.6703581

Cited by 3 publications

(2 citation statements)

References 3 publications

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“…Research related to credit scoring has been widely carried out in various different credit contexts for both agricultural and non-agricultural credit. Credit scoring research in the agricultural sector was conducted by [11], who in this study conducted research related to the risks of controlling agricultural MSME credit using the logistic regression method. Further related research was conducted by [12].…”

Section: Introductionmentioning

confidence: 99%

“…In addition to various contexts, research related to credit scoring uses various methods. Commonly used methods are logistic regression [11], fuzzy logistic regression [13], classification and regression tree [14], ensemble classification based on supervised clustering [16], random forest by developing feature selection algorithms [15], and deep belief networks with restricted Boltzmann machines [17]. The application of random forest methods for credit scoring studies also varies, such as building a credit scoring model based on feature selection and grid search to optimize random forest algorithms [10] and using online credit data in agriculture with syncretic cost-sensitive random forest (SCSRF) [12].…”

Section: Introductionmentioning

confidence: 99%

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Credit Scoring Model for Farmers using Random Forest

Bahri¹,

Herdiyeni²,

Suprehatin³

2023

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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One of the problems faced by farmers in Indonesia is capital. Based on Indonesian Central Statistics Agency survey results, the number of farmers who borrow capital from formal institutions such as banks is still small. This is because the process of applying for loans at banks is lengthy, farmers are considered high-risk and unbankable, and the rating of the agricultural sector is unattractive to banks. This study aims to determine the attributes and design a model of agricultural credit assessment. This study uses secondary data related to bank credit ratings and land productivity from banks in the Telagasari sub-district in 2018–2020 and Cipayung sub-district in 2020. Data were analyzed using random forests. The research process includes four stages: data collection, data pre-processing, model building, and model analysis and evaluation. This study produced five important variables that are relevant to farmers: planting costs, sales, land productivity, total production, and land area. The model built produces the most optimal accuracy of 83% with an AUC score of 81%. Based on the AUC performance classification, it can be concluded that the model that has been made is good at predicting the credit status of farmers because the AUC value is included in the good classification predicate.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Credit Scoring Model for Farmers using Random Forest

Bahri¹,

Herdiyeni²,

Suprehatin³

2023

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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An Ontology-Based Model for Credit Scoring Knowledge in Microfinance: Towards a Better Decision Making

Addi

Souissi²

2020

2020 IEEE 10th International Conference on Intelligent Systems (IS)

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Farmer’s Credit Rating Model and Application Based on Multilayer Unified Network with Linear Classifier

2020

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This article innovatively builds the infrastructure of farmer credit rating index system into a multilevel unidirectional network structure. First, according to the logical structure of the three-level credit rating index system, a four-level unidirectional network is constructed, and the credit rating calculation formulas of all indexes at the four-level network are established. Furthermore, the special cases of the credit rating formula with the first- and second-level farmer credit rating index system are discussed. On this basis, it is extended to a credit rating index system with more than four levels, and the corresponding credit rating formula is established. Finally, the general formula of credit rating formula of the farmer credit rating index system from first level to multilevel is obtained. In order to solve the problem of farmers' credit rating, this paper also designs a linear segmentation classifier to classify the results of multilayer unidirectional network, establishes the rules of farmers' credit rating and the unidirectional network linear segmentation evaluation model of farmers' credit rating, and discusses the properties of bank credit based on farmers’ credit rating. Finally, the model established in this paper is applied to the credit rating of farmers in A County, Guangdong Province in China. When the credit rating of 160 farmers is carried out, the evaluation results are in line with the actual credit rating of farmers in A County, with an accuracy of 100%. This research has the maneuverability to carry on the scientific credit rating to the countryside. This study has important method guidance and operability for rural credit rating.

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Agriculture microfinance risk control based on credit score model in China

Cited by 3 publications

References 3 publications

Credit Scoring Model for Farmers using Random Forest

Credit Scoring Model for Farmers using Random Forest

An Ontology-Based Model for Credit Scoring Knowledge in Microfinance: Towards a Better Decision Making

Farmer’s Credit Rating Model and Application Based on Multilayer Unified Network with Linear Classifier

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