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
The difficulty for small enterprises and farmers to obtain loads is a characteristic in mountainous townships in China. This has become a bottleneck for the townships’ economic development and farmers’ income promotion. With the deep promotion of market economy, the social disharmonious factors, such as rural social management and long-standing debts, have become urgent problems for mountainous townships. How to jump out of the current social management mode and explore a new credit scoring model has become a focus for the current rural financial research studies in China. Especially, how to establish a sound index system for farmer credit rating, to build the financial service platform, to establish an incentive and restraint mechanism, to guide the farmers to participate in the credit system, to innovate social management style, and to improve management by decreasing social management costs using such credit system are all urgent problems for mountainous townships in China. Based on the current social management-based credit rating method, which is already put into practice on a trial in some mountainous townships in China, and the logic structure of the credit rating index system, this paper firstly uses mathematical techniques to present the general description of credit rating system, and then it constructs a multilayer unidirectional network for farmers’ credit rating and establishes a linear segment model to evaluate the farmers’ credit grades. Finally, it implements the proposed method to a case study and effectively solves the credit rating problem for farmers in some mountainous townships of China.
The concepts of rural areas are fundamentally different from China and other countries, and the percentage of population engaged in farming in other countries differs from that in China. Thus, the literatures of credit rating for farmers in other foreign countries do not have so much reference value for us. Especially, in Europe and America, the credit rating for farmers is a part of the general personal credit rating system, and it is not a separate research topic. In China, the percentage of population engaged in farming is still very large. The economic development in mountainous townships lags behind cities and the financial market is not perfect, as well as there are many disharmonious social factors. Thus, the research on credit rating problems based on social management for farmers is necessary and indispensable.
There are many literatures about credit rating or credit evaluation for enterprises (including commercial banks), and most of them are already commercialized. One of the most relevant literatures related to farmer’s credit rating proposed a banking credit worthiness evaluation method by combining Fuzzy rough set and Fuzzy C-means clustering. The proposed rule-based method was used to predict farmers’ creditworthiness and applied to actual bank data from 2044 farmers within China and used to aid in agricultural load decision-making [
Research on microfinance, a meaningful study, is that someone has conducted informal credit rationing and credit demand surveys in four emerging counties in China [
Research on the credit rating model and credit rating index system, through analysing hierarchy process (AHP) and fuzzy comprehensive evaluation method, establishes the risk evaluation index model of rural credit cooperatives and analyzes the risk level of rural credit cooperatives in Hebei Province [
This paper mainly studies the linear segment evaluation model of multilayer unidirectional network for farmers’ credit rating. Firstly, within the proposed three-level credit rating index system, it derives the calculation formula for each index of the four-layer unidirectional network for farmers’ credit rating, as well as the calculation formula for farmers’ credit evaluation. Secondly, this paper also derives the credit evaluation formulas for the one-level farmer’s credit rating system and the two-level farmer’s credit rating system; it further derives the credit evaluation formulas for the four-level farmer’s credit rating system. Thirdly, this paper designs a linear segment classifier to classify the credit ratings outputs from the multilayer unidirectional network and establishes the rules of credit rating for farmers and the rules of linear segmentation evaluation model of the unidirectional network for farmer’s credit rating. It also discusses the properties of bank loan risk based on the farmers’ credit rating. Finally, this paper applies the theoretical model to evaluate farmer’s credit rating in A County, Guangdong Province, China. Though the research of this paper is very innovative, it never applied for Chinese Patent in 2012 [
In order to evaluate the farmers’ credit grade, firstly we have to select the indexes used to perform the credit rating, namely, we should firstly construct the credit rating index system for farmers. At present, the characteristic of credit rating in rural areas can be described as follows. Firstly, some mountainous townships have been trying out the credible family and credible village and have developed the credit rating index system for farmers which is suitable for the development of rural economic in local area. However, as the rural areas are widely distributed in China, the conditions for agricultural production, the living standards of farmers, and the degree of cultural education vary from area to area; thus, the construction of the farmer’s credit rating index system differs from area to area. Secondly, the current popular method adopted in China to conduct farmer’s credit rating is the expert evaluation method. It quantifies each of the indexes and takes the summation of scores obtained by each index as the final credit score of the farmer and finally gives the farmer’s credit grade using this total score.
Based on the current situation of the farmer’s credit rating in China, this paper puts famer’s credit rating index system as an index system with multilayer unidirectional network structure. It combines the credit indexes in each grade through constructing the multilayer unidirectional network, calculates farmer’s credit scores by obtaining scores from outputs of the network, and obtains the credit grades of the outputs in the network by designing the linear segment classifier for credit rating, thus finishing the credit rating for farmers.
Let us consider a farmer’s credit rating index system with a network structure of four layers. Suppose there are
To calculate the credit scores and to conduct the credit rating, it is necessary to assign a score to each of the 3rd-level indexes. Suppose the scores of indexes in each rank are represented by
Suppose we divide farmers’ credit score into
Farmer’s credit rating system based on four-layer unidirectional network.
In Figure
According to the calculation rules of farmers’ credit score, the score of each index in the 1st level is the summation of all scores of its 2nd-level indexes. Thus, the calculation formula for the scores of the 1st-level indexes
Assuming the weights for the 1st-level indexes are
The result calculated from formula (
Currently, various credit index systems are adopted to conduct the credit rating for farmers in the mountainous agricultural counties of China. The common one is the two-level index system. If it is the two-level index system, a three-layer unidirectional network needs to be established to build the farmer’s credit rating system, as shown in Figure
Farmer’s credit rating system based on three-layer unidirectional network.
The calculation formula of the 1st-level index is
Similarly, assume that
More specifically, if the credit index only has one level, the unidirectional network structure is a two-layer network. With direct weighted sum of the score of each attribute index, we can obtain the 1st-level index score. Assuming
This session considers the more general case. Suppose there are
With
On the second layer are the
Suppose there are
From (
Summarize the above practice, and we obtain the following theorem.
If a multilayer unidirectional network is established according to the method proposed in this paper, then, for the credit rating system of farmers with
When When When When
Therefore, for any positive integer
In this session, based on the total credit scores obtained from the calculation formula (
The rules of farmers’ credit rating.
Credit score | Credit grade |
---|---|
The score is greater than or equal to | The 1st grade |
The score is greater than or equal to | The 2nd grade |
The score is greater than or equal to | The 3rd grade |
… | … |
The score is greater than or equal to | The ( |
The score is smaller than | The |
Furthermore, we use
Formula (
With farmer’s credit rating evaluated, the bank can determine the load credit for farmers based on their credit grades. Suppose credit line for the N credit grades
Suppose the credit grades
If the credit lines of credit grade
To achieve the stimulation purpose and to enhance the loan’s antirisk ability, the rural credit unions often adopt the co-guarantee policy when they provide load to farmers. Under the co-guarantee policy, if a farmer is not in the highest credit grade and if he is willing to join the co-guarantee program, his credit grade would not change but his credit line could be up for a range. Under the co-guarantee policy, for a farmer whose credit grades is
Farmer credit rating with
Credit score | Credit grade | Credit line | Join co-guarantee program or not |
---|---|---|---|
The score is greater than or equal to | 1st grade | ||
The score is greater than or equal to | 2nd grade | Yes | |
No | |||
The score is greater than or equal to | 3rd grade | Yes | |
No | |||
… | … | … | … |
The score is greater than or equal to | Yes | ||
No | |||
The score is smaller than | Nth grade | Yes | |
No |
If credit line for credit grade
The construction of farmer credit index system in
The 1st-level indexes consist of famer’s social stability, production and operation, financial income, individual performance, moral character, social credit, and so on, which can be divided into four mutual categorical attributes: social management, basic information, credit quality, and family finance.
The 2nd-level indexes are the further subdivision of each of the 1st-level indexes according to their mutual categorical attributes. For example, the social management in the 1st-level indexes is subdivided into legal compliance, respecting the old and caring for the young, social welfare participation, military service, compliance of the family planning, medical insurance, and so on. The credit quality in the 1st-level indexes is subdivided into the financial institution credit, the quality of farmer, social credit, external guarantee, and so on. The remaining indexes can be done in the same manner.
The 3rd-level indexes are the subdivision description for all cases with optional categories that might appear in the 2nd-level indexes, and the contents of these cases are mutually disjoint. For example, the legal compliance in the 2nd-level indexes are described as three cases: first, all family members are legal compliance and there are no gambling phenomena of family members; second, there exist gambling phenomena of family members in the past, but there is no bad behavior during the year; and third, there exist gambling phenomena or behavior of violating the law and discipline of the family members during the year. The social credit in the 2nd-level indexes is described as four cases: fine, good, general, and bad. And the remaining indexes can be done in the same manner.
In
The scoring method for the 3rd-level indexes in social management category (the 1st level) is incentive based, with positive and negative incentives simultaneously. For a farmer, he/she will get positive score if he/she performs well in the index; he/she will get 0 score if he/she performs OK in the index; he/she will get negative score (penalty) if he/she performs below expectation in the index.
As the scale of farm production and operation, the production and business operation ability, the credit quality, the family income, and so on are different from farmer to farmer; thus, the scores are various from attribute to attribute for each index in the 3rd level. Furthermore, every attribute is ranked from “good” to “bad” and the corresponding scores are ranked from high to low.
For example, in the
The 3rd-level indexes under the legal compliance and their scores.
The 1st-level indexes | The 2nd-level indexes | The 3rd-level indexes | Score |
---|---|---|---|
Social management | Legal compliance | All family members are legal compliance and there are no gambling phenomena | 2 |
There exist gambling phenomena in the family members in the past, but there is no bad behavior during the year | 1 | ||
There exist gambling phenomena or behavior of violating the law and discipline in the family members during the year | −2 | ||
Respect the old and care for the young | Good family solidarity; good relationship with neighbors, and there exist no quarrel phenomena | 2 | |
The family solidarity is very good, gets on averagely with the neighbors, and there exist occasional quarrel phenomena | 1 | ||
The family solidarity is very good, but gets on averagely with the neighbors; in addition, there exist frequent quarrel phenomena | 0 | ||
The family solidarity is good, but there exist frequent quarrel phenomena; or the family gets on bad with the neighbors, and there exist frequent quarrel phenomena | −2 |
Farmers’ credit is divided into five credit grades: excellent, excellent, good, average, and bad based on the results of their credit score. And their credit line can be determined based on their credit grade as well as whether they join the co-guarantee program or not, which is shown in Table
The credit rating for farmers in A County in 2011.
Credit score | Credit grade | Credit rating | Credit line | Conditions |
---|---|---|---|---|
≥105 | Excellent I | AAA | CNY 50,000 | |
90 to 104 | Excellent II | AA | CNY 40,000 | With joint guarantee of three or more families |
CNY 30,000 | Without joint guarantee | |||
80 to 89 | Good | A | CNY 20,000 | With joint guarantee of three or more families |
CNY 10,000 | Without joint guarantee | |||
70 to 79 | Average | B | CNY 10,000 | With joint guarantee of three or more families |
CNY 0 | Without joint guarantee | |||
69≤ | Bad | C |
From Table For farmers whose credit scores are greater than or equal to 105, their credit grade is “Excellent I” and the credit rating is “AAA”, with credit line up to 50,000 Chinese Yuan. For farmers whose credit scores are from 90 to 104, their credit grade is “Excellent II” and the credit rating is “AA.” The credit line is divided into two cases; the first case is that, for the farmers who joint the co-guarantee program, which has three or more families in the program, the credit line is 40,000 Chinese Yuan; the second case is that, for the farmers who do not joint the co-guarantee program, their credit line is 30,000 Chinese Yuan. For farmers whose credit scores are from 80 to 89, their credit grade is “Good” and their rating is “A.” The credit line is divided into two cases; the first case is that, for the farmers who joint the co-guarantee program, which has three or more families in the program, the credit line is 20,000 Chinese Yuan; the second case is that, for the farmers who do not joint the co-guarantee program, their credit line is 10,000 Chinese Yuan. For farmers whose credit scores are from 80 to 89, their credit grade is “General” and their credit rating is “B.” For the farmers with joint co-guarantee program, their credit line is 10,000 Chinese Yuan. For the farmers who do not joint co-guarantee, their credit line is 0 Chinese Yuan. For farmers whose credit scores are smaller than or equal to 69, their credit grade is “Bad” and their credit rating is “C.” Their credit line is 0 Chinese Yuan.
Suppose we use matrix
Then, the numbers of the indexes from the 1st level to the 3rd level in the credit scoring table farmers in
From matrix (
The proposed model is evaluated with farmers’ data from
Thus, there are 108 input nodes in the network as there are
2nd-level indexes in the credit rating system and the nodes in the middle layer of the network are composed of each index in the 2nd level; thus, there are 30 nodes in the middle layer of the network. The nodes in the fourth layer of the network are composed of each of the indexes from the first level, and there are four 1st-level indexes possessed by the credit rating index system. That is to say,
A multilayer one-way network of farmer credit rating with 4 inputs and 5 classifications (
In the process of credit rating, the rating indicators of the same layer are of equal importance. Therefore, when
From formula (
Feeding the scores obtained by each of the 3rd-level indexes of the 160 farmers into the network, the credit score of each farmer can be estimated based on formula (
From Table
With farmers’ credit scores estimated based on (19), these farmers’ credit grades can be evaluated using the linear segment classifier (10). Finally the results of the credit rating for these 160 farmers are the number of people in grade “Excellent”
Discrimination accuracy.
Excellent I | Excellent II | Good | General | Bad | |
---|---|---|---|---|---|
Original rating (number) | 0 | 8 | 31 | 41 | 80 |
Discrimination results (number) | 0 | 8 | 31 | 41 | 80 |
Discrimination accuracy | 100% | 100% | 100% | 100% | 100% |
The Scatter diagram of credit scoring of 160 farmers is shown in Figure
Scatter diagram of credit scoring of 160 farmers.
Nowadays, the credit rating system, the credit rating method, and the related rating mechanism are still not perfect in the rural areas of China. As the concepts of rural areas of foreign countries are fundamentally different from that of China, and the percentage of population engaged in farming in foreign countries differs from that in China, as well as the fact that the relevant literatures in foreign countries are obviously different from the actual situations in China. Thus, the proposed methods in these literatures that can be used as references in China are very rare. Therefore, when studying the financial innovation problem of China, we must start with the specific scenarios in rural areas of China, with consideration of different geographic location’s living conditions, business environments, and cultural characteristics, and construct the credit rating system and credit rating method which are compliance with history, economy, and culture of rural areas in China. Only in this way we can solve the farmer credit rating problems and the corresponding financial innovation problems in China.
Firstly, based on the logical structure of the three-level credit rating index system, this paper constructs a four-layer unidirectional network and conducts OR operation on the network’s inputs. These inputs are the farmer’s credit scores assigned to the 3rd-level indexes estimated by the expert scoring method. Then, this paper works out the credit scores of every output nodes in every layer based on the network structure, where the credit scores of the output nodes in the third layer are exactly the credit scores of the 1st-level indexes, and the credit score of the output node in the fourth layer is exactly the final credit score of the farmer.
Furthermore, this paper offers the calculation formula for index in each level of the three-level credit rating index system, as well as the credit score calculation formula for the four-layer unidirectional network. This paper also obtains the special case calculation formulas for the one-level and two-level credit rating index system. Based on these, it extends the formulas to cover the case of four-level credit rating index system and provides the credit score calculation formula for unidirectional network with more than five layers. In the end, it deduces the general calculation formulas for multilayer unidirectional network with the multilevel credit rating index system.
In addition, this paper also designs a linear segment classifier to classify the credit grades of outputs from the multilayer unidirectional network and builds the credit grades rules for farmers. It also discusses the properties of bank credit risk based on the farmers’ credit grades. Finally, this paper applies the theoretical model to evaluate the 160 farmers’ credit grades in A County, Guangdong Province, China. The result from the approaches proposed by this paper is compliance with the actual rating result in A County, which means the accuracy is 100%.
The studies in this paper involve the construction method for the credit rating index system, the establishment of the multilayer unidirectional network, the build of the credit scoring model, the design of the linear segment classifier for credit grade evaluation, and the application of the model. All of these have important theoretical innovation and practical application value. This paper can offer specific guidance to the reform and innovation of rural finance and the credit rating for farmers in China. It also has important theoretical research and scientific guidance value to the exploration and perfection of the credit rating system, to promote the credit concept to farmers in China, and to promote economy development in rural areas of China.
All data in the article come from a county sub-branch, Guangdong Province, People's Bank of China.
The authors declare that they have no conflicts of interest.
Sulin Pang contributed in writing the first draft, conceptualization, framework design, collecting data, investigation, data resources, drawing images, formal analysis, tables, establishing the model, project administration, revise, adding content; theorem proving, error correction, partial writing, completing the final draft, and funding. Shouyang Wang contributed in methodology, framework design, establishing the model, analysis data, error correction, partial writing, supervision, and completing the final draft. Lianhu Xia contributed in error correction, quality improvement, theorem proving, and completing the final draft. Dr. Lianhu Xia participated in the discussion of the final revised draft, the proof of the theorem in the process of revising the article, and contributed to the final draft. So, he was added to the final revised version.
The paper was supported by National Natural Science Foundation of China (91646112).