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Article
Publication date: 2 January 2024

Yi-Hsin Lin, Ruixue Zheng, Fan Wu, Ningshuang Zeng, Jiajia Li and Xingyu Tao

This study aimed to improve the financing credit evaluation for small and medium-sized real estate enterprises (SMREEs). A financing credit evaluation model was proposed, and a…

Abstract

Purpose

This study aimed to improve the financing credit evaluation for small and medium-sized real estate enterprises (SMREEs). A financing credit evaluation model was proposed, and a blockchain-driven financing credit evaluation framework was designed to improve the transparency, credibility and applicability of the financing credit evaluation process.

Design/methodology/approach

The design science research methodology was adopted to identify the main steps in constructing the financing credit model and blockchain-driven framework. The fuzzy analytic hierarchy process (FAHP)–entropy weighting method (EWM)–set pair analysis (SPA) method was used to design a financing credit evaluation model. Moreover, the proposed framework was validated using data acquired from actual cases.

Findings

The results indicate that: (1) the proposed blockchain-driven financing credit evaluation framework can effectively realize a transparent evaluation process compared to the traditional financing credit evaluation system. (2) The proposed model has high effectiveness and can achieve efficient credit ranking, reflect SMREEs' credit status and help improve credit rating.

Originality/value

This study proposes a financing credit evaluation model of SMREEs based on the FAHP–EWM–SPA method. All credit rating data and evaluation process data are immediately stored in the proposed blockchain framework, and the immutable and traceable nature of blockchain enhances trust between nodes, improving the reliability of the financing credit evaluation process and results. In addition, this study partially fulfills the lack of investigations on blockchain adoption for SMREEs' financing credit.

Article
Publication date: 11 December 2020

Jiajun Liu and Pingyu Jiang

Social manufacturing has emerged. It aims to integrate the manufacturing resources of micro- and small-scale manufacturing enterprises (MSMEs) and help MSMEs cope with the…

536

Abstract

Purpose

Social manufacturing has emerged. It aims to integrate the manufacturing resources of micro- and small-scale manufacturing enterprises (MSMEs) and help MSMEs cope with the dynamic, service-oriented and personalized market demands. In social manufacturing, MSMEs cooperate with each other through manufacturing resource sharing. However, because MSMEs are distributed and decentralized, the efficiency of establishing reliable cooperation between MSMEs is relatively low. Therefore, this paper presents a blockchain-driven cyber-credit evaluation system (BCCES) to implement distributed cyber-credit evaluation. BCCES can provide reliable cyber-credit for distributed MSMEs without the trusted third party. This can improve the efficiency of establishing reliable cooperation among unauthentic MSMEs.

Design/methodology/approach

The paper proposes a BCCES to evaluate MSMEs' cyber-credit in decentralized environment. In BCCES, a cyber-credit evaluation model is proposed by improving set pair analysis (SPA) method, and cyber-credit smart contract and distributed consensus mechanism are designed according to the runtime logic of distributed cyber-credit evaluation.

Findings

The results confirmed that BCCES is feasible and effective to implement cyber-credit evaluation without the trusted third party. With the advantages of blockchain, BCCES can automatically realize cyber-credit evaluation through smart contract and distributed consensus. At the same time, BCCES can evaluate the real-time cyber-credit of MSMEs based on their latest service evaluation. In addition, we can design corresponding smart contracts according to actual requirements, which makes blockchain applicable to different distributed scenarios.

Originality/value

The paper combines blockchain and SPA to implement cyber-credit evaluation in social manufacturing and provides a new feasible idea for cyber-credit evaluation without the trusted third party. This can also provide MSMEs a reference of applying blockchain to other distributed scenarios through combining smart contract and different algorithms.

Details

Industrial Management & Data Systems, vol. 121 no. 4
Type: Research Article
ISSN: 0263-5577

Keywords

Open Access
Article
Publication date: 29 October 2019

Zhishuo Liu, Tian Fang, Yao Dongxin and Nianci Kou

Current models of transaction credit in the e-commerce network face many problems, such as the one-sided measurement, low accuracy and insufficient anti-aggression solutions. This…

Abstract

Purpose

Current models of transaction credit in the e-commerce network face many problems, such as the one-sided measurement, low accuracy and insufficient anti-aggression solutions. This paper aims to address these problems by studying the transaction credit problem in the crowd transaction network.

Design/methodology/approach

This study divides the transaction credit into two parts, direct transaction credit and recommended transaction credit, and it proposes a model based on the crowd transaction network. The direct transaction credit comprehensively includes various factors influencing the transaction credit, including transaction evaluation, transaction time, transaction status, transaction amount and transaction times. The recommendation transaction credit introduces two types of recommendation nodes and constructs the recommendation credibility for each type. This paper also proposes a “buyer + circle of friends” method to store and update the transaction credit data.

Findings

The simulation results show that this model is superior with high accuracy and anti-aggression.

Originality/value

The direct transaction credit improves the accuracy of the transaction credit data. The recommendation transaction credit strengthens the anti-aggression of the transaction credit data. In addition, the “buyer + circle of friends” method fully uses the computing of the storage ability of the internet, and it also solves the failure problem of using a single node.

Details

International Journal of Crowd Science, vol. 3 no. 3
Type: Research Article
ISSN: 2398-7294

Keywords

Article
Publication date: 15 May 2017

Yanyan Gao, Jun Sun and Qin Zhou

The purpose of this paper is to estimate the effectiveness of the credit evaluation system using the borrowing data from China’s leading P2P lending platform, Renrendai.com.

1200

Abstract

Purpose

The purpose of this paper is to estimate the effectiveness of the credit evaluation system using the borrowing data from China’s leading P2P lending platform, Renrendai.com.

Design/methodology/approach

The current credit valuation systems are classified into the forward-looking mechanism, which judges the borrowers’ credit levels based on their uploaded information, and the backward-looking mechanism, which judges the borrowers’ credit levels based on their historical repayment performance. Probit models and Tobit models are used to examine the effectiveness of credit evaluation mechanisms.

Findings

The results show that only the “hard” information reflecting borrowers’ credit ability can explain the default risk on the platform under the forward-looking credit evaluation mechanism. The backward-looking credit evaluation mechanism (BCEM) based on the repeated borrowings produces both promise-enhancing and “fishing” incentives and thus fails to explain the default risk, and weakens the effectiveness of forward-looking credit indicators in explaining the default risk because it encourages borrowers to invest in forging forward-looking credit indicators. Additional information such as the interest rate and the repayment periods reveals borrowers’ credit and thus can also be used as a predictor of borrowers’ default risk.

Practical implications

The findings suggest that current ex ante screening based on the information collected from the borrowers or repeated borrowings is inadequate to control the default risk in P2P lending markets and thus needs be improved. Ex post monitoring and sharing on defaulter’s information should be strengthened to increase the default cost and thus to deter potential bad borrowers.

Originality/value

To the authors’ knowledge, this is the first paper classifying the credit evaluation system in online P2P lending market into the forward-looking type and the backward-looking type, which is important since they provide different incentives to borrowers. The paper also investigates and provides evidence on the promise-enhancing and “fishing” incentives of BCEMs.

Details

China Finance Review International, vol. 7 no. 2
Type: Research Article
ISSN: 2044-1398

Keywords

Article
Publication date: 1 November 2018

Ahmad Raza Bilal and Mirza Muhammad Ali Baig

The purpose of this paper is to investigate the balanced role of internal and external compliance in risk evaluation process of specialized agriculture financing. The authors…

1074

Abstract

Purpose

The purpose of this paper is to investigate the balanced role of internal and external compliance in risk evaluation process of specialized agriculture financing. The authors examine the adaptive behavior of risk managers to determine the role of proposed transformation for risk monitoring (RM) and control process in risk mitigation and avoidance of agriculture credit failure.

Design/methodology/approach

A self-administered survey was conducted to collect data from 353 risk-related officers and managers in Zarai Taraqiati Bank Limited (ZTBL) Pakistan. The authors used a previously tested scale for the main constructs. The descriptive analyses were used to gauge the model capacity for determining the strength of proposed risk patterns in agriculture risk management.

Findings

The results reveal that risk evaluation process in ZTBL is reasonably efficient in mitigating risks. Given the sensitive nature of farm credit, there is a need of fundamental reforms in risk policy manuals in line with central bank’s agriculture prudential regulations and Basel-III standards. The results fully support H1 and H2, while H3 is partially validated. The result patterns indicate serious issues in risk evaluation process in agriculture finance that is causing higher delinquency in farm credit.

Research limitations/implications

Based on highlighted issues, the authors recommend valuable guidelines in the RM review system for agriculture financing products at ZTBL.

Practical implications

The authors propose remodeling of agriculture risk management and offer valuable insights to the agriculture financial regulators and government in taking policy initiatives in the pre-and-post agriculture risk evaluation process. The proposed model enables RM process to improve farm credit delinquency, particularly in ZTBL and other agriculture banking networks in commercial banks.

Originality/value

This is the first study to empirically investigate RM evaluation process in agriculture risk management of ZTBL in Pakistan, thus, offers new horizon of farm credit regulatory compliance in agricultural sector of Pakistan.

Details

Agricultural Finance Review, vol. 79 no. 1
Type: Research Article
ISSN: 0002-1466

Keywords

Article
Publication date: 16 October 2017

Huifeng Pan, Man-Su Kang and Hong-Youl Ha

Although the study of credit ratings has focused on traditional credit bureau resources, scholars have recently emphasized the importance of big data. The purpose of this paper is…

Abstract

Purpose

Although the study of credit ratings has focused on traditional credit bureau resources, scholars have recently emphasized the importance of big data. The purpose of this paper is to examine both how these data affect the credit evaluations of small businesses and how financial managers use them to stabilize their risks.

Design/methodology/approach

Using data from 97,889 data points for normal guarantees and 1,678 data points for accidents in public funds, the authors explore the effects of trade area grades as well as the superiority of the use of big data when evaluating credit ratings for small businesses.

Findings

The results indicate that the grade information of trade areas is useful in predicting accident rates, particularly for small businesses with high credit scores (AAA-A). On the other hand, the accident rates of small businesses with low credit scores increased from 3.15-16.67 to 3.20-33.3 percent. These findings demonstrate that accident rates for the businesses with high credit scores decrease, but accident rates for businesses with low credit scores increase when using the grades of trade areas.

Originality/value

The authors contribute to the literature in two ways. First, this study provides one of the first investigations on information on trade areas through public financial perspectives, thereby extending the financial risk and retail literature. Second, the current study extends the research on the credit evaluation of small businesses through the big data application of real transaction-based trade areas, answering the call of Park et al. (2012), who recommended an exploration of the relationship between business start-ups and financial risk.

Details

Management Decision, vol. 55 no. 9
Type: Research Article
ISSN: 0025-1747

Keywords

Book part
Publication date: 1 January 2009

Dean Karlan, Tomoko Harigaya and Sara Nadel

In the past decade, microfinance institutions (MFIs) have experienced a boom in innovations of lending products, partly fueled by donors who see microfinance as the next promise…

Abstract

In the past decade, microfinance institutions (MFIs) have experienced a boom in innovations of lending products, partly fueled by donors who see microfinance as the next promise to alleviate poverty. Examples of these new products are the combination of credit with health or life insurance, business and health education, savings products, and the adoption of (or conversion to) individual loan liability. The add-on features generally aim at reducing the vulnerability of clients while contributing to asset creation, hence improving repayment rates and the sustainability of the service. The product innovations typically result from organizations striving to extend outreach, increase impact, and promote sustainability. As in other industries, MFIs typically decide whether to adopt new strategies based on other MFIs’ success with the innovations. Many new microlending products and approaches continue to be developed. However, MFIs must generally rely on qualitative and descriptive case studies and anecdotal evidence on the effectiveness of these innovations to decide whether to implement the new strategies. The usual case study approach does not provide tangible evidence that can enable other organizations to know what changes can be expected if they were to adopt similar product changes.

Details

Moving Beyond Storytelling: Emerging Research in Microfinance
Type: Book
ISBN: 978-1-84950-682-3

Article
Publication date: 22 December 2021

Gia Sirbiladze, Harish Garg, Irina Khutsishvili, Bezhan Ghvaberidze and Bidzina Midodashvili

The attributes that influence the selection of applicants and the relevant crediting decisions are naturally distinguished by interactions and interdependencies. A new method of…

Abstract

Purpose

The attributes that influence the selection of applicants and the relevant crediting decisions are naturally distinguished by interactions and interdependencies. A new method of possibilistic discrimination analysis (MPDA) was developed for the second stage to address this phenomenon. The method generates positive and negative discrimination measures for each alternative applicant in relation to a particular attribute. The obtained discrimination pair reflects the interaction of attributes and represents intuitionistic fuzzy numbers (IFNs). For the aggregation of applicant's discrimination intuitionistic fuzzy assessments (with respect to attributes), new intuitionistic aggregation operators, such as AsP-IFOWA and AsP-IFOWG, are defined and studied. The new operators are certain extensions of the well-known Choquet integral and Yager OWA operators. The extensions, in contrast to the Choquet aggregation, take into account all possible interactions of the attributes by introducing associated probabilities of a fuzzy measure.

Design/methodology/approach

For optimal planning of investments distribution and decreasing of credit risks, it is crucial to have selected projects ranked within deeply detailed investment model. To achieve this, a new approach developed in this article involves three stages. The first stage is to reduce a possibly large number of applicants for credit, and here, the method of expertons is used. At the second stage, a model of improved decisions is built, which reduces the risks of decision making. In this model, as it is in multi-attribute decision-making (MADM) + multi-objective decision-making (MODM), expert evaluations are presented in terms of utility, gain, and more. At the third stage, the authors construct the bi-criteria discrete intuitionistic fuzzy optimization problem for making the most profitable investment portfolio with new criterion: 1) Maximization of total ranking index of selected applicants' group and classical criterion and 2) Maximization of total profit of selected applicants' group.

Findings

The example gives the Pareto fronts obtained by both new operators, the Choquet integral and Yager OWA operators also well-known TOPSIS approach, for selecting applicants and awarding credits. For a fuzzy measure, the possibility measure defined on the expert evaluations of attributes is taken.

Originality/value

The comparative analysis identifies the applicants who will receive the funding sequentially based on crediting resources and their requirements. It has become apparent that the use of the new criterion has given more credibility to applicants in making optimal credit decisions in the environment of extended new operators, where the phenomenon of interaction of all attributes was also taken into account.

Article
Publication date: 1 May 1989

Ehsan Nikbakht and Mohammed H.A. Tafti

An appropriate approach to determine and measure the characteristics of “good” borrowers has always been the subject of inquiry by various lenders both in domestic and…

Abstract

An appropriate approach to determine and measure the characteristics of “good” borrowers has always been the subject of inquiry by various lenders both in domestic and international financial markets. Cost of bad debts incurring from non‐payers and slow payers is a major source of loss which would affect profits and consequently the value of the lending firm. A carefully designed and economically feasible method to select the right borrowers is consistent with the goal of a modern lending institution which is to minimize the risk of bad debts and increase the wealth of its shareholders. The issue of selecting good borrowers is more serious in the case of potential credit card holders where the number of applicants is far greater than the number of commercial, real estate, and other institutional borrowers. Lending institutions cannot afford spending more than a certain limited amount of time to scrutinize the application of each applicant. Competition and increasing cost of information are other reasons that a lender should approve or reject submitted applications in a reasonably short period of time with minimum decision errors.

Details

Managerial Finance, vol. 15 no. 5
Type: Research Article
ISSN: 0307-4358

Article
Publication date: 14 September 2023

Cheng Liu, Yi Shi, Wenjing Xie and Xinzhong Bao

This paper aims to provide a complete analysis framework and prediction method for the construction of the patent securitization (PS) basic asset pool.

Abstract

Purpose

This paper aims to provide a complete analysis framework and prediction method for the construction of the patent securitization (PS) basic asset pool.

Design/methodology/approach

This paper proposes an integrated classification method based on genetic algorithm and random forest algorithm. First, comprehensively consider the patent value evaluation model and SME credit evaluation model, determine 17 indicators to measure the patent value and SME credit; Secondly, establish the classification label of high-quality basic assets; Then, genetic algorithm and random forest model are used to predict and screen high-quality basic assets; Finally, the performance of the model is evaluated.

Findings

The machine learning model proposed in this study is mainly used to solve the screening problem of high-quality patents that constitute the underlying asset pool of PS. The empirical research shows that the integrated classification method based on genetic algorithm and random forest has good performance and prediction accuracy, and is superior to the single method that constitutes it.

Originality/value

The main contributions of the article are twofold: firstly, the machine learning model proposed in this article determines the standards for high-quality basic assets; Secondly, this article addresses the screening issue of basic assets in PS.

Details

Kybernetes, vol. 53 no. 2
Type: Research Article
ISSN: 0368-492X

Keywords

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