适合中小型制造企业的客户信用评估的研究

本在对中小型制造企业的客户管理调研基础上，建立了适合中小型制造企业的信用评估指标体系，提出了基于AHP、TOPSIS和聚类分析的客户信用评估方法，并在上海某企业进行了实践，取得了较好的效果。     

A online credit evaluation method based on AHP and SPA

Online credit evaluation is the foundation for the establishment of trust and for the management of risk between buyers and sellers in e-commerce. In this paper, a new credit evaluation method based on the analytic hierarchy process (AHP) and the set pair analysis (SPA) is presented to determine the credibility of the electronic commerce participants. It solves some of the drawbacks found in classical credit evaluation methods and broadens the scope of current approaches. Both qualitative and quantitative indicators are considered in the proposed method, then a overall credit score is achieved from the optimal perspective. In the end, a case analysis of China Garment Network is provided for illustrative purposes.     

Research on the vehicle routing problem with interval demands

In this paper, a vehicle routing problem with interval demands is investigated based on the motivation of dispatching vehicles to deliver perishable products in practice. A nonlinear interval-based programming method is used to build a model for the vehicle routing problem with interval demands, which assumes that demands of customers are uncertain but fall in given intervals and actual demand of a customer becomes known only when the vehicle visited the customer. A vehicle-coordinated strategy was designed to solve the service failure problem. A hybrid algorithm based on the artificial immune system is also proposed to solve the model for vehicle routing problem with interval demands. The validity of methods and sensitivity analysis are illustrated by conducting some numerical examples. We find that the tolerant possibility degree of interval number has significant impacts on the distances. The planned distance strictly increased, while the additional distance strictly decreased and the total distance after coordinated transport has a U-typed relationship with the tolerant possibility degree of interval number.     

一种基于三参数区间灰色语言变量的多属性群决策方法

针对属性值为三参数区间灰色语言变量的不确定型多属性决策问题进行了研究,本文将语言变量和三参数区间数融合, 提出了三参数区间灰色语言变量的概念, 定义了三参数区间灰色语言变量的运算规则和可能度公式,在此基础上建立了基于投影模型的三参数区间灰色语言变量的多属性群决策方法。最后,通过对移动银行服务质量评估案例验证本模型的有效性。     

基于迁移学习的客户信用评估模型研究

客户信用评估是银行等金融企业日常经营活动中的重要组成部分。一般违约样本在客户总体中只占少数,而能按时还款客户样本占多数,这就是客户信用评估中常见的类别不平衡问题。目前,用于客户信用评估的方法尚不能有效解决少数类样本稀缺带来的类别不平衡。本研究引入迁移学习技术整合系统内外部信息,以解决少数类样本稀缺带来的类别不平衡问题。为了提高对来自系统外部少数类样本信息的使用效率,构建了一种新的迁移学习模型:以基于集成技术的迁移装袋模型为基础,使用两阶段抽样和数据分组处理技术分别对其基模型生成和集成策略进行改进。运用重庆某商业银行信用卡客户数据进行的实证研究结果表明:与目前客户信用评估的常用方法相比,新模型能更好地处理绝对稀缺条件下类别不平衡对客户信用评估的影响,特别对占少数的违约客户有更好的预测精度。     

A multi-depot period vehicle routing problem arising in the utilities sector

This paper considers the resource planning problem of a utility company that provides preventive maintenance services to a set of customers using a fleet of depot-based mobile gangs. The problem is to determine the boundaries of the geographic areas served by each depot, the list of customers visited each day and the routes followed by the gangs. The objective is to provide improved customer service at minimum operating cost subject to constraints on frequency of visits, service time requirements, customer preferences for visiting on particular days and other routing constraints. The problem is solved as a Multi-Depot Period Vehicle Routing Problem (MDPVRP). The computational implementation of the complete planning model is described with reference to a pilot study and results are presented. The solution algorithm is used to construct cost-service trade-off curves for all depots so that management can evaluate the impact of different customer service levels on total routing costs.     

面向移动商务的多维异质推荐信任评估模型

针对移动商务环境下供应商信任缺失问题,建立了一种多维异质推荐信任评估模型。该模型实现了多种异质评价信息与直觉模糊信息的转换,较全面、细腻刻画了评价信息的主观性和模糊性。为了避免推荐者和信任属性的主观赋权带来的二次不确定性,给出了基于原始异质评价信息的熵权法。考虑到推荐者的相关领域熟悉程度,利用信心直觉模糊集成算子计算综合信任直觉模糊数,并依据相对接近度进行信任决策。最后,通过一个移动商务供应商的信任评估实例验证该模型的有效性和合理性。     

基于相对熵和可变模糊集理论的战略性新兴产业企业信用评价

针对现有模糊评价方法缺乏动态可变性的不足,将可变模糊集理论引入到战略性新兴产业企业信用评价中;同时,考虑到主观赋权法与客观赋权法各有优缺点,采用基于相对熵的组合赋权法确定指标权重,从而构建了基于相对熵和可变模糊集理论的战略性新兴产业企业信用评价模型.模型克服了现有模糊评价方法的不足,较好的兼顾了赋权的主观偏好和客观信息,提高了信用评价结果的可信度、可靠性与稳健性.以20家战略性新兴产业上市公司为样本,实证分析结果说明了模型的可行性和适用性.     

TOPSIS method for quality credit evaluation: A case of air-conditioning market in China

Quality credit is a new concept invented in China and to the best of our knowledge, there hasn’t been a widely-accepted quality credit indicator system and no quantitative method has been employed in quality credit evaluation up to now. To take the researches on quality credit a step further, this paper aims to establish a quality credit evaluation indicator system for air-conditioning enterprises in Chinese market and use TOPSIS (technique for order preference by similarity to ideal solution) method to evaluate quality credit of the enterprises. Based on the data of 8 air-conditioning enterprises, including 6 Chinese enterprises and 2 Japanese enterprises, three experiments with three different indicator systems are used to determine the final indicator system and verify the feasibility and effectiveness of TOPSIS. In Experiment one, an original indicator system is established to evaluate the quality credit of the 8 enterprises. In Experiment two and three, two reasonably adjusted indicator systems are used and the indicator system in Experiment three is the final one that we recommend. The analysis of experiments verifies that the proposed quality credit indicator system is reliable and TOPSIS is suitable for quality credit evaluation.     

基于客户信用度的物流配送车辆路径问题研究

针对当前城市物流配送过程中普遍存在的客户中途取消订单、无故退换货等交易违约问题,引入客户信用度的测度方法。根据客户历史交易违约数据计算客户信用值,并转化求解客户信用度,构建了包含车辆配送成本、租赁成本以及违反时间窗惩罚成本的配送路径优化模型。设计了一种遗传(GA)-禁忌搜索(TS)混合算法进行模型求解,在算法过程中应用精英保留策略进行循环迭代寻优。结合重庆某外卖物流配送网络的实例数据,验证了模型和算法的有效性和可行性。实验结果给出了不同服务策略下的物流配送调度方案,并进行了基于客户信用度的客户配送服务序列调整比较和敏感度分析。研究表明客户信用等级的合理划分可以有效降低物流配送成本和提高客户服务水平。     

Developing a measure of risk adjusted revenue (RAR) in credit cards market: Implications for customer relationship management

Current models of customer lifetime value (CLV) consider the discounted value of profits that a customer generates over an expected lifetime of relationship with the firm. This practice can be misleading in the financial services markets because it ignores the risk posed by the customer (such as delinquency and default). Specifically, in the credit card market, the correlation between revenue and risk is positive. Therefore, firms need to adjust a customer’s profits for the associated risk before developing a measure of customer lifetime value. We propose a new measure, risk adjusted revenue (RAR), that can incorporate multiple sources of risk and demonstrate the usefulness of the proposed measure in correctly assessing the value of a customer in the credit card market. The model can be extended to compute risk adjusted lifetime value (RALTV). We use the RAR metric to understand the effectiveness of different modes of acquisition, and of retention strategies such as affinity cards and reward cards. We find that both reward- and affinity-cardholders generate higher RAR than non-reward and non-affinity cardholders respectively. The ordering of different modes of acquisition with respect to RAR (in decreasing order) is as follows: Internet, direct mail, telesales, and direct selling.     

Multi-group discrimination using multi-criteria analysis: Illustrations from the field of finance

The primary objective in the discrimination problem is to assign a set of alternatives into predefined classes. During the last two decades several new approaches, such as mathematical programming, neural networks, machine learning, rough sets, multi-criteria decision aid (MCDA), etc., have been proposed to overcome the shortcomings of traditional, statistical and econometric techniques that have dominated this field since the 1930s. This paper focuses on the MCDA approach. A new method to achieve multi-group discrimination based on an iterative binary segmentation procedure is proposed. Five real world applications from the field of finance (credit cards assessment, country risk evaluation, credit risk assessment, corporate acquisitions, business failure prediction) are used to illustrate the efficiency of the proposed method as opposed to discriminant analysis.     

服务管理目标优化配置的Fuzzy-QFD线性规划模型构建与求解

由于服务管理的复杂性和模糊性,现有方法难以有效解决基于主观语言评价的服务质量改进问题。本文拓展了质量功能展开(QFD)方法在服务业中的应用,通过构建一个模糊线性规划模型,以求解最大化提高顾客需求综合满意度的企业能力优化配置问题。首先基于顾客感知-期望差距的模糊评估确定顾客需求、需求权重和边界约束等模型参数,接着运用模糊线性回归和非对称三角模糊数的隶属函数,将含有模糊变量的模糊线性规划问题转化为经典线性规划问题,进而求得不同模糊条件下的模型解。最后通过网购平台的实例验证了模型的有效性和可行性。     

A comparison of strategies to develop a customer default scoring model

Behavioural scoring models are generally used to estimate the probability that a customer of a financial institution who owns a credit product will default on this product in a fixed time horizon. However, one single customer usually purchases many credit products from an institution while behavioural scoring models generally treat each of these products independently. In order to make credit risk management easier and more efficient, it is interesting to develop customer default scoring models. These models estimate the probability that a customer of a certain financial institution will have credit issues with at least one product in a fixed time horizon. In this study, three strategies to develop customer default scoring models are described. One of the strategies is regularly utilized by financial institutions and the other two will be proposed herein. The performance of these strategies is compared by means of an actual data bank supplied by a financial institution and a Monte Carlo simulation study.     

Enhanced decision support in credit scoring using Bayesian binary quantile regression

Fierce competition as well as the recent financial crisis in financial and banking industries made credit scoring gain importance. An accurate estimation of credit risk helps organizations to decide whether or not to grant credit to potential customers. Many classification methods have been suggested to handle this problem in the literature. This paper proposes a model for evaluating credit risk based on binary quantile regression, using Bayesian estimation. This paper points out the distinct advantages of the latter approach: that is (i) the method provides accurate predictions of which customers may default in the future, (ii) the approach provides detailed insight into the effects of the explanatory variables on the probability of default, and (iii) the methodology is ideally suited to build a segmentation scheme of the customers in terms of risk of default and the corresponding uncertainty about the prediction. An often studied dataset from a German bank is used to show the applicability of the method proposed. The results demonstrate that the methodology can be an important tool for credit companies that want to take the credit risk of their customer fully into account.     

An intelligent-agent-based fuzzy group decision making model for financial multicriteria decision support: The case of credit scoring

Credit risk analysis is an active research area in financial risk management and credit scoring is one of the key analytical techniques in credit risk evaluation. In this study, a novel intelligent-agent-based fuzzy group decision making (GDM) model is proposed as an effective multicriteria decision analysis (MCDA) tool for credit risk evaluation. In this proposed model, some artificial intelligent techniques, which are used as intelligent agents, are first used to analyze and evaluate the risk levels of credit applicants over a set of pre-defined criteria. Then these evaluation results, generated by different intelligent agents, are fuzzified into some fuzzy opinions on credit risk level of applicants. Finally, these fuzzification opinions are aggregated into a group consensus and meantime the fuzzy aggregated consensus is defuzzified into a crisp aggregated value to support final decision for decision-makers of credit-granting institutions. For illustration and verification purposes, a simple numerical example and three real-world credit application approval datasets are presented.     

供应链融资模式下中小企业信用风险评估研究——基于左右得分的模糊TOPSIS算法

中小企业信用风险评价是供应链融资实现的关键。本文在对已进行的研究总结的基础上,建立了一套比较完整的评价指标体系,然后运用DEA、AHP和基于左右得分的模糊TOPSIS相结合的评价方法对中小企业在供应链融资中的信用风险进行评价。最后,将方法成功应用到具体实例,结果表明将AHP方法和左右得分模糊TOPSIS结合应用到多层次指标评价问题是合理、可行的,能够为供应链融资信用风险评估提供决策依据。     

改进的云重心评判法在高技术企业信用评价中的应用

提出一种改进的云重心评判法,并应用于高技术企业信用评价.首先,借鉴TOPSIS法的基本思想,基于理想状态和负理想状态,对综合云重心向量进行归一化,并采用修正的加权偏离度来衡量云重心的变化,从而克服了传统云重心评判法的不足.其次,针对高技术企业信用评价中的不确定性,运用改进的云重心评判法对高技术企业信用状态进行实证测评,结果证明了该方法的合理性和适用性.     

考虑预算约束的旅游服务质量提升决策方法

通过对Kano模型的定量化分析,从最大化顾客满意视角提出考虑预算约束的旅游服务质量提升决策方法。采用问卷调查方式获取顾客旅游服务质量因素的评价信息;依据Kano模型,对评价信息进行处理并建立顾客满意度与旅游服务质量满足水平的关系函数(S-CR);基于S-CR函数构建考虑预算约束条件下、顾客满意最大化的旅游服务质量提升决策模型,通过求解确定最优的预算分配方案。最后,通过实证验证模型的可行性与有效性。     

农户信用评估系统的设计与运用研究

农户信用评估的研究对推动农村消费信用的发展，促进农村经济良好运行十分重要。本在构造农户信用评估的指标体系的基础上，提出了农户信用评估神经网络模型的算法，利用实际搜集到的农户资料进一步建立了农户信用评估模型，继而构造了农户信用评估系统，并举例说明了该系统的实际运用，以期能为农村经济发展中的农户信用评价及相关研究提供一丝基础性启发。