Customer Risk Research Articles

Acceptance sampling plans based on percentiles for extended generalized exponential distribution with real data application

In this paper, when the life test is truncated at a pre-determined time, new acceptance sampling plans (ASP) are developed based on percentile lifetime as a quality parameter for the extended generalized exponential distribution (EGED). An inspection of a representative sample of goods or materials from a big lot or batch is used in quality control testing under acceptance sampling plans to assess whether the lot satisfies predetermined quality requirements. For the suggested new acceptance sampling plans, the minimum sample sizes needed to emphasize the determined lifetime percentile are obtained for a given customer's risk. The sample plans' operating characteristic function values and the producer's risk are built at various quality levels for some distribution parameters. Some numerical examples are presented to show how the suggested ASP can be used. A genuine industrial data set from a top Korean automobile is fitted to the EGED relative to other competitors and the applicability of the suggested ASP is illustrated. It is found that the suggested ASP for the EGED can be applied by the researchers in production engineering fields.

Optimasi Fitur Seleksi Random Forest Menggunakan GA Dalam Klasifikasi Data Penyakit Gagal Jantung

Diseases of the heart and blood vessels, such as coronary artery disease (heart attack), cerebrovascular disease (stroke), heart failure (HF), and other pathologies, are collectively referred to as cardiovascular disease (CVD). Globally, around 17 million people a year die from cardiovascular disease, with mortality increasing significantly for the first time in 50 years. Has performed an analysis of the performance of the selection algorithm with case studies predicting the determination of the customer's risk profile. Data mining is the extraction of previously unknown or previously hidden patterns from large databases or data warehouses. This study compares data mining classification models Nave Bayes, Decision Tree, Random Forest, KNN, and SVM to find the most effective model for classifying customer profile data. Later, the most accurate model will be proposed as a replacement model for forecasting the customer's risk profile. As a result, the accuracy value obtained is 82.93% and AUC is 0.896. Then accuracy testing is carried out using the rapidminer application. Testing on rapidminer was carried out with the highest accuracy obtained with an accuracy value of 86.64% and an AUC of 0.880.

Analysis of legal framework solutions to protect retail consumers

PurposeThis article analyses the existing retail and distribution legal framework in which Internet companies operate, focusing on data breaches and other issues of relevance to these companies. In order to identify who should be responsible for the largest share of improving people's quality of life, this study takes into account the perspectives of both consumers and businesses (or service provider). The author states that where there is a high probability of a security or privacy breach and the customer suffers moderate to severe damage, the burden of proof may shift to the corporation. However, the customer's obligation is conditioned by factors such as the customer's risk tolerance, the customer's losses and the efficiency of the security investment.Design/methodology/approachThe author suggests that the decentralized nature of blockchain, information sharing, immutability and smart contracting capabilities have the potential to disrupt established business models and social norms. Challenges related to trust, customs oversight and payments are discussed, as well as the process of creating the framework for electronic commerce. As part of this research, the author has taken into consideration the increasing popularity of Internet shopping.FindingsThe author demonstrates that due to the worldwide reach of the internet and the fast advancement of computer technology, the economies of the globe have grown increasingly linked. Even though e-commerce has been growing rapidly in recent years due to innovations in both technology and international retail and distribution forms, it still confronts a number of challenges.Research limitations/implicationsIn e-commerce that makes use of blockchain technology, there are significant costs associated with transferring data formats, a lack of consensus and limited emissivity in the flow of law and information. Reduced costs and associated negative externalities would be tremendously beneficial for both private enterprise and forward-thinking public policy.Practical implicationsThis paper examines the potential liability concerns that may arise in the context of electronic transactions should a breach of security or privacy occur, as the author shows from a practical standpoint. Computers, mobile devices, tablets, sensors, smart meters and even autos are just some of the many channels via which data may be sent. It is conceivable for data flows in e-commerce, cloud and the Internet of Things to follow a regular pattern. This may endanger the confidentiality or security of the data. These have evolved into a significant barrier that web stores must overcome.Originality/valueThe author argues that resolving disputes related to the processing of electronic transactions is crucial to the growth of e-commerce businesses since customer happiness is directly correlated with business success.

Optimal design of multiple dependent state repetitive group sampling plan for Inverse Power Lomax distribution

A sampling plan named as Multiple Dependent State Repetitive Group Sampling (MDSRGS) plan is introduced for a time-truncated life test given that the underlying distribution of the product's lifetime is Inverse Power Lomax distribution (IPLD). The proposed sampling plan is developed with the help of two already developed sampling plans (MDS and RGS). The two-point approach OC function known as the producer's risk and customer's risk is used to determine the parameters of the proposed plan. An optimization method is used for different values of customer's risk and producer's risk, for static values of experiment termination ratio and mean ratio, to find out plan parameters (minimum size of the sample, the number of acceptance and rejection, and the number of successive lots). Tables are created for different known values of shape parameters of Inverse Power Lomax distribution. The efficiency of the proposed MDSRGS plan is examined by conducting a comparative study. To accompany the results, graphs are also used for visualizing the average sample and acceptance probabilities with the specific mean ratio. Two real-life applications are also incorporated to demonstrate the operating procedure of the proposed plan.

Enterprise's Finances: Possible Customer's Risks when Outsourcing Accounting

The study aims to determine the essence, classification, reduction methods, and the impact on accounting organization of the risks faced by the customer and the contractor when outsourcing accounting. The article examines the types of risks that arise when outsourcing accounting. Based on the use of such scientific methods as logical generalization and comparison, as well as an expert survey, the following classification criteria and types of risks for the customer have been identified: concerning the customer's activities (operational, financial, control); by the level of loss and distortion of information (risk of disclosure of confidential information, risk of loss of customer documentation, accounting risk); by the aspect of emergence (psychological, moral, loss of reputation); by the source of occurrence (subjective, objective), by the possibility of prediction (predicted, unpredictable); by the possible consequences (risk resulting in loss to the customer; risk resulting in the customer receiving a specified level of income). The reasons and consequences of the proposed types of risks have been determined. The authors also have determined the influence of each of them on accounting organization and the main stages of risk management and methods of risk reduction. The practical importance of improving the classification of risks in outsourcing accounting is to assist parties in contractual relations in developing a strategy and forecasting business activities. The theoretical value lies in the possibility of using the research results to develop new classifications.

Information transmission along supply chains: Stock price reaction of suppliers upon a customer's release of qualitative risk information

We examine suppliers' stock price reaction to the disclosure of qualitative risk information from important customers. By leveraging recent advances in textual analysis, we quantify customers' qualitative information disclosure and use it to gauge the spontaneous stock price reaction of the supplier's stock. Using a sample of 2028 customer-supplier-firm observations in China, we find that, on average, the stock price of a supplier negatively reacts to its customer's qualitative risk information disclosure. Our findings are robust to alternative metric of qualitative risk disclosure, alternative metric of stock price reaction, after accounting for customer stock returns, and after mitigating selection bias. Moreover, when comparing the magnitudes of coefficients, we find the impact of the qualitative risk information disclosure on suppliers' stock price is larger than the firm, industry, and macroeconomic information embedded in the same annual report; its value is also greater than the impact of a customer's negative stock price. Last, we show that the adverse impact of customers' qualitative risk information disclosure is more salient if the customers are relatively important or the information is more useful to investors. We discuss implications of our findings to supply chain risk management.

Acceptance sampling plans under two-parameter Quasi Shanker distribution assuring mean life with an application to manufacturing data.

The acceptance sampling plan (ASP) is a statistical tool used in industry for quality control to determine the quality of products by selecting a specified number for testing in order to accept or reject the lot. The main objective is to develop a new ASP based on truncated life tests assuming that the lifetime follows the two parameters Quasi Shanker distribution, since this distribution showed its superiority in providing a better model for some applications than the exponential distribution. The ASP steps are carried out to find the minimum sample sizes needed to assert the certain life mean that are calculated under a given customer's risk. The operating characteristic values of the sampling plan and the producer risk values are obtained. The efficiency of the suggested plans is analyzed based on real data that is fitted to the Quasi Shanker distribution. For various values of the Quasi Shanker distribution parameters, numerical examples are presented for illustrative purposes. The results indicate that the suggested ASP provides smaller sample sizes than other competitors considered in this study. The suggested ASP has been found to provide a substantial sampling economy in terms of reducing the sample. Hence, it is recommended that the ASP can be used in industry and for future research works as double and group ASP.

Logistic Model of Credit Risk Based on MCMC Method

In this paper, the Markov Chain Monte Carlo (MCMC) method is used to estimate the parameters of Logistic distribution, and this method is used to classify the credit risk levels of bank customers. OpenBUGS is bayesian analysis software based on MCMC method. This paper uses OpenBUGS software to give the bayesian estimation of the parameters of binomial logistic regression model and its corresponding confidence interval. The data used in this paper includes the values of 20 variables that may be related to the overdue credit of 1000 customers. First, the Boruta method is adopted to screen the quantitative indicators that have a significant impact on the overdue risk, and then the optimal segmentation method is used for subsection processing. Next, we filter three most useful qualitative variables. According to the WOE and IV value, and treated as one hot variable. Finally, 10 variables were selected, and OpenBU-GS has been used to estimate the parameters of all variables. We can draw the following conclusions from the results: customer’s credit history and existing state of the checking account have the greatest impact on a customer's delinquent risk, the bank should pay more attention to these two aspects when evaluating the risk level of the customer.

INFLUENCE OF MEASUREMENT ERROR DISTRIBUTION THE ACCURACY RATES OF THE CONTROL

The influence of the type of distribution of measurement error and measurement uncertainty on the reliability of the multiparameter measurement testing is studied. Indicators of testing reliability include the probability of a control error of type 2 (P2), as well as the risk of the customer Rз and the risk of the manufacturer Rп. These control reliability indicators were calculated for different values of measurement uncertainty. The study was performed using the Monte Carlo method (imitation modelling). Three models of measurement error distribution were used: normal distribution, triangular (Simpson) distribution, uniform distribution. The results obtained allow us to establish the measurement uncertainty that provides the required risk of the customer (or another indicator of reliability). The following was established. For the symmetric law of error distribution, the highest estimates of the reliability indicators take place with a uniform distribution of the measurement error. The probability of P2 is weakly dependent on the number of controlled parameters and the type of distribution of the measurement error, but it significantly depends on the accuracy of measurements. The customer's risk depends significantly on both the number of controlled parameters and the accuracy of measurements during control.

Optimal pricing in on-demand-service-platform-operations with hired agents and risk-sensitive customers in the blockchain era

On-demand service platforms are popular nowadays. Many platforms hire agents to serve customers who are risk sensitive towards the waiting-time. In this paper, we apply the mean-risk theory to analytically explore how the risk attitude of customers affects the optimal service pricing decision of the on-demand platform, consumer surplus (CS) of customers, the expected profit (EP) and profit risk (PR) of the platform (and the hired service agents). In the basic model, assuming consumers are homogeneous, we find that if the customers are more risk averse (risk seeking), the optimal service price will drop (increase). Comparing among the three different risk attitudes of customers, we find that when the customers are risk seeking, the CS and the platform's EP are highest, even though the platform's PR is also highest. While the opposite happens when the customers are risk averse. In the extended model with a market including customers with different risk attitudes, the blockchain technology helps the platform assess the proportion of risk seeking, risk neutral and risk averse customers accurately. We explore the optimal service prices under both the common pricing policy and the customized pricing policy (with-respect-to customer's risk attitude), and derive the value of blockchain technology mediated customized service pricing strategy. We conclude by highlighting that the risk attitudes of customers play a critical role in determining the optimal on-demand service pricing, and the blockchain technology is a valuable technological tool to help.

Customer's operational risk towards electronic banking products and its mitigation: a covariance-based structural equation modelling approach

Customer's operational risk towards electronic banking products and its mitigation: a covariance-based structural equation modelling approach

Customer's operational risk towards electronic banking products and its mitigation: a covariance-based structural equation modelling approach

The e-banking operations can cause loss or damage to the customers in the form of processing errors, fraud, or via external events. The purpose of this work is to analyse the major risk factors influencing the customers in their e-banking operations. Based on the classic stimulus-response approach, we have identified 15 risk stimuli and four major risk constructs that can cause some encumbrance in e-banking operations. We have also identified some risk mitigation strategies based on the review of literature and focus group discussions. In this work, a stimulus-response model was constructed by linking the operational risk in e-banking with the risk mitigation strategies. Further, we used data from 742 respondents for empirically validating the proposed model. The results of covariance-based structural equation modelling (CB-SEM) revealed that the strategies identified have a positive effect on mitigating the internet banking risk (IBR) only.

Risk Perception Analysis with FAHP in Turkish Brand Patented Automobile Production

Detailed analysis of the products that are accepted as status indicators in the society must be done before the presentation to the market. Cars are also considered as status indicators in society. Performing perception analysis before the presentation to the market will provide many benefits. For products that have not been produced, perception analysis also reveals the expectation of the customer. It can be said that the individual perceives the risk about the product if perception of potential customers is creating a negative impact on the buying trends. Customer's perception of risk affects the purchasing decision of the individual in terms of financial, psychological, physical, functional, social and time criteria. Risk perception encountered in real life in all areas is a multi-criteria concept with uncertainty. In the study, fuzzy logic method which gives successful results in uncertainties was used together with AHP method which is commonly used in multi-criteria decision making (MCDM) problems. The article used the risk perception in Turkish Brand Patented Automobile (TBPA) production with fuzzy analytic hierarchical process (FAHP) method. The potential customer's risk perception and wishes and expectations are presented. Keywords : Perception, MCDM, Fuzzy Logic, TBPA DOI : 10.7176/JSTR/5-3-22

A Mid‐Range Theory of Control and Coordination in Service Triads

The increased frequency of the adoption of service‐based business models by manufacturers, such as solution provision, has given rise to service triads. While there is consensus that actors in service triads are relationally and performatively interdependent, less is understood about how service triads are controlledandcoordinated. In this study, we use an inductive case‐based approach to build an understanding about the roles, approaches, and contextual factors that influence how service triads are controlled and coordinated. We collected and analyzed data from nine companies forming three service triads, each comprising a customer, a manufacturer of an asset, and a service supplier. We synthesized our findings in a theoretical framework, where we show that first, both, controlandcoordination, are present in service triads rather than just control as previously posited. Second, controlling and coordinating service triads is not a single actor's responsibility but rather a collective effort shared by two or three actors. Third, we uncovered four contingent factors that influence the dynamics of how service triads are controlled and coordinated: the customer's risk exposure due to the offering's failure, the substitutability of the offering, the contractual safeguards, and the relationship closeness.

An empirical study of the Volkswagen recall crisis in China: Customer's risk perceptions and behavior responses based on an information flow

ABSTRACTProduct-harm crises usually lead to product recalls, which would attract extensive public concern on the issue of product defects, but it is not yet clear how product recalls influence customers' risk perceptions and behavior responses. In this study, guided by the protective action decision model (PADM) and the heuristic-systematic model (HSM), and through an empirical study involving 467 participants drawn from the customers of Volkswagen, a conceptual model of information flow was developed, which starts from individuals' product experience, strengthening or weakening through information seeking and processing, then directly acts on the risk perception, and ultimately influences protective actions. By using structural equation modeling to test the model, and the results shown that customers' information need, seeking, and processing are significantly related to risk perception, their behavioral intentions (loyalty or boycott) are also significantly influenced by risk perception and product experience. Next, multiple-group analysis is employed to compare the distinctions between two groups of customers (those whose cars belong to the recall, and those not belonging to the recall). Unexpectedly, the role of information processing in the recall group is weaker than that in the not recalled group. At last, theoretical and practical implications and suggestions for further research are also discussed.

MiFID Directive: Which Implementation within Belgian Financial Institutions

By relying on interviews with three financial institutions in Belgium, we determine how the MiFID directive has resulted in practice and how investors perceive these procedural changes. The results reveal some homogeneity between the three institutions both in determining the customer's risk, in the portfolio composition as in the appropriateness of the products sold to the investor's profile.

Securing Transactions in E-Commerce using Visual Cryptography and Steganography

The Contemporary world has seen a tremendous usage of E-Commerce because of its appliance. The Information confidentiality is one of the major requirements to the users of Online Banking Systems. The issue with traditional Online banking applications is that they need to send the sensitive contents associated with the transaction such as Personal Identification Number (PIN), One Time Password (OTP) to the targeting customers in the form of plaintext, which is vulnerable to unauthorized access. Personal Identity theft and phishing attack are common threats of online shopping. Phishing is a technique which involves unauthorized gaining of sensitive information like password, bank details, credit card details from victims, often for malicious reasons, by pretending as a trustworthy entity in an electronic communication. It is a social engineering technique used to mislead users. The solution to the above consequences requires a software application that holds proficient encryption procedures. To enhance the security of the content on the internet, this paper proposes a technique that introduces an idea of Visual Cryptography and Steganography. These methods represent a new approach which would provide a confined information for the fund transfer. The method ensures the security to the customer's data and decreases customer's risk thus preventing identity theft. Keywords: Online shopping; identity theft; Phishing; Visual Cryptography; Steganography

A Novel Concept for the Development of Availability-Oriented Business Models

Globalization, Industry 4.0 and increasing price pressure are only a few impact factors influencing the competition in capital goods industry. Thus, the manufacturers need to differentiate by creating individual bundles, consisting of technical products and lifecycle-oriented services, called Product-Service Systems (PSS). There is an increasing demand of availability-oriented PSS, in which the manufacturer guarantees the availability of the technical product and takes over a part of the customer's manufacturing risk. PSS can be offered by means of different business models. This article presents a novel concept to develop availability-oriented business models, which considers requirement elicitation for technical development of smart machine components, and customer-oriented methods of design thinking approach, i.e. Persona analysis, Value-network mapping and Customer Journey. The concept was validated in three use cases, regarding technology-push and market-pull perspective.

Application and Comparison of Neural Network, C5.0, and Classification and Regression Trees (CART) algorithms in the Credit Risk Evaluation Problem (Case Study: A Standard German Credit Dataset)

Due to the reducing global economic stability, the demand of banks for predicting their customer's credit risk has significantly increased and has become more critical, still challenging than ever. This paper addresses the problem of credit risk evaluation of bank's customers utilising data mining tools. Three classification techniques include: neural network, C5.0, and classification and regression trees (CART) algorithms. In order to evaluate the performance of the classification techniques, an innovative two-stage evaluation process is proposed. Firstly, the optimal status of algorithms is found by tuning its parameters. Secondly, these tuned algorithms are ranked by the analytical hierarchy process (AHP) method while four criteria of overall accuracy, precision, sensitivity, and specificity are considered. As a case study, a standard German credit dataset are used to validate the performance of the proposed algorithms. It is illustrated that the neural network algorithm is the superior algorithm to evaluate bank customers' credit risk.

Prevention of crime in B2C E-Commerce: How E-Retailers/Banks protect themselves from Criminal Sctivities

Online shopping and banking institutes are a current trend in e-commerce, where online shopping and banking are frequently used over the Internet. With the fast growth of the technology and on-line practices, criminal activities also have grown dramatically. Customer trust in the security of online activities is extremely immportant for successful trading. Both buyers and sellers are vulnerable to online risks. With the current focus being on precautions that individual buyers and sellers should consider protecting themselves from criminal activities online, the issue of what e-commerce websites should do to minimize their customer's risk is apparently a neglected area in current literature. This research analyses deficiencies in online shopping and online banking websites, and identifies suitable procedures that e-commerce operators may apply to minimize the risk of criminal activities.