Customer Information Research Articles

A Campus-based Chatbot System using Natural Language Processing and Neural Network

A chatbot is designed to simulate human conversation and provide instant responses to users. Chatbots have gained popularity in providing automated customer support and information retrieval among organisations. Besides, it also acts as a virtual assistant to communicate with users by delivering updated answers based on users' input. Most chatbots still use the traditional rule-based chatbot, which can only respond to pre-defined sentences, making the users unlikely to use the chatbot. This paper aims to design and build a campus chatbot for the Faculty of Information Science & Technology (FIST) of Multimedia University that facilitates the study life of FIST students. Before the FIST chatbot can be used, natural language processing techniques such as tokenisation, lemmatisation and bag of word model are used to generate the input that can be used to train the neural network model (multilayer perceptron model). It makes the FIST chatbot comprehends user intent by analysing their questions, enabling it to address a broader range of inquiries and cater to the student's need with accurate answers or information related to the Faculty of Information Science & Technology. Besides, we also developed the backend interface allowing the admin to add and edit the dataset in the proposed chatbot and enable it continuously responds to the student with the latest and updated information.

Information Sharing and Personalized Pricing in Online Platforms

With the rise of big data technology, an online platform can easily gather customer information to engage in price discrimination and obtain additional profits. Sharing customer information with a third-party seller increases the platform’s commission and information revenue, but the seller’s personalized pricing using customer information intensifies the price competition, which may damage the profitability of the platform’s own product. Whether to share information remains an unsolved strategy decision for the platform. We employ a game-theoretic model to characterize the interplay of information sharing by the platform and the pricing strategies of two firms. We consequently study four basic scenarios where the two firms adopt either uniform or personalized pricing policies. In equilibrium, the seller does not always have incentives to acquire information, and the platform is not always willing to share information. Intriguingly, with different combinations of the commission rate and the new consumer ratio, the equilibrium of the overall system has four possible results where the information may not be used for price discrimination. With a relatively high commission rate and a low new consumer ratio, the platform no longer pursues a demand for its own product and lets the seller occupy the whole market, which leads to the lowest consumer surplus and social welfare. We finally show that in the event of a relatively high commission rate, prohibiting information sharing increases consumer surplus and social welfare, verifying the necessity of regulations. These results could provide useful guidelines for platform managers and regulators to better design information sharing and price discrimination policies.

Determining Intrusion Attacks Against Online Applications Using Cloud-Based Data Security

Cloud technology makes it possible for users to access information from anywhere, all the time, on any device, and that is the major cause of the many different types of assaults. In principle, multiple dangers, including data leakage, information leakage, and unauthorized information accessibility, are active in cloud environment layering. Modern technological advancements are made accessible on a daily basis through cloud technology. In the cloud, access control and encryption solutions are more complicated. Because of this greater level, security flaws in online applications and systems are more likely to occur. Somewhere at the ends of the end nodes, a malignant insider can carry out protection assaults. Nevertheless, problems with user privacy and data protection on cloud-based social networking sites continue to exist. Such problems are not known to users. On that social networking site, they post a variety of images, videos, and private information that endures even after eradication. However, some of the data that has been made public was intended to be kept private; as a result, online social information has significantly increased the risk of personally identifiable information leaking. The context of cloud technology depends on the customer capabilities such as quick storing and retrieving offered through cloud computing environments. Dependable cloud providers use a number of methodologies to deliver various digital services, creating a variety of security risks. In this paper, the study of determining intrusive cyber-attacks over the online applications using the cloud data security. Restricting access to shared resources is essential to prevent hackers from stealing vulnerabilities in cloud computing to get unauthorised access to a user's activities as well as information. Gaining access to customer information and obstructing the use of cloud computing are the primary objectives of intrusions on cloud services.

Distribution and Delivery Model on E-Commerce Service for MSMEs

This research aims to develop an e-commerce service application model for Micro, Small, and Medium Enterprises (MSMEs) that facilitates the sale of MSME products online. The application model allows MSMEs to expand market reach and sell products more efficiently through the internet. In this application, data about customer locations, product locations, and information about available road networks are collected and entered into the system. Furthermore, using Dijkstra's method, the shortest route from the initial location or distribution point to the customer can be calculated automatically by considering factors such as distance, time, and transportation that may affect the speed of delivery. The MSMEs e-commerce service application model can optimize distribution and delivery with the aim of increasing the competitiveness and growth of MSMEs in the digital era. The results obtained in this study provide guidance and contributions in the development of a sustainable e-commerce system for MSMEs.

Risk Factors Assessment of Smart Supply Chain in Intelligent Manufacturing Services Using DEMATEL Method With LINGUISTIC q-ROF Information

With the rapid development of technological informatization, competition among enterprises is gradually transitioning from being "production-centered" to being "customer-centric," making service-oriented enterprises increasingly important. In addition to this, as global manufacturing advances in the process of intelligent manufacturing (IM), there is growing attention on the integration of manufacturing and the service industry, which has garnered the interest of numerous experts and scholars in the field of intelligent manufacturing services (IMS). This article combines intelligent manufacturing enterprises, intelligent service nodes, and consumers. Based on the background of intelligent manufacturing services, it collected risk factors within the smart supply chain (SSC) that connect different service nodes. These factors were evaluated by experts using a proposed linguistic q-rung orthopair fuzzy weighted averaging (Lq-ROFWA) operator in combination with the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method for aggregation operations. Finally, we obtain the conclusions that the most influential factor affecting other risk factors is the inadequate identification of core customer needs; and the most important risk factor for smart supply chains oriented to intelligent manufacturing services is the leakage of customer information. After analyzing the relevant data, we will provide some theoretical and managerial implications for IM enterprises.

Water meter inaccuracy assessment and operation control considering customer information

Water Meter Inaccuracy (WMI) is one of the apparent loss components contributing to an increase in non-revenue water. The estimation of the volume of water lost due to WMI and control within a utility is still a challenge; thus, the current study investigated its field operation using customer consumption records. A few studies estimated WMI using data obtained in a controlled experimental environment; however, WMI assessment requires a significant computation that includes unforeseen field fault operating environment. This study is of the view that experimental results are necessary for meter type selection, based on the designed flow characteristics and proposes a WMI assessment and control approach in which the field under- or over-registering meters are identified in a billing system. The analysis of this study reveals that a utility operation environment, economic factor and social trust can be efficiently improved using customers water consumption records.

Farmstead-Specific Weather Risk Prediction Technique Based on High-Resolution Weather Grid Distribution

In recent years, the importance and severity of weather-related disasters have escalated, attributed to rising temperatures and the occurrence of extreme weather events due to global warming. The focus of disaster management has shifted from crisis management (e.g., repairing and recovering from damage caused by natural disasters) to risk management (e.g., prediction and preparation) while concentrating on early warning, thanks to the development of media and communication conditions. The Rural Development Administration (Korea) has developed the “early warning service for weather risk management in the agricultural sector” that detects weather risks for crops from high-resolution weather information in advance and provides customized information to respond to possible disaster risks in advance in response to the increasing number of extreme weather events. The core technology of this service is damage prediction technology that determines the overall agricultural weather risk level by quantifying the current growth stage of cultivated crops and the probability of possible weather disasters according to the weather conditions of the farm. Agrometeorological disasters are damages caused by weather conditions that can affect crops and can be predicted by estimating the probability of damage that may occur from the interaction between hazardous weather and crop characteristics. This review introduces the classification of possible weather risks by their occurrence mechanisms, based on the developmental stage of crops and prediction techniques that have been developed or applied to date. The accumulated crop growth and weather risk information is expected to be utilized as support material for farming decision-making, which helps farmers proactively respond to crop damage due to extreme weather events by providing highly reliable disaster forecasts through the advancement of prediction technology.

Online transfer learning-based residential demand response potential forecasting for load aggregator

Accurate demand response (DR) potential forecasting is the basis for load aggregators (LA) to make optimal bidding strategies in DR market trading. LAs usually face practical challenges when they perform forecasts for those new customers who have no historical response data. Transfer learning provides a promising solution to this problem by leveraging knowledge acquired from other existing contracted customers. However, traditional transfer learning methods are trained offline and cannot make use of the latest response information of these new customers, which may result in large forecasting errors since the response behavior of new customers usually dynamically changes. To address the above issues, this paper proposes an online transfer learning-based DR potential forecasting framework, in which two forecasting models are established. The first one is built using the historical data of existing customers and this model is then transferred to the target domain (i.e., new customers) by parameter sharing and fine-tuning. The second model is built using the local response data of new customers, which gradually accumulates with the increasing participation of DR events. These two models are combined by an adaptive ensemble framework based on their online performances, thus enabling it to dynamically track the changes in new customers' response behavior. Case studies on a real-world dataset validate the effectiveness of the proposed framework.

CYBERSECURITY IN BANKING: A GLOBAL PERSPECTIVE WITH A FOCUS ON NIGERIAN PRACTICES

The paper review cybersecurity practices in banking, with a specific focus on Nigerian banks. Cybersecurity has become a paramount concern in the banking industry worldwide, given the escalating frequency and sophistication of cyber threats. This study provides an overview of the global landscape of cybersecurity in banking, with a specific focus on practices observed in Nigeria. The global banking sector is witnessing a surge in digital transformation, marked by the adoption of advanced technologies and online financial services. However, this digitization brings with it unprecedented cybersecurity challenges, ranging from data breaches and ransomware attacks to sophisticated financial fraud. Financial institutions globally are compelled to fortify their cybersecurity frameworks to protect sensitive customer information, ensure the integrity of financial transactions, and maintain trust in the digital financial ecosystem. Nigeria, as a key player in the African banking landscape, faces unique cybersecurity challenges and has developed distinct strategies to safeguard its financial institutions. This study explores the cybersecurity practices employed by Nigerian banks, considering regulatory frameworks, incident response mechanisms, and collaborative efforts with international cybersecurity entities. The analysis encompasses case studies illustrating real-world cyber threats and incidents in both global and Nigerian banking contexts. It investigates the effectiveness of cybersecurity measures implemented by Nigerian banks, shedding light on the nation's response to evolving cyber risks. Furthermore, the study delves into collaborative initiatives between Nigerian banks, regulatory bodies, and international cybersecurity organizations to enhance information sharing, threat intelligence, and collective defense mechanisms. It also explores the role of public awareness campaigns in fostering a cyber-resilient banking environment. The insights provided aim to contribute to the collective understanding of cybersecurity challenges in the global banking sector while offering a nuanced perspective on Nigerian practices. As digital financial ecosystems continue to evolve, the findings underscore the importance of ongoing adaptation, collaboration, and innovation in safeguarding the integrity and trustworthiness of banking systems, both on a global scale and within the Nigerian context.
 Keywords: Cybersecurity, Banking, Global Perspective, Nigerian, Cybersecurity, Artificial Intelligence.

Influence of Customer Integration and Information Systems Integration on Organizational Performance of Clearing and Forwarding Firms in Kenya

Influence of Customer Integration and Information Systems Integration on Organizational Performance of Clearing and Forwarding Firms in Kenya

Research on Unfair Competition in the Digital Economy: Innovation, Regulation, and Balancing Strategies

Online unfair competition is on the rise, disrupting market order and violating consumer rights in the context of the fast developing digital economy. Therefore, the purpose of this essay is to investigate the nature and forms of unfair competition in the internet economy and how it affects innovation. In addition, it examines the shortcomings of the existing regulatory frameworks and suggests balanced approaches and effective techniques to encourage the digital economy's healthy expansion. Defamation, misleading advertising, and web crawlers are common examples of unfair competition on the Internet. Another kind of unfair competition is data mining, which involves acquiring rivals' customer information or trade secrets. These behaviors not only undermine the principle of fair competition in the market but also mislead consumers and harm their interests. There is a certain relationship between unfair competition on the Internet and innovation. On the one hand, unfair competition on the Internet may stem from companies' excessive pursuit of innovation. To stand out in fierce market competition, companies may resort to unfair means to gain competitive advantages. On the other hand, unfair competition on the Internet may also inhibit innovation, as companies may reduce their innovation investment due to concerns about competitors taking unfair measures. The current regulatory mechanism has certain shortcomings in addressing unfair competition behavior on the Internet. On the other hand, the regulatory mechanism's definition of unfair competition on the Internet is not clear enough, which makes it difficult for regulatory agencies to accurately judge and handle it in the law enforcement process. At the same time, the regulatory mechanism's punishment for unfair competition on the Internet is not strong enough to effectively deter illegal behavior. To address unfair competition on the Internet, it is necessary to establish effective regulatory mechanisms and balance measures. Firstly, it is necessary to clarify the definition of unfair competition on the Internet and establish specific regulatory rules and standards. Secondly, it is necessary to strengthen the enforcement efforts of regulatory agencies, increase the severity of penalties, and ensure the effectiveness of regulatory mechanisms. At the same time, it is also necessary to strengthen corporate self-discipline, promote compliance with market rules, and jointly maintain market order. In short, unfair competition on the Internet has had a negative impact on the development of the digital economy, and it is necessary to establish effective regulatory mechanisms and balance measures to promote the healthy development of the digital economy.

Effectiveness of Homeopathic Medicine in Canine Urinary Incontinence: Outcomes of Owner Response Survey

With advances in data collection via electronic surveys, they have become a popular tool for researchers especially with the changes in normalcy caused by the Coronavirus pandemic. During this pandemic period, previously collected traditional format paper data on Homeopet ™ ‘Leaks No More’ was collated, analysed, written up, reviewed, and published Farrington TJ and Smith . The current study was again an owner response survey like the previous studies but done electronically. In the case of this Homeopet™ 'Leaks No More' survey, the selection criteria used to identify eligible participants who had purchased the product directly from the manufacturer's website helped to ensure that the responses collected were from genuine product users including first time and repeat users. This helped to eliminate potential bias in the responses and ensured that the data collected was of high quality. It ensured elimination of spurious responses and replies from unverified users. The use of gratuities only to those who agreed to provide fully completed surveys containing a true assessment of their pets' responses with an email and other contact details including agreement to be contacted helped to improve the response rate and quality of data collected. In fact the normal verifying of email addresses and names of respondents was turned off for data protection requirements after collection before sending data to analyst and authors removing only from them ability to cross-reference to an original mail shot offering the survey to people who had joined the mailing list on Homeopets own sales site, the researchers were after completion of the paper able to ensure that the data collected was authentic and reliable without exposing any personal data for the participants from the survey collection while still ensuring it was verifiable. Despite striving to write the survey questionnaire in a balanced inclusive way so that it would resonate with a wide range of perspectives and viewpoints across a broad range of views, one can assume that those who filled it out were more likely to have been open to complementary medicine or to have a pet who had failed to respond or responded poorly to conventional therapy. It also meant that emails and names could be verified and if necessary, through the email DNS, and thus could be contacted to complete any missing data needed, which thanks to response numbers was not necessary so the data remained completely confidential. As Homeopet ™ ‘Leaks No More’ is an OTC (Over the Counter) retail product which during the pandemic sold primarily online through various online retailers due to the restricted access to bricks and mortar retailers, shops, and stores there was little or no direct access to or contact with the end users and thus the electronic survey proved highly effective in that environment. Overall, electronic surveys have proven to be a more successful and time-efficient way of collecting data compared to traditional methods as shown in this research paper. The high-quality data collected in this study will be valuable for future research and product development in the field of complementary medicine for pets. Methods and Outcomes: An online questionnaire template was developed using questionnaires from the previous studies and he decision was to use SurveyMonkey® as the mode of delivery to participants. Homeopet™ LLC employed an independent consultant to design and develop the front end for owner response input and the back end for data collection. Ms Jenny Doran Halcyon Days Ltd. worked with Dr Farrington and his team, some external individuals unfamiliar with the project (to ensure the public would understand the questionnaire fully), including staff at Homeopet familiar with the process, to test prototype versions of the SurveyMonkey® and tailor them to the projects requirements till the entire team came to a consensus acceptable to Homeopet™ in the time constraints available. The survey was then made available online with an invitation to participate added to the purchase process on the manufacturer’s website. Other online outlets were considered but were not open to the authors having access to their customer information even for verification purposes. This outlet area is being worked on for future surveys.

IoT-Driven Smart Trolleys and Robots: Transforming Retail with Edge Computing

Time is an expensive resource in our fast-paced society, and people frequently lose a good deal of it waiting at supermarket and shopping mall checkout counters. An automated intelligent shopping cart has been designed for supermarkets to solve the shortcomings of the current billing systems. This trolley reduces the amount of time customers spend at the checkout counter, improving convenience and saving time, by scanning products using the Atmega 328 controller and RFID tags. Customers can better their shopping experience by monitoring the amount of items and the overall cost thanks to the digital document shown on an LCD. With electronic bills sent via email and thorough purchase information available through the shop's website, the intelligent cart manages shopping and payment procedures, allowing customers to buy things and leave the store fast. In order to manage product and customer information, this system needs an Arduino board, an RFID reader, an RFID tag, an LCD display, a database manager, and a website. Leveraging the Internet of Things (IoT) for smooth connection with the worldwide network, the administrator can access this information anywhere.

Tasks, ways and approaches to improve service maintenance of machinery (on the example of Rostselmash)

The article is devoted to the issues of improving the system of service maintenance of machinery manufactured by Rostselmash Combine Plant LLC. The strategic objective is to develop the corporate service network in terms of its quantitative expansion and qualitative growth. The introduction lists the main points of the company’s service network strategy. The main body of the publication contains the main advantages of transitioning to a new system of guarantee fulfilment, as well as approaches to achieve such a transition. Potential difficulties and unresolved problems hindering the improvement of the service delivery system are outlined. Also, in the ‘Materials and Methods’ section of the publication, approaches to the improvement of machinery service are formulated, touching upon such areas of this work as: payment for warranty services, termination of warranty obligations, training of mechanics and specialists of service centers, etc. The main business objectives covering the following aspects of service improvement work are presented as the results of the research study: franchise development, increasing customer satisfaction, information gathering, and cost reduction. Each of the outlined aspects is given its extended description in the third section of the article.

GA optimization-based BRB AI reasoning algorithm for determining the factors affecting customer churn for operators

Customer churn directly leads to the weakening of the economic efficiency of an operator and even the loss of its competitive advantage in the market share. The identification and prediction of customer churn in the era of interconnection has become more complex, as it not only is based on the analysis of customer information and customer churn data but also must take into account the intricate relationships between the market economy and culture. In the era of big data, numerous predictive models are based on more redundant features, which increases the complexity of the algorithms and the difficulty of analyzing customer churn. Therefore, in this paper, a belief rule base (BRB) artificial intelligence inference algorithm based on GA optimization to determine the factors affecting customer churn for operators proposed. First, customer churn data from a website are analyzed, and features are extracted. Second, a BRB is established from the experience of operator experts, and a BRB inference prediction model is constructed for predicting and analyzing customer churn. Finally, the BRB model is optimized by GA optimization, the input characteristics with high feature weights are obtained, and the accuracy of the churn analysis is verified according to the obtained features. The results show that this method not only outperforms the comparative SVM and BP neural network models for predicting customer churn but also provides better judgment of the main inputs of customer churn, thus reducing the reliance on input information, optimizing the complexity of the algorithm, and enabling operators to obtain a more accurate understanding of the main factors that lead to churn.

Strengthening Customer Data Protection in Healthcare: An Empirical Analysis of Data Privacy and Security Measures for Ensuring Customer Information Security

Strengthening Customer Data Protection in Healthcare: An Empirical Analysis of Data Privacy and Security Measures for Ensuring Customer Information Security

The impact of AI-based cyber security on the banking and financial sectors

BD and AI are now transforming the banking and finance industry at a very fast pace, which is leading to change in the banking and finance institutions. This change is making them better, customer-oriented and financially rewarding organizations. Big data and AI have been useful in the banking and financial institutions to assess and manage the risks. Through the analysis of big amounts of unstructured data in real time, AI algorithms are capable of identifying risks. This makes it easy to put preventive measures in place to avert the risks. In addition, big data and AI have come a long way in solving the problem of fraud in banking and finance. This paper showed how big data and AI improve risk management, Cyber threat, and fraud in banking and finance by using data analysis and data pattern identification in real-time. That is why our work emphasizes the importance of implementing secure privacy and explaining the AI algorithm to eliminate ethical and Cyber security issues. Using analytical approaches, AI can identify the transactions with the help of comparison with the previous data and the behavioral characteristics related to the fraud. This approach to fraud prevention has been effective in reducing losses while at the same time improving the customer’s confidence in the company. On the other hand, there are disadvantages of big data and AI such as privacy, security, and ethical issues. Measures that can be used to safeguard customer information have to be employed in order to effectively safeguard the consumer data. Furthermore, transparency and accountability of the AI algorithms are crucial in order to avoid unfair decisions.

Acquisition of Customer Information and Corporate Decision Making

Acquisition of Customer Information and Corporate Decision Making

CRM-СИСТЕМЫ КАК ИНСТРУМЕНТ ПОВЫШЕНИЯ ЭФФЕКТИВНОСТИ ЭЛЕКТРОННОЙ ТОРГОВЛИ

In the modern world, one cannot underestimate the importance of CRM systems, which have a significant impact on business development, cost reduction and other factors. Any organization needs a customer base, its updating and the safety of important information on transactions. Using CRM simplifies life not only for managers of any business, but also for full-time employees of the organization. With the help of customer relationship management systems, companies can automate marketing processes, accounting processes, reduce manual data collection work and organize customer information. This article examines the different types of CRM systems, their impact on e-commerce, as well as the PIM and OMS systems that are an integral part of the toolkit. In addition, a number of advantages that CRM systems have were considered. These include increasing the efficiency of marketing strategies and the work of the sales department, ensuring high-quality communication within the company, increasing the company’s competitiveness and the active use of CRM reporting. All the benefits of implementing CRM systems are revealed only if the product’s capabilities are used correctly. The fact that the system has been implemented does not guarantee a solution to the company’s internal problems associated with the ineffectiveness of the sales department and incomplete filling of the database. All these problems can be solved only if the systems are correctly implemented and used, as well as high-quality employee training. After studying the features of CRM systems and their impact on e-commerce companies, it was concluded that the implementation of CRM systems is necessary for any corporate business whose goal is to increase revenue and build strong relationships with customers.

Development of an Integrated System that Combines Existing Customer Information and Map Information, Including Potential Transaction Possibilities

Development of an Integrated System that Combines Existing Customer Information and Map Information, Including Potential Transaction Possibilities