Application of Artificial Intelligence Technology Optimized by Deep Learning to Rural Financial Development and Rural Governance

The aim of this article is to promote the development of rural finance and the further informatization of rural banks. Based on DL (deep learning) and artificial intelligence technology, data pre-processing and feature selection are conducted on the customer information of rural banks in a certain region, including the historical deposit and loan, transaction record, and credit information. Besides, four DL models are proposed with a precision of more than 87% by test to improve the simulation effect and explore the application of DL. The BLSTM-CNN (Bi-directional Long Short-Term Memory-Convolutional Neural Network) model with a precision of 95.8%, which integrates RNN (Recurrent Neural Network) and CNN (Convolutional Neural Network) in parallel, solves the shortcomings of RNN and CNN separately. The research result can provide a more reasonable prediction model for rural banks, and ideas for the development of rural informatization and promoting rural governance.

Data-Supported CRM as a Lever for DMO Success: A Social Exchange Relationship Approach

Customer relationship management (CRM) is proving to be one of the most promising business strategies. However, in the field of destination marketing literature, a problem exists as to how data-supported CRM can be established. While customer data management has already been well exploited in other industries, DMOs lack customer proximity and data sovereignty. The aim of this paper is to fill this research gap and show how a data-based CRM can be deployed by DMOs based on the principles of social exchange theory. In 13 expert interviews, these aspects were examined from the DMO’s point of view. The results show that the exchange relationship must be established taking into account the DMO’s extraordinary conditions and critical success factors. In order to stimulate guests’ desire for dialogue or the willingness to disclose personal data, DMOs should offer high-quality customer benefits. A combination of hedonic and utilitarian benefits are found to be the most effective stimuli. In return, only the most necessary customer information should be requested and subsequently built passively. Only if the cost and benefit ratio of the exchange relationship is positive for both parties, a database for the CRM can be built in order to foster long-lasting relationships with potential and returning guests.

Research on Default Prediction for Credit Card Users Based on XGBoost-LSTM Model

The credit card business has become an indispensable financial service for commercial banks. With the development of credit card business, commercial banks have achieved outstanding results in maintaining existing customers, tapping potential customers, and market share. During credit card operations, massive amounts of data in multiple dimensions—including basic customer information; billing, installment, and repayment information; transaction flows; and overdue records—are generated. Compared with preloan and postloan links, user default prediction of the on-loan link has a huge scale of data, which makes it difficult to identify signs of risk. With the recent growing maturity and practicality of technologies such as big data analysis and artificial intelligence, it has become possible to further mine and analyze massive amounts of transaction data. This study mined and analyzed the transaction flow data that best reflected customer behavior. XGBoost, which is widely used in financial classification models, and Long-Short Term Memory (LSTM), which is widely used in time-series information, were selected for comparative research. The accuracy of the XGBoost model depends on the degree of expertise in feature extraction, while the LSTM algorithm can achieve higher accuracy without feature extraction. The resulting XGBoost-LSTM model showed good classification performance in default prediction. The results of this study can provide a reference for the application of deep learning algorithms in the field of finance.

Customer churn prediction for telecommunication industry: A Malaysian Case Study

Background: Customer churn is a term that refers to the rate at which customers leave the business. Churn could be due to various factors, including switching to a competitor, cancelling their subscription because of poor customer service, or discontinuing all contact with a brand due to insufficient touchpoints. Long-term relationships with customers are more effective than trying to attract new customers. A rise of 5% in customer satisfaction is followed by a 95% increase in sales. By analysing past behaviour, companies can anticipate future revenue. This article will look at which variables in the Net Promoter Score (NPS) dataset influence customer churn in Malaysia's telecommunications industry. The aim of This study was to identify the factors behind customer churn and propose a churn prediction framework currently lacking in the telecommunications industry. Methods: This study applied data mining techniques to the NPS dataset from a Malaysian telecommunications company in September 2019 and September 2020, analysing 7776 records with 30 fields to determine which variables were significant for the churn prediction model. We developed a propensity for customer churn using the Logistic Regression, Linear Discriminant Analysis, K-Nearest Neighbours Classifier, Classification and Regression Trees (CART), Gaussian Naïve Bayes, and Support Vector Machine using 33 variables. Results: Customer churn is elevated for customers with a low NPS. However, an immediate helpdesk can act as a neutral party to ensure that the customer needs are met and to determine an employee's ability to obtain customer satisfaction. Conclusions: It can be concluded that CART has the most accurate churn prediction (98%). However, the research is prohibited from accessing personal customer information under Malaysia's data protection policy. Results are expected for other businesses to measure potential customer churn using NPS scores to gather customer feedback.

INNOVATION MARKETING TOOLS IN THE ENTERPRISE MANAGEMENT SYSTEM

The article is devoted to the issue of managing innovative marketing in a modern competitive market environment. As a result of research has been established that innovations play a crucial role in ensuring economic growth. However, there is a problem of inefficient use of innovative marketing among domestic enterprises. The article considers the main trends in innovative marketing and clarifies the importance of managing the marketing activities of the enterprise. Among the main marketing trends were considered the following: digital, conversational and omnichannel marketing. New marketing technologies with elements of artificial intelligence and machine learning are considered, which include: predictive analytics, Programmatic platform, content management systems, SEO / SEM optimization tools, Big Data, ecommerce platforms, data collection and management tools, tag management, tracking unfinished sales. The most popular digital marketing skills in 2020 are identified: coding, development, video editing, copywriting, graphic design, software expertise, SEO / SEM experience, data analysis, marketing automation, software and project management. Due to the reorientation of the business towards customer-centrism, a special place in the publication is given to the indicators of customer uniqueness and the definition of the customer life cycle. Knowledge of the calculation of the customer's vital value is crucial for the marketing success of the business, allows to increase the consumer value of the company's services to consumers through a timely response to the interests and requirements of customers. The confidentiality of customer information is an important issue in the era of digital marketing, so the paper studies some technology and basic ways to identify and protect users' personal data.

What to do after a data breach? Examining apology and compensation as response strategies for health service providers

Innovative IT-enabled health services promise tremendous benefits for customers and service providers alike. Simultaneously, health services by nature process sensitive customer information, and data breaches have become an everyday phenomenon. The challenge that health service providers face is to find effective recovery strategies after data breaches to retain customer trust and loyalty. We theorize and investigate how two widely applied recovery actions (namely apology and compensation) affect customer reactions after a data breach in the specific context of fitness trackers. Drawing on expectation confirmation theory, we argue that the recovery actions derived from practice, apology, and compensation address the assimilation-contrast model’s tolerance range and, thus, always lead to satisfaction with the recovery strategy, which positively influences customers’ behavior. We employ an experimental investigation and collect data from fitness tracker users during a running event. In the end, we found substantial support for our research model. Health service providers should determine specific customer expectations and align their data breach recovery strategies accordingly.

Pengungkapan Kuantitatif dan Kualitatif Segmen Operasi Berdasarkan PSAK 5 Revisi 2009 (Penyesuaian 2015) di Indonesia

Reporting of operating segments has become an important concern, therefore there is a PSAK 5 regulation which is continuously updated based on IFRS 8 to improve operating segment reporting. This study aims to examine the application of operating segment disclosure and its determinants in public companies in Indonesia.This research method is quantitative method. The operating segment in the financial statements of the LQ 45 Index’s Company in 2016 is analyzed by scoring to the items required by PSAK 5 Revised 2009 (Amandement 2015). The results showed that the majority cozmpany's compliance level of quantitative information is medium level of compliance,. Quantitative disclosure shows the most reported items are profit loss and total assets, meanwhile the least reported item is other non current assets and main customer information . Meanwhile, the most reported item of qualitative disclosure is the main products and services which generate revenues for the operating segments. This study shows that companies disclose more quantitative information than qualitative. In addition, the good corporate governance mechanism that determines the extent of disclosure of operating segments is institutional ownership and the board of directors. This research has implications for operating segment regulators to do better and contribute to the agency theory and signaling theory.

Free Lunch? Vendor Offers during COVID-19

Academic libraries received numerous free offers during the COVID-19 pandemic. Existing business literature suggests that there are benefits and costs associated with free offers for both the businesses that provide them and their customers. This study analyzes the free offers received during a three-month period at the beginning of the pandemic in 2020. The author monitored direct offers from vendors, [email protected], information obtained from peers, and publicly available data from the International Coalition of Library Consortia (ICOLC). The offers that would normally require paid institutional subscriptions were included in the study. Databases were the largest offer category (41%), followed-by e-books (20%). Most (76%) required registration by library representatives, allowing vendors to track usage data. Only a small portion (8%) of these free offers was already held at the study site, Penn State University Libraries (PSUL). The implication might be that most of the offers were either new, not high-priority or not affordable for PSUL. The findings of this study suggest free offers provide intangible value for both libraries and vendors that cannot be measured through cost-per-use data analysis. For example, libraries gained opportunities to trial new products without any risk, temporarily expand their collections, and help users during the crisis when access to the library buildings was disrupted. Vendors increased product visibility, gained customer information and usage data, identified potential customers, and created goodwill with the library community. This study is relevant to business librarianship not only because these free offers included business and related disciplines but also because some business librarians engage with vendor relations and need to understand different business models including free offers.

Marketing Information, Analytics, and Intelligence

Companies collect customer information, store the information in databases, and analyze it to generate customer insights. The study focuses on the roles of customer relationship management (CRM) in making proper usage of the information, marketing analytics, and the marketing intelligence generated to develop fruitful customer relationships. Companies employ advanced marketing analytics and big data to understand customers and implement CRM effectively. The customer insights generated should be distributed and used properly. Although companies benefit from implementation of CRM, the implementation has its own drawbacks. Implementation of CRM will not solve all issues related to customers. It has its own drawbacks. However, proper implementation and effective utilization of CRM will help companies in developing customer relationships, in growing their businesses, and in achieving business excellence in the long run.