Issue 2 (December)

P2P (Peer to Peer) online lending is an emerging Internet finance mode that gathers small-amount fund lending to fund demander. This paper draws on the existing credit risk assessment research, combines rationality, science and other principles, according to the characteristics of the famous online loan platform, collects borrower information and combines computer technology to design a borrower credit risk assessment system. In this paper, we make improvements based on the famous LightGBM algorithm (Light Gradient Boosting Machine). Firstly, In the process of data input, the improved Convolutional Neural Network CNN model is adopted to extract features from the data. Specifically, the Global Average Pooling(GAP) layer is adopted to replace the full connection layer to improve the Convolutional Neural Network. This paper first proposes a P2P online loan default prediction model based on GCNN-LightgBM. The model integrates the advantages of the improved Convolutional Neural Network and LightGBM model, and realizes the efficient prediction of network loan default. Then, in order to improve the accuracy of P2P online loan borrower default prediction, this paper proposes a new model based on LightGBM and Bagging (LGB-BAG). LGB-BAG uses LightGBM as the base learner. With the help of LightGBM, which can effectively reduce the deviation of the model, and Bagging, which can reduce the variance of the model, the volatility of the prediction is further reduced (F1 variance), so that the LGB-BAG model has smaller deviation and variance, and the prediction effect is further improved. In our ablation experiment, the proposed model (GCNN-LGB-BAG) obtained an AUC of 0.86 and an accuracy of 0.97, both of which outperformed all benchmark models. This paper uses actual data to identify the loan risks of P2P online lending platforms, aiming to provide investment reference for investors and methodological support for relevant online lending regulators.

Research Objectives: Build a dynamic imbalanced financial distress prediction model to solve concept drift I and imbalanced data distribution simultaneously. Research Methods: Based on the improved AdaBoost algorithm, ADA-CSSVM-TW model is built with cost sensitive support vector machine as the base classifier, and the data of Chinese manufacturing companies from 2010 to 2020 are used for the empirical analysis. Research Findings: The ADACSSVM-TW model I can significantly improve the prediction accuracy, with excellent performance and reliable robustness. Research Innovations: The cost sensitive support vector machine is used as the base classifier of the improved AdaBoost algorithm, and the dynamic imbalanced financial distress prediction model is built. Research Value: The research of this paper has important theoretical value and practical significance for Chinese listed companies to effectively prevent the occurrence of financial distress in practice.

Credit default identification technology usually requires a higher classification accuracy, and low rate of false positives, in view of the traditional credit default identification model based on machine learning, in dealing with high dimensional imbalance of financial transaction data on the problem of lower overall classification accuracy, along with the development of the large data, the credit transaction data of large-scale and high dimension feature, It is necessary to ensure the efficient and timely construction of the credit default recognition model. Semi-supervised learning can reduce the cost of manual labeling, and train the model in time by making full use of a large amount of unlabeled data and a small amount of labeled data, which has important research significance in the field of credit default recognition. At the same time, the traditional supervised learning model can only recognize the default behaviors that have happened before, but the default behaviors are variable, and the semi-supervised learning algorithm can recognize the unknown default behaviors. From the perspective of semi-supervised learning, DBN, as a semi-supervised deep learning framework, utilizes its feature learning ability and iForest unsupervised learning algorithm's ability to identify abnormal behaviors to propose a default identification method by use of the DBN-Iforest. This paper first introduces the experimental data, including the data source and the pretreatment process of the original data. Then the performance evaluation criteria applied to all experimental models in this paper are introduced in detail. Then the steps of DBN-iForest algorithm are described in detail. Next, algorithms for iForest optimization are introduced, including particle swarm optimization algorithm and simulated annealing optimization algorithm. Finally, through comparative analysis of experimental results at different levels, the advantages of the DBN-Iforest model are proved, the classification performance is improved, and the difficulty of data resource shortage in credit default recognition is overcome. The semi-supervised default identification method by use of the DBN-IForest fully improves the classification performance of DBN and iForest by using less label data.

With the development of Internet and E-commerce, picture advertising, as an important form of display advertising, has the characteristics of high visibility, strong readability and easy to obtain user recognition. An increasing number of Internet companies are paying attention to what kind of advertising pictures can attract more clicks. Recently, Deep Learning has been employed in automatic feature extraction, and has made remarkable achievements in the fields of computer vision, speech recognition, natural language processing and artificial intelligence. Compared with traditional shallow model, deep learning can automatically extract more complex features from simple features, which reduces the intervention of artificial feature engineering to a certain extent. Based on deep learning technology, this paper studies the prediction model of click through rate (CTR) for advertising, and proposes an end-to-end CTR prediction depth model for display advertising, which integrates the feature extraction of display advertising and CTR prediction to directly predict the probability of an advertisement image being clicked by users. This paper studies the deep-seated nonlinear characteristics through the multi-layer network structure of deep network, and carries out several groups of experiments on the private display advertising data set of a commercial advertising platform. The results show that the model proposed in this paper can effectively improve the prediction accuracy of CTR compared with other benchmark models.

This paper investigates the multifaceted economic impacts of artificial intelligence (AI) with a primary focus on productivity, labor market dynamics, and policy implications. The proliferation of AI technologies has ushered in a new era of automation and intelligence augmentation, reshaping various aspects of the global economy.

The research begins by elucidating the significance of AI as a transformative technology. It underscores how AI systems, including machine learning, natural language processing, and robotics, have the potential to revolutionize industries, enhance productivity, and create new economic opportunities.

One central theme of the study is the examination of AI's influence on productivity. It explores how AI-powered automation and decision-support systems can streamline operations, reduce inefficiencies, and boost overall productivity in diverse sectors, ranging from manufacturing to healthcare.

Furthermore, the study delves into the labor market dynamics in the AI era. It analyzes the impact of automation on employment, job displacement, and the need for reskilling and upskilling of the workforce. It also explores the emergence of new AI-related job roles and the potential for AI to augment human capabilities.

A significant aspect of the research is the discussion of policy implications. It addresses the role of governments and institutions in shaping AI adoption, regulation, and ethical use. It explores policy considerations related to data privacy, AI ethics, and the promotion of AI research and development.

Through an analysis of case studies and industry trends, the paper identifies successful models of AI implementation, highlighting best practices and lessons learned. It also emphasizes the importance of ethical AI deployment to mitigate societal challenges.

In conclusion, this paper contends that AI's economic impacts are far-reaching, with the potential to enhance productivity and reshape the labor market. It underscores the need for proactive policy frameworks that balance AI innovation with ethical considerations and labor market stability.

This paper explores the strategic dimensions of marketing for social and environmental causes, emphasizing the delicate balance between profit and purpose in contemporary business practices. In an era marked by growing societal and environmental concerns, organizations are increasingly recognizing the need to integrate social and environmental responsibility into their marketing strategies.

The research begins by highlighting the evolving landscape of corporate social responsibility (CSR) and sustainability in the context of marketing. It underscores how consumers, stakeholders, and regulatory bodies are demanding more ethical and sustainable business practices.

One central theme of the study is the examination of strategic marketing approaches that align profit-making with societal and environmental impact. It explores how businesses can harness the power of purpose-driven marketing to create meaningful connections with consumers while advancing social and environmental causes.

Furthermore, the study investigates the role of storytelling and brand narratives in conveying a company's commitment to social and environmental responsibility. It analyzes successful campaigns and strategies that effectively communicate a brand's values and purpose.

A significant aspect of the research is the discussion of ethical marketing practices. It addresses issues related to greenwashing, authenticity, and the responsibility of organizations to uphold the integrity of their social and environmental claims.

Through an analysis of case studies and industry trends, the paper identifies successful models of strategic marketing for social and environmental causes, highlighting best practices and lessons learned.

Additionally, the study emphasizes the importance of measuring the impact of purpose-driven marketing initiatives. It discusses key performance indicators (KPIs) and metrics to assess the effectiveness of campaigns in driving both profit and positive societal outcomes.

In conclusion, this paper contends that strategic marketing for social and environmental causes offers a unique opportunity for organizations to align their profit motives with a higher purpose. It underscores the transformative potential of purpose-driven marketing in fostering a more sustainable and responsible business landscape.

This paper explores innovations in human capital management (HCM) with a primary focus on strategies for attracting and retaining top talent in the competitive business landscape. Human capital, characterized by the skills, knowledge, and abilities of employees, is a critical asset for organizations seeking to thrive and succeed.

The research begins by highlighting the evolving dynamics of the modern workforce. It underscores the challenges organizations face in recruiting and retaining highly skilled professionals in a globalized and competitive environment.

One central theme of the study is the examination of innovative HCM strategies. It explores how organizations can leverage technology, data analytics, and talent management practices to identify, attract, and retain top talent. It delves into the role of predictive analytics in workforce planning and the use of artificial intelligence (AI) in talent acquisition.

Furthermore, the study investigates the importance of employee engagement and workplace culture in talent retention. It analyzes successful models of employee development, mentorship programs, and career progression that contribute to a positive work environment and long-term employee commitment.

A significant aspect of the research is the discussion of diversity and inclusion in HCM. It addresses the importance of fostering diverse and inclusive workplaces as a means to attract and retain top talent from all backgrounds.

Through an analysis of case studies and industry trends, the paper identifies successful models of HCM innovation, highlighting best practices and lessons learned.

Additionally, the study emphasizes the importance of measuring the effectiveness of HCM strategies. It discusses key performance indicators (KPIs) and metrics to assess talent acquisition, engagement, and retention efforts.

In conclusion, this paper contends that innovations in human capital management are essential for organizations seeking to remain competitive and sustainable. It underscores the transformative potential of strategic HCM in attracting, developing, and retaining top talent.

This paper explores the revolutionary impact of blockchain technology on various industries, with a primary focus on finance, supply chain management, and its disruptive potential in other sectors. Blockchain, originally designed as the underlying technology for cryptocurrencies like Bitcoin, has evolved into a transformative force reshaping traditional business processes.

The research begins by providing a comprehensive overview of blockchain technology. It explains the fundamental principles of distributed ledgers, decentralized consensus mechanisms, and smart contracts that underpin blockchain systems.

One central theme of the study is the examination of blockchain's applications in finance. It delves into the use of blockchain for secure and transparent transactions, digital identity verification, and the tokenization of assets. It explores the potential for blockchain to revolutionize payment systems, cross-border transactions, and financial inclusion.

Furthermore, the study investigates blockchain's impact on supply chain management. It analyzes how blockchain can enhance transparency, traceability, and trust in supply chains by recording every transaction and movement of goods. It discusses successful use cases in areas such as food safety and provenance tracking.

A significant aspect of the research is the discussion of blockchain's disruptive potential beyond finance and supply chain. It explores emerging applications in healthcare, voting systems, intellectual property, and more. It also addresses the challenges and regulatory considerations associated with blockchain adoption.

Through an analysis of case studies and industry trends, the paper identifies successful models of blockchain implementation, highlighting best practices and lessons learned.

Additionally, the study emphasizes the importance of blockchain interoperability, scalability, and security in realizing its full potential.

In conclusion, this paper contends that blockchain technology has the power to disrupt traditional business models and reshape industries. It underscores the transformative potential of blockchain in finance, supply chain, and numerous other sectors, ushering in a new era of transparency, security, and efficiency.

This paper delves into the critical theme of economic resilience and recovery in the aftermath of the global COVID-19 pandemic. It highlights the strategies required for rebuilding economies, ensuring growth, and fortifying against future crises.

The research begins by providing an overview of the unprecedented challenges posed by the pandemic and its profound impact on global economies. It underscores the need for a multifaceted approach to address the economic fallout.

One central theme of the study is the examination of strategies for economic recovery. It explores fiscal and monetary policy measures, government interventions, and international cooperation in stabilizing economies and restoring growth. It also assesses the role of innovation and digital transformation in adapting to the new normal.

Furthermore, the study investigates the importance of resilient supply chains and business continuity planning. It analyzes how organizations can fortify their operations against disruptions and adapt to changing market dynamics.

A significant aspect of the research is the discussion of social and labor market policies. It addresses the need for inclusive policies that support vulnerable populations, ensure employment opportunities, and bridge socio-economic disparities.

Through an analysis of case studies and industry trends, the paper identifies successful models of economic resilience and recovery. It highlights best practices and lessons learned from regions and sectors that have demonstrated resilience in the face of adversity.

Additionally, the study emphasizes the importance of data-driven decision-making and scenario planning in preparing for future shocks. It discusses the role of technology in facilitating remote work, digital education, and healthcare solutions.

In conclusion, this paper contends that economic resilience and recovery post-pandemic require a holistic approach that combines policy measures, innovation, and social inclusion. It underscores the importance of adaptability, preparedness, and collaboration in building economies that can withstand and thrive in the face of uncertainty.

This paper explores the future of sustainable transportation in the context of evolving technologies, environmental concerns, and urban planning. As the world grapples with the challenges of climate change and urbanization, the need for sustainable and efficient transportation systems has never been more critical.

The research begins by highlighting the environmental and societal impacts of conventional transportation, including pollution, congestion, and energy consumption. It underscores the imperative to transition to sustainable alternatives.

One central theme of the study is the examination of electric vehicles (EVs) and their role in sustainable transportation. It explores the advancements in EV technology, charging infrastructure, and the potential for widespread adoption. It also assesses the environmental benefits of EVs and their contribution to reducing greenhouse gas emissions.

Furthermore, the study investigates the concept of smart mobility and its integration into urban planning. It analyzes how data-driven solutions, ride-sharing platforms, and autonomous vehicles are shaping the future of transportation. It discusses the role of urban design in promoting walkability, cycling, and public transit.

A significant aspect of the research is the discussion of policy frameworks and incentives for sustainable transportation. It addresses government initiatives, regulations, and financial incentives that promote the adoption of EVs and the development of eco-friendly transportation systems.

Through an analysis of case studies and industry trends, the paper identifies successful models of sustainable transportation initiatives. It highlights best practices and lessons learned from cities and regions that have embraced sustainable mobility solutions.

Additionally, the study emphasizes the importance of public awareness and education in fostering a culture of sustainable transportation. It discusses the role of public-private partnerships in advancing sustainable mobility initiatives.

In conclusion, this paper contends that the future of sustainable transportation lies in a combination of electric vehicles, smart mobility solutions, and urban planning that prioritizes environmental stewardship and the well-being of communities.