The study proposes a machine learning framework that optimizes the approval processes of Neo Banks loan applications and overcomes the limitations of traditional credit scoring models. Using feature engineering, ensemble methods, and hyperparameter optimization on customer demographics, financials, and transaction details to determine loan acceptance. The results show that the improved models have the accuracy of 97.87% and 88.59% recall and are far better compared to traditional methods (logistic regression: 63.76% recall). Significant predictors are education level (importance: 0.35), income (0.30), and family size, with the probability of approval among high-income, high-education customers being 45% higher. The framework reduces the false negative rate, allowing the Neo Banks to focus on the cream of the crop applicants but avoiding risks. Examples of practical strategies are customized marketing and dynamic pricing. This work talks about limitations such as data imbalance, and future research suggests integrating real-time behavioral data and fairness-aware modeling. It fills a gap between tech innovation and operational requirements, providing a scalable method for updating credit risk evaluation in digital banking.

This paper aims to discuss the dynamics of gold pricing through two key approaches: the Error Correction Model (ECM) and the Gold Supply and Demand Balance Valuation Model. These models provide insights into how short-term fluctuations and long-term trends in gold prices are shaped. The ECM is particularly helpful for analyzing non- stationary data like gold prices, while the valuation model focuses on the interaction between supply and demand across different economic conditions. By combining these methods, we can better understand the key drivers of gold prices and offer practical tools for investors, policymakers, and businesses. This study aims to bridge the gap between theoretical models and real-world market behavior, providing a comprehensive framework for understanding and forecasting gold prices in both stable and volatile markets.

Under the backdrop of rapid development of financial technology and fierce market competition, this paper discusses how commercial banks transform deposit customers into loan customers, so as to improve the profitability of commercial banks and enhance their competitiveness in the market. As the article notes, although deposit business is still an important funding source for banks, the classic profit model of deposit business has come under duress due to declining yields and increased competition. In comparison, the loan business has higher profit margins, but the risk characteristics of the loan require banks to meet risk control so as to expand their credit scale. In order to achieve this trade-off, banks should take multi-dimensional strategies, including granular customer segmentation, differentiated credit strategy, leveraging financial technology (big data analysis, artificial intelligence, etc.), and optimizing loan product design and risk management mechanism. The paper also incorporates theories on customer lifecycle management, credit scoring, behavioral economics, and risk management from different streams of literature to conceptualise a theoretical framework that relates to the multi-dimensional facets of deposit-to-loan conversion. It conducts a case study of practical cases of domestic and foreign banks and summarizes their successful experience. Lastly, the potential future trends for banks in optimizing their credit businesses are elaborated upon, accentuating the need for innovative business models, improved customer service systems, and technological tools. If you need to write articles related to Deposit customers, loan customers, precision marketing, customer lifecycle, financial technology Keywords, you would be happy to include those.

This study explores how globalization affects economic inequality across social classes through transnational economic interactions by a quantitative analysis and case studies. A multivariate linear regression model is constructed based on World Bank data after the 2008 financial crisis. The results show that export price levels(log(pl_x)) and capital prices(log(pl_k)) significantly increase income inequality, while consumer prices(log(pl_c)) and share of labor income(labsh) have a suppressive effect. The study found that domestic policies(such as the high welfare system in Northern Europe) can partially offset the negative impact of globalization, but the model has limited explanatory power for Latin American and African countries. The study advocates addressing the challenge of inequality under globalization through tax reform, strengthening social security and industrial diversification policies.

As COVID sweeps through the global economy, it has a negative impact on almost any country in the entire world. Although the direction of public opinion is that the impact is bad, the research intends to prove its truth through statistical methods. Is the gap between rich and poor in China really getting bigger because of COVID? Will people's income become more unequal as a result? By calculating the Gini coefficients and counting their trends, the research found that China's income equality has actually improved in the short term. This conclusion is based on a detailed analysis of income data from 31 provincial-level administrative regions across several years, allowing for a more comprehensive understanding of regional income patterns. The research not only evaluates numerical trends but also considers relevant social and economic factors that might have contributed to these changes. While most assume that crises like COVID-19 intensify inequality, this study finds that, under certain conditions, temporary improvements in income equality can emerge, possibly due to emergency policies or economic adjustments.

In the highly competitive banking environment, the issue of bank credit card customer churn is of great concern. The purpose of this study is to develop machine learning models for predicting whether a credit card customer is churned or not and determining the core factors of customer churn. By comprehensively collecting multidimensional data from Thera Bank customers and constructing three models, Logistic Regression, Neural Network, and XG boost, to analyze the data. After the study, it was found that factors such as the frequency of customers' recent contact with the bank and the number of product holdings may have a significant impact on customer churn or not. We accurately assess the characteristics of customers who tend to churn after analyzing these factors based on machine learning models, for example, customers with a lower total number of credit card transactions and a higher total transaction amount are more likely to churn. The results of this research help banks gain a deeper understanding of customer behavior and provide a data-driven basis for formulating targeted customer retention strategies, according to which banks can optimize their products and services and improve their customer management processes to effectively reduce customer churn, thereby enhancing their competitiveness in the banking industry and achieving sustainable development.

The period of globalization is marked by the contributions of American startups, in boosting economic progress and promoting innovation and social transformation. The study intends to examine startups in the food service sector in China and the U.S. comparing their approaches and how they blend societal and business objectives with regard to various factors, like governmental policies, social demand and cultural traditions that influence each country’s setting. This study delves into the methods of adjusting the tactics used in Shenzhen and Silicon Valley. Renowned hubs, for entrepreneurship activities. It also examines the increasing significance of startups that are dedicated to pursuing objectives with as much vigor as they pursue business triumphs. By studying the experiences of Hinichijou in China and Everytable in the U.S. it uncovers solutions to challenges such, as attracting clients securing backing for a business venture and recruiting individuals. The study findings help us understand how vital it is for every business model to be localized and provide advice, for startups looking to integrate responsibility into their strategies while aiming for success at the highest levels of achievement. This research significantly contributes to both local knowledge of connections and provides a clear path, for promoting sustainable social entrepreneurship.

Globalization, as a significant feature of the current world economic development, has exerted extensive and complex influences on the issue of inequality on a global scale. This article aims to delve into the intrinsic connection between globalization and inequality, and to sort out the differentiated impacts that the process of globalization has brought about on income distribution, development opportunities, and educational skills among different countries and groups through mechanisms such as trade, capital flows, and technology transfer. Research findings reveal that globalization has dual impacts: on the one hand, through trade exchanges among countries and sharing of technological achievements, it can promote rapid economic growth; on the other hand, it also exacerbates inequality within and among some countries. Trade liberalization has led to employment shocks for low-skilled workers and widened the gap between the rich and the poor. Technology transfer, while enhancing production efficiency, also leads to the income disparity between high-skilled and low-skilled workers.

Liquidity risk poses a significant threat to the stability of the banking sector, as highlighted by the recent bank run happened with Silicon Valley Bank (SVB). This paper examines the causes of liquidity risk through a case study and literature review of Silicon Valley Bank, focusing on its concentrated business model, reliance on uninsured deposits, asset-liability management practices, and internal risk governance. The analysis reveals that SVB’s liquidity crisis was driven by its business focus and high dependence on uninsured deposits, compounded by inadequate management of interest rate risk and weaknesses in its internal risk controls. Although external supervision by the Federal Reserve was in place, its conservative approach failed to prevent the crisis. This study contributes to the literature by providing a comprehensive analysis of liquidity risk and internal governance in the context of a high-profile banking failure. It recommends that banks diversify their business models and improve the risk management for the financial investment strategies. Additionally, regulators should enhance oversight of regional and large foreign banking organizations to ensure financial stability.

Predictions about M&A success always suffer from the challenge of unbalanced data, which can easily lead to biased predictions. In addition, previous empirical studies have certain limitations when facing high-dimensional relationships, making it difficult to provide a more global perspective. This study constructs and compares several machine learning models to propose an optimal model. This optimal model is lightGBM, which is constructed from the data after SMOTE oversampling. The results of LightGBM come from the CSMAR database of 3672 M&A transactions, revealing the relative importance and directions of 50 predictors on M&A outcomes, which may have contradictory results or do not appear in previous literature. The findings of this study provide new insights into predicting the success of M&A deals of Chinese listed companies and suggest new directions for future research.