American Journal of Business and Operations Research

Direct marketing strategies in the banking sector have undergone evolution with the integration of predictive analytics and machine learning techniques. The focus of this study is on the utilization of these technologies to foresee bank term deposit subscriptions. The methodology encompasses data exploration, visualization, and the implementation of machine learning models. Datasets from Kaggle are employed, relationships within the data are explored through crosstabulations and heat maps, and feature engineering and preprocessing techniques are applied. The study individually implements models such as SGD Classifier, k-nearest neighbor Classifier, and Random Forest Classifier. The results indicate that the best performance among the evaluated models was exhibited by the Random Forest Classifier, achieving an accuracy of 87.5%, a negative predictive value (NPV) of 92.9972%, and a positive predictive value (PPV) of 87.8307%. These findings provide valuable insights for banks seeking to optimize their marketing strategies within the dynamic landscape of the financial industry.

The accessibility of data is altering how businesses make decisions at different levels. Scholars and professionals are investigating the ways in which Business Process suppliers can profit from the availability and application of data, particularly in relation to decision-making concerning service provision. Business Process Improvement is one of the applications that is anticipated to gain the most from the accessibility of information. Suppliers of services can avoid failures by making prompt and well-informed decisions based on the evaluation of the resource's health state. Despite this, providing data-driven BPI services is not simple, and providers must set up their systems to correctly gather, process, and utilize past and current data. This study introduces a data-driven business intelligence framework to provide use full insights for improving business process activities. This framework offers a set of visualization tools that help interpret the relation between different factors that can improve the management of different business processes. Moreover, our framework provides successful integration of random forests to allow predictive modeling of sales, profits, and discounts across different regions.

In the tough cell phone business, guessing phone­ prices right is a key but hard job for new companies. Joining different types of info to look at stock prices may help, but we need strong ways to see how phone things and their costs tie together. This study wants to make stock price checking better in the cell phone busine­ss by using ways to join info. The work looks for strong ties between many phone things like memory, camera details, and screen size and how they affect the price. To fix this, very careful work was done to clean and fix the info. The Quadratic Discriminant Analysis rule­ was then used, along with top classifiers, for saying what will happen. Our findings demonstrate the QDA model's ability to detect subtle patterns and nonlinear correlations in the mobile phone data set. The model's resilience and predictive ability are demonstrated through visualizations such as ROC AUC and Precision-Recall curves. Comparative analyses with current approaches highlight the higher performance of the suggested data fusion approach. The use of QDA in data fusion models demonstrates its versatility in capturing complicated interactions, resulting in nuanced insights into mobile phone price factors. This study adds an improved prediction framework for mobile phone price analysis, which is critical for new enterprises looking to gain a competitive advantage in the volatile mobile industry.

Based on the business context, resilience and sustainability seem to have multiple dimensions and connections. Administrative sustainability strategies can help a company develop and become more resilient. With the use of a sustainability maturation index (SMI), this study attempts to analyze how the financial success of a business is affected by its approach to sustainable development. As resilience abilities are closely linked to the SMI, this study proposes to explore the initial integration of both sustainable development and resilience criteria into a single framework. To determine whether there could be an interaction between the SMI and economic performance indices, planned conversations were used to gather data from 35 different firms. The investigation disproves widely circulated claims, demonstrating that there is no meaningful correlation between profitability and sustained business operations. It's noteworthy to point out that market emphasis, organizational size, and firm place of origin do not significantly correlate with SMI. One could argue that to evaluate the effects of environmentally friendly procedures, a company's multi-dimensional performance, which includes both financial and non-financial measurements, should be considered. In addition, more research is required to identify the nonfinancial metrics of success that businesses use to measure resilience and sustainable development to create a cohesive framework that facilitates trade-off evaluation.

effective risk management is an indispensable requirement for improving the flow of transactions in dynamic financial markets. To this end, this study presents an applied predictive analytics methodology, that integrate gradient boosting algorithm to model the risk behavior in dynamic markets. This study, based on predictive analytics in monetary and financial systems, faces an urgent need for robust models that can overcome the uncertainties inherent in dynamic markets. Holistic experimentations on public case study of U.S retail data demonstrate the predictive power of the proposed approach of the state-of-the-art techniques across different performance metrics. This in turn highlights the nuanced interaction between variables and delivering intuitions into crucial risk determining factor.

The study aims to explore the moderating effect of audit quality on the association between voluntary disclosure and profitability. The study sample consists of (34) industrial companies listed on the "Amman Stock Exchange (ASE)" during the period (2017-2021). To achieve the objectives of the study, the content analysis method has been used to measure the impact of voluntary disclosure by reviewing 32 items of voluntary disclosure. Several statistical methods that fit the objectives of the study are used as well. The results of the study show that there is a positive association between the voluntary disclosure level and the profitability of the company, and that the moderating variable of audit quality negatively affects the relationship between voluntary disclosure and the profitability of the company. Considering the aforesaid results, the study recommends making voluntary disclosure by the industrial companies listed on the "ASE" due to its role in enhancing the profitability of companies.

This study investigates the intricate dynamics of fostering economic growth and global trade through digitalized international transport corridors (ITCs), focusing on the proposed China-Kyrgyzstan-Uzbekistan (CKU) railway project within the framework of the eTIR Convention. Employing a comprehensive mixed-methods approach, integrating quantitative data from official sources, the research delves into the transformative potential of the CKU railway in linking Central Asia to China and Europe, thereby amplifying Uzbekistan's economic growth and global trade. Despite the project's potential to bolster trade competitiveness, stimulate regional integration, and foster economic diversification, the analysis uncovers challenges such as geopolitical tensions, environmental impacts, and financial sustainability, which could impede seamless implementation and operation. Leveraging the ARDL (2,1) model, the study reveals a direct dependency between the GDP growth rate and income from China to Uzbekistan, suggesting a substantial linear relationship. However, caution is advised in drawing definitive policy or economic implications, as further nuanced analysis and contextual understanding are imperative. The paper concludes by offering nuanced policy recommendations to enhance the governance and management of ITCs in Uzbekistan, emphasizing the necessity for a holistic and adaptive approach to navigate the complexities of such transformative projects, especially within the digitalized landscape and the context of the eTIR Convention.

The article considers information about the current state of small business and private business entities, their growth trends, and the article also shows the role of small business and private business entities in the current economy and their share in some sectors. The article describes the external and internal problems that are currently the most difficult for business entities and how to solve them. Additionally, the results of the survey conducted among entrepreneurs are also included in the analytical center of the article. Statistical indicators and results of econometric analysis are expressed in a convenient visual graphic form.

This article presents our research effort to explore the convergence of sustainable banking practices and predictive modeling for bank loan defaults, with a primary emphasis on addressing the pressing need for resilient financial systems. To this end, an applied methodology is presented in this study to model bank loan defaults, emphasizing the incorporation of sustainability criteria into predictive analytics. Given the temporal nature of load data, our approach leverages Long Short-Term Memory (LSTM) networks as its backbone process for predictive modeling.  The empirical results of the public case study underscored the enhanced predictive accuracy completed through this approach, emphasizing the pivotal function of integrating sustainability metrics in predicting mortgage defaults inside the banking area.

Medium-sized enterprises face the dual challenge of optimizing inventory practices while aligning with sustainable objectives.  Effective inventory management in medium-sized enterprises remains a critical factor in balancing operational efficiency and sustainability. This study addresses the challenge by investigating strategic optimization approaches for inventory control. The background explores the complexities of inventory management, emphasizing the need for enhanced techniques in medium-sized enterprises. The problem statement underscores the necessity of innovative methodologies to navigate the intricate landscape of inventory control and sustainability. To address these challenges, advanced methodology based on LightGBM is applied to forecast demand, assess vendor purchase costs, optimize stock levels, and evaluate predictive performance. Empirical findings, showcased through visualizations, revealed insights into the distribution of purchase costs among vendors, daily sales velocity by product, and comparisons between current and recommended stock levels for top products. Comparative analyses demonstrated LightGBM's superior predictive performance over baseline models, highlighting its potential as a valuable tool in decision-making for inventory management.