Search Results for operational-efficiency

Abstract

The aim of the research is to identify the external determinants of the performance of the banking sector, represented by growth rate in gross domestic product (GGDP) and inflation (I), and the internal determinants represented by size (S), operational efficiency (OE) and financial intermediation (FM) for the period (1996 to 2017), in some of the Arabic countries represented by (Egypt, Jordan, Saudi Arabia, the Emirates). To reach this goal, cross-sectional time-series models (Pooled regression model, fixed effects model, random effects model) were used. These models were compared using the restricted F test and Hausman's test, and it was found that the random effects model is the appropriate model to represent the relationship between the research variables.The results of the research revealed that there was no effect of the external determinants (growth in GDP and inflation) and financial intermediation as one of the internal determinants on the performance of banking sectors, expressed in return on capital (ROE), because the relationship between these variables was not significant, in addition to the existence of a negative impact of a significant indication of the volume on the performance of the banking sectors and a positive impact with a significant effect of the operational efficiency on the performance of that sector. The research recommended the necessity for the supervisory and supervisory authorities to pay special attention to size and operational efficiency for their clear impact on the performance of the banking sectors, the research sample.

Abstract

Given the challenges facing Iraqi industry—such as limited government support and the high cost of production compared to imported goods—industrial organizations are in urgent need of adopting effective technologies to improve their operational efficiency. Supply chain analysis stands out as one of the most significant of these technologies, as it helps optimize the flow of materials and information, reduce costs, and enhance the accuracy of accounting data.

This study aims to highlight the impact of supply chain analysis on strengthening the effectiveness of the accounting information system through a field application at the National Company for Chemical and Plastic Industries. The findings indicate that integrating supply chain activities with the accounting system contributes to improving the quality of financial information, controlling costs, and supporting managerial decision-making. The study further recommends the development of digital systems that integrate logistical and accounting functions, along with training personnel on modern technologies. 

Keywords: supply chain, accounting information system, operational efficiency, cost reduction , effective and ineffective activities.

Abstract

This research aims to examine the role of time-directed resource consumption accounting (TD-RCA) techniques in achieving competitive advantage for economic units by improving costing accuracy, rationalizing resource consumption, and enhancing product quality. TD-RCA relies on analyzing resource consumption based on the actual time of activities, which helps determine the fair cost of products, reduce waste, and achieve higher operational efficiency.

Process reengineering also contributes to the redesign of manufacturing activities with the aim of eliminating unnecessary processes and enhancing customer value, leading to reduced costs and improved product quality. The research focuses on the application of these two techniques in a ready-made garment factory in Najaf. The results showed that combining the two techniques helps reduce overall costs, increase the factory's competitiveness, and enhance its responsiveness to market demands.

The research recommends the adoption of these modern techniques in cost management, given their positive impact on achieving a sustainable competitive advantage, achieving optimal resource utilization, and enhancing production efficiency.

Abstract

Given the swift digital changes occurring in the Banking industry, the purpose of this paper is to examine how well artificial intelligence systems can forecast and protect against future disasters.  By utilizing its skills in big data analytics, forecasting financial behavior, and more accurately and effectively managing risks, artificial intelligence (AI) is increasingly regarded as a crucial component in the development of banking systems and improving their operational efficiency.

 By enhancing client satisfaction, tailoring banking services to meet the demands of each individual, and cutting down on operational errors and administrative expenses, banks hope to gain a competitive edge by utilizing these technologies.  AI also helps to speed up credit decisions, make it possible to identify financial crime early, and create clever marketing plans based on forecasts of future market trends.

In order to ensure financial sustainability and achieve integration between digital transformation and the demands of banking innovation, studies show that the future of AI encompasses strategic, cultural, human, technological, and organizational dimensions in addition to technical ones.

 The paper also examined a number of anticipated long-term effects of AI applications, such as increased forecasting precision, lower operating expenses, better customer satisfaction, increased worker productivity, and assistance with investment choices.  The findings show that implementing AI applications in the banking sector is a strategic requirement to guarantee long-term growth and competitiveness in the digital era, not a technical luxury.

In order to enhance lending decisions and lower default risks, the paper also assesses how well a number of categorization algorithms work in assessing loan applicants' creditworthiness.  Using a dataset that represented the traits and financial activities of clients, seven machine learning techniques were used: Gradient Boosting, Random Forest, Extra Trees, Gaussian Naive Bayes, Logistic Regression, SVC-RBF, and KNN.

The paper used a database of 21 variables for loan applicants. Numerical variables included (age, income, credit score, debt-to-income ratio, and loan amount). Descriptive variables included (loan purpose, region, marital status, employer, educational level, and application channel). Binary variables included (whether or not the applicant had a history of default). These variables were used to predict the approval or rejection decision, with the dependent variable being represented by two values: 0 for rejection and 1 for approval.

The models were evaluated using the following six key performance indicators: Accuracy, Precision, Recall, F1 Score, Receiver Operating Characteristic Area Under the Curve (ROC AUC), and Brier Score.   The findings demonstrated that the Gradient Boosting algorithm performed best overall in both probability prediction quality and customer differentiation across different risk levels.  The Random Forest algorithm, which showed stability and balanced metrics, came next.  On the other hand, despite its moderate performance, Logistic Regression provided great interpretability, while the Gaussian Naive Bayes algorithm demonstrated high sensitivity in identifying high-risk customers.  In terms of overall accuracy and probability quality, some models—like SVC-RBF and KNN—performed worse.