Search Results for banking-industry

Abstract

The research aims towards measuring the impact of the development of the banking industry on sustainable development, especially after the development of the banking industry in recent years and the transition to digital banking services that have      achieved great profits for the banking industry     by applying it to the Iraqi banking industry during the period                                                                              

 (2004-2020), using the ARDL model, where the research deals with measuring the impact of the development of the banking industry represented by the independent variables (credit provided to the private sector to GDP,                                                                                                                                  

Total deposits to GDP, and money supply to GDP) on sustainable development, which is achieved through key indicators, which are economic indicators, social indicators, environmental indicators, and institutional indicators, where these approved variables were expressed through indicators (per capita GDP, population ratio in regions urbanization, change in forest area, spending on research and development), and the research concluded the great developmental role of the banking industry to achieve sustainable development as well as the need to develop and provide credit for modern projects that support clean energy and green environment                                                                                             

Abstract

It is not superfluous to claim that Iraqi banks are not technologically advanced. The aim of this research is to identify the factors that affect the adoption of electronic banking services in the Iraqi banking industry. The researchers focused on discussing and examining three main factors: the technological factor, the systems factor (legal and economic), and the environment factor (internal and external). The researchers used the descriptive approach in presenting and discussing the theoretical framework of the research and previous studies. Then they used the analytical statistical approach in examining the influencing factors. The questionnaire method was used to collect primary data from the research sample, which represented 16 Iraqi banks. 101 questionnaires suitable for analysis were obtained. Compiling the answers and analysing them statistically. The results of the study indicated that the main obstacles facing the Iraqi banking industry in adopting electronic banking services are: security risks, lack of trust, lack of a legal and regulatory framework, lack of information and communications technology infrastructure, and lack of competition between local and foreign banks. The researchers proposed a series of measures that the banking industry and government could take to address the various challenges identified. These measures include: creating a clear set of legal frameworks on the use of technology in the banking industry, supporting the banking industry by investing in ICT infrastructure, and banks should focus on competing in technological innovation rather than the traditional rules of retail banking competition.

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.

Abstract

The study objective to determine the extent to which the principles of continuous improvement are applied in the field of innovation in banking services and industry, by relying on technological development in the banking industry and administrative systems in order to achieve customer satisfaction and increase confidence in banking services, by providing banking services that keep pace with the automation of banking operations and in line with the trends The Central Bank is in the transition to electronic money, so the research stems from an important intellectual provocation, namely, do Iraqi banks use continuous improvement techniques and what are the criteria for evaluating their services? The research also aims to track the development of the characteristics of services according to the development of the financial needs of customers and to know the nature of the relationship between the principles of CI and the characteristics of BS. , standard deviation, correlation coefficient, coefficient of determination) that the most important characteristics of BS are understandable to the customer, and this depends on the employees and the declared policy of the bank.