Search Results for financial-sustainability

Abstract

This research aims to demonstrate the contribution of internal auditing to help manage and reduce financial risks and achieve financial sustainability. The research included a number of variables to identify risks, their types, the foundations of their management, and the procedures followed to reduce risks. To achieve the research objectives and test its hypotheses, we conducted a case study of the most important financial risks that are likely to face self-financing units in Nineveh Governorate.

The most important results of the study reached by the researcher were the absence of regulations governing the performance of internal auditing to carry out its role in managing and reducing risks, the weakness of the role of internal auditing in achieving financial sustainability, and the lack of a clear program for internal auditing prepared in accordance with sustainability. The study concluded with a number of recommendations, the most important of which are: The necessity of ensuring that there is a plan in each department that includes steps and procedures to reduce the financial risks that may be exposed to and review them continuously. The importance of internal audits directing the unit to prepare reports and data related to sustainability in general and financial sustainability in particular. Internal audit must measure the financial sustainability of financial reporting information through specific quantitative measures

Abstract

This study examines the sales tax audit mechanism and its impact on financial sustainability in Iraq (the case of telecommunications companies) and investigates the main problems in the tax audit process with regard to the appropriateness of the type of audit used, the appropriateness of methods for selecting the audit case, and audit examination techniques used. Mixed in order to achieve research objectives and answer research questions. Specifically, the methods used in the study include submitting questions to auditors and tax investigators, and documentary and literary analysis. Using these research methods, the results of the study reveal that the sales tax audit mechanism is still undeveloped and uses a minimal set of tax audit activities that are conducted in order to address specific risks, in contrast to the concept of the sales tax audit mechanism which is a tool that improves voluntary compliance and increases performance future revenue and thus achieving financial sustainability by educating and helping taxpayers understand their tax obligations. Good interaction between tax auditors and taxpayers, in the end, the study presents the measures that can be taken by the public tax authority to alleviate the problems in the tax audit process.

Abstract

The concept of sustainability is concerned with the indicators that can be relied upon in estimating the sustainability of the economy at the macro level and what is related to it.  Financial sustainability, on the other hand , focuses on public debt and its potential positive effects if used efficiently in financing the resource gap, or negative if Limit yourself to financing current consumption.

As far as the matter is concerned with the Iraqi economy, the rentier nature of the economy has spared the economy from resorting to public debt for the purposes of covering the “gap of resources”, and resorting to it was limited in times of crisis, preserving ad hoc attitude, away from its supposed role in achieving sustainable development. The aim of this paper is to examine the economic implications of financial sustainability and its projection on the Iraqi situation, while the research problem was to investigate the economic significance of the indicators in terms of their qualitative difference in the rentier developing economy such as Iraq.   The paper reached conclusions in correspondence with its problem and hypothesis

Abstract

This research aims to diagnose the impact of the quality of bank assets from loans and credit loans on cash flows generated from operating activities by studying a sample of commercial banks listed on the Iraq Stock Exchange for a period of 10 years from 2010 to 2019. The research used the descriptive analytical approach based on the SPSS statistical program by measuring the strength of the correlation between the study variables by simple linear regression and continuity of chains. The problem of the research was whether cash flows through operating activities are affected by the quality of bank assets? The research found that there is a positive moral impact between the quality of assets and cash flows. The quality of assets is a good indicator of the predictability of future cash flows as a result of the direct relationship and the positive impact between them. This encourages banks to focus on achieving the quality of assets with the aim of obtaining cash flows from their operating activities. The research recommends the need to reduce the proportion of non-performing loans through credit undertakings and by managing the potential risks of bank loans and other assets while at the same time adopting sustainability policies that achieve high profitability ratios and thus high cash flows.

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.