Al Kut Journal of Economics and Administrative Sciences

This paper attempts to investigate the nexus between inflation and unemployment rates in Iraq for the period (2004–2024) based on Phillips curve paradigm, with an application of involves in autoregressive distributed lag ARDL bounds testing methodology. Results reveal a negative relationship between inflation and unemployment in the long run as well as in the short run, but it is weaker than expected because of specific features of the Iraqi economy, given that Iraq is a dominated country depending on oil revenues

The research aims to study the impact of employing artificial intelligence control tools in enhancing the credibility of periodic financial statements in Iraqi private banks, with a focus on the case study of the International Trust Bank as a banking model that seeks to adopt digital transformation technologies in the fields of auditing and financial control. The study relied on the descriptive-analytical approach through the analysis of the bank's financial statements for the period (2020-2024), in addition to using interviews with some internal control staff. The results showed that The implementation of artificial intelligence systems contributed to reducing the accounting error gap and increased the speed and efficiency of auditing, which reflected positively on the level of investor and customer confidence in the bank. The study recommended that the experience should be generalized to the rest of Iraq's private banks in order to enhance transparency and the quality of financial reporting

The purpose of the research is to measure the role of financial agility, represented by (organizational factors, technological factors, human factors, technical and contextual factors, and structural and legal factors), as a driving force for improving data-driven innovation capabilities, represented by (marketing orientation capabilities, infrastructure capabilities, and innovation talent capabilities), among a sample of employees in financial intermediation companies in thi-qar province, including seven companies (al-zaamili, al-qand iq, fnjn, acad, al-jihad, al-nawafith, al-ehab). Due to the importance of financial agility, which has emerged as a necessity after the rapid changes witnessed in the business environment, characterized by the speed and diversity of changes, some companies have been able to adapt to these transformations and have succeeded in entering the competitive market with strength, while others have suffered from the challenges resulting from that environment, where they are now facing difficulties in maintaining their position due to the challenges imposed by market opportunities and threats. Accordingly, the research problem was formulated in the form of a main question: (does financial agility act as a driving force for improving data-driven innovation capabilities among a sample of employees in financial intermediation companies in thi-qar province?), and to achieve the main objective of the research, the study relied on the questionnaire as a primary tool for collecting the necessary data, as (100) questionnaires were distributed to the employees the concerned companies, and retrieved (83) statements, of which (7) are defective, and (76) are valid for analysis, which indicates that the sample response rate reached (76%). To analyze the research outputs, two statistical programs were adopted: (spss & amos.v.29) for extracting targeted tests. Accordingly, the research presented several results, the most important of which is the existence of a relationship and impact between financial agility and data-driven innovation capabilities, meaning that financial agility is considered a major factor in enabling companies to improve their data-based creative capabilities, as it allows them flexibility in managing financial resources and fully utilizing opportunities, thereby enhancing their ability to innovate and adapt to market changes more effectively. The research also recommended that companies should focus on establishing flexible financial plans that respond to rapid market changes, which in turn requires continuous re-evaluation of financial resources and ensuring their effective allocation to support innovation

The forward-looking approach to the measurement of credit risk is included in IFRS 9. What SICR, not to mention dynamic migration mechanisms, maybe, needs more detailed scrutiny. It is one of the most radical changes to accounting policing in the present days. Key issues include how credit losses should be accounted for, measured and disclosed by banks as well as the impact on regulatory capital requirements and risk-management practices. Now, as it is in the future, for banks and their overseers and any other believer that accurate financial reporting standards and sound risk management capabilities are a Good Thing to be done under whatever new framework we devise – it is of crucial importance that they understand what causes migration across stages. This initial up of several implementation outcome studies focuses on the key drivers for movement in accounts under IFRS 9, and what impact material increases in credit risk have upon expected loss calculations at retail banks and thus addresses a lack of available empirical data in the practical application results to date for IFRS 9. Drawing on a unique dataset of 2.4 million retail banking accounts from five large European banks covering the period between 2018 to 2023, this article utilizes logistic regressions models, survival analysis and machine learning methods to identify the key drivers that lead to migration into diverse phases of the customer relationship lifecycle. It uses both numerical and non-numerical characteristics of credit deterioration. The findings indicate that payment delinquency aggravated is the most important factor, with an odds ratio of 4.23 (p < p < 0.001). Following these other changes in debt-income ratio and their increase on Rem stage (odds ratio of 2.17, p8000). Last but not least come the macroeconomic stress indicators, and since we already know that they are used by every bank, even outside IFRS its odds ratios for taking such decisions exceeded 1.89 (p-value<0.001). Stage 2 classifications produce expected loss provisions 3.2 times the size of those from Stage 1 provisions with an accurate rate of 89% in predicting future defaults. But: it varies widely in how much it is used by product type and location. It should be noted that this research is focused on the European retail lending markets, and other kinds of lenders in Europe and in other areas of the banking industry might not be generalized to. However, these results contain useful recommendations for banks who may need to re-calibrate their thresholds of a high increase in credit risk or enhance the accuracy of expected loss by including into their macroeconomic scenarios indicators related to client behavior that are forward looking. This study is among the first extensive analyses on migration behavior across various European banking enterprises and provides some interesting practice that helps understand practical implications of IFRS 9 implementation as well as regulatory compliance. These findings will inform future policy work at the European Capital Adequacy system or national regulators and also general banking industry best-practice.

The purpose of forecasting models premised on time series (TS) is to generate predictive models that provide a closer approximation of future data, with an acceptable error. A proposed framework is developed within the framework of this study as a hybrid model to improve the forecasting accuracy of the traditional TS models based on the Multivariate Adaptive Regression Splines (MARSplines) technique. The integration targets to take advantage of the flexibility and adaptation of MARSplines in the TS. In order to compare the performance of the suggested model, historical data over a six-year period of the Borsa Istanbul were used as a case study. The results indicate that the combined regression-based method gives a significant increase in the predictive capabilities and practical importance of the time series model

This study aims to identify the impact of cloud financial operations on digital finance operations in relation to financial decision-making. To achieve the objective of the study, the researcher presented the problem of the study. The study's problem was represented in answering the following question: What is the impact of applying cloud financial operations on digital finance for financial decision-making? The researcher also presented some previous studies related to each of cloud financial operations and digital finance in order to determine the relationship that links cloud financial operations to digital finance for financial decision-making.
Researcher also relied on: Designe survey list to test study hypothesis - Study community and sample of respondents identified in 3 groups of workers in: Companies provide innovative financial services - some banks in Iraq consisting of 150 individuals including: workers in the information systems sector, workers in the financial sector, workers in the finance and credit sector. The study hypothesis has been tested by using the statistical program SPSS.
Study concluded : Cloud financial operations are all types of financial transactions that take place at the individual and institutional levels. These were done using tangible and intangible technological means depending on communications technology, the Internet, and cloud storage and access to financial data. Integration takes place between cloud financial operations and digital finance operations; hence, the more capable the use of cloud financial operations, the more the use of digital finance in making financial decisions will be possible..

This paper proposed a new distribution called the Lomax-Pareto distribution using the theory of mean generalized distributions applied to the Lomax distribution. The new distribution (Lomax-Pareto distribution) had four parameters (α, β, θ,ʎ) , and some crucial functions were developed for this distribution, such as the joint function, the probability function, and the reliability function. Two estimation procedures for the parameters of this new distribution were proposed, namely, the maximum likelihood estimation and the least squares estimation methods. Monte Carlo simulation was then used to compare methods, where samples of sizes 10, 25, 50, and 100 were generated, and criteria such as MSE and Bias were used. The results showed that the LSE method was better than the MLE method, as it yielded some good and acceptable results.