Search Results for logistic-regression

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 purpose of this research is to study the impact of the presence of ISIS in Iraq on the reputation of joint-stock companies in this country. For this purpose, the accepted reputation of companies in the Iraqi Stock Exchange was measured through a regression model and using the hypothetical variable regarding the presence of ISIS in Iraq, the topic was investigated.

The method of conducting this research is descriptive-correlational, and the selected sample includes 35 companies that were listed on the Iraqi stock market during the years 2013 to 2018. The research hypotheses were tested using a multiple logistic regression model based on panel data.

   The results of this research indicate that there is a significant relationship between the presence of ISIS in Iraq and the reputation of companies accepted on the stock exchange of this country, which means that the reputation of companies on the Iraqi stock exchange decreased with the presence of ISIS in Iraq.

Keywords: company reputation, ISIS, Iraqi economy, logistic regression.

Abstract

This study aims to compare and improve the methods of building investment portfolios for a sample of Iraqi banks listed on the Iraq Stock Exchange, by comparing traditional methods such as the Markowitz model with modern techniques based on machine learning. The Markowitz model is key to balancing return and risk across the medium-variance optimization framework, a traditional model that many financial institutions rely on. The study focused on exploring the extent to which machine learning techniques such as key component analysis (PCA), supporting vector machine (SVM), logistic regression, and random forest can improve the performance of the investment portfolios of these banks in a volatile environment such as the Iraq Stock Exchange. These techniques rely on processing and analyzing huge financial data to discover hidden patterns and relationships that help increase returns and reduce risk more effectively compared to traditional methods. The historical financial data related to the shares and assets of the banks of the research sample in the Iraq Stock Exchange was used to evaluate the performance of portfolios according to indicators such as expected return, variance, and Sharpe ratio. The study aims to provide innovative solutions that help banks make smarter and more effective investment decisions, commensurate with the local market conditions and the economic and political challenges they face.     

Abstract

The current study aims to predict the failure of companies through the use of financial ratios derived from cash flow disclosure and then categorize them into two categories, the safe category means that the company is in a secure financial position capable of providing cash and fulfilling financial obligations, and the second category is the unsafe category where the company is In a troubled financial situation unable to meet the financial obligations, as (11) financial ratios derived from the cash flow statement were used, and the study was applied in the Iraq Stock Exchange, as the sample consisted of (42) companies listed in it and for the period 2016-2020. Through the use of logistic regression analysis to the prediction model that works to classify companies, with an accuracy rate of 52.4%, the model consists of (4) financial ratios, which are (the ratio of operational activity, the ratio of operating cash to sales, the ratio of operating cash return to total assets, and finally the percentage of cash return operating to total liabilities)