Search Results for financial-behavior
Abstract
Based on the importance of the role played by financial intelligence and behavior in strengthening companies in the financial markets and stabilizing them by achieving an added market value for companies assuming the moral and important impact of financial intelligence and behavior and proving the statistical hypothesis, a major problem was assumed represented in the statement of the impact of financial intelligence and behavior on the financial performance of financial decision makers in the sample of Iraqi companies? Therefore, the study sought to diagnose and explore the companies that provided and achieved levels of financial intelligence represented by profits, revenues and returns, and financial behavior represented by deals and sessions, within the sectors (banks and industry) of the study sample, and using the (ARDL) methodology in a period that extended 2010-2023 with a detailed theoretical description of the latest concepts and methods of measurement for both financial intelligence and behavior, as the results of the analysis of the first model of banks proved that there is a direct, significant and escalating impact that begins with revenues through profits and ends Returns are the most important in such a sector characterized by many and severe risks and from different internal and external environments, unlike the industry sector, which is affected by other factors, so it is necessary to follow the best market model that has achieved the highest levels of intelligence and behavior of financials for the duration of the research in order to be a model for consolidating weak and declining levels of behavior and intelligence in a number of companies and sectors.
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.