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This study aims to enhance credit risk identification, improve loan borrower review efficiency, and increase default prediction accuracy rate using data mining and machine learning techniques. The study also employs deep learning to develop a consumer loan default prediction model that minimizes credit risks and ensures consistent development. The researchers collected data from a survey of 1000 participants, stratified into local and foreign banks, and selected the top 11 banks based on turnover and customer volume. To construct the machine learning model, Keras, a neural network library that runs on TensorFlow, was utilized. The model predicts loan applicant default likelihood. The study's practical implications demonstrate a noteworthy success rate of customer default prediction, which can significantly benefit banks. The model was evaluated on a test set of 250 records and achieved a test set accuracy of 95.2%, correctly predicting the default state of 238 out of 250 respondents.

The prime purpose of the research was to examine, through empirical study, and help determine the government’s role as moderator in the relationship between Porter’s diamond firm strategy, structure, and rivalry construct and the firm’s performance in Pakistan’s automotive sector. In the study, the research philosophy was positivism, and a deductive approach was applied as the main reason to establish generalizations in the results. With a view to determining the magnitude, direction, and significance, researchers emphasized descriptive and inferential statistical techniques. Data was solicited from 166 sample respondents through a structured questionnaire. To establish the reliability perspective and validity context of the questionnaire constructs, a pilot study was part of the research study. Parametric and nonparametric statistical techniques also became an integral part of analysis for drawing objectively based conclusions. For moderation purposes, the tool was macro, Process v3.0 (model 1). The study findings aptly indicate that in a theoretical context, the relationship is not significant, whereas the practical perspective reflects that government policy does influence industry firms’ performance; however, the mediating role of government between Porter’s constructs, viz., firm strategy, structure, and rivalry, and industry firms’ performance documented no impact. In the constituencies of academia, automotive sector players, and policymakers, the moderation results shed light on theoretical and practical contextualization.

The paper examines four main sources of financial risk exposure for four banks in the United Arab Emirates during the years 2003 to 2009. It begins by measuring stability levels for different types of financial risks using the coefficient of variation measure. The research then proceeds to investigate the nature of the relationship between financial risk and the actual reported financial performance of each company, namely the return on equity and return on assets. The findings show that all four sources of financial risk showed significant stability while both profitability indicators exhibited significant instability over the same period. Moreover, performance on the return on assets indicator was strongly and positively tied to a company’s return on equity indicator across the four banks but exhibited little or no interconnection with the four types of risks that banks are commonly exposed.

The overriding purpose of the study was to ascertain through empirical research whether Porter’s diamond firm strategy, structure and rivalry construct influence the key automotive industry of Pakistan. The Researchers applied positivism research philosophy to help generalize the research study outcomes. Descriptive analytics was deployed to help determine the significance, direction and magnitude of the study. Primary data was obtained from senior managers working in the industry by applying a structured questionnaire for subsequent analysis. While soliciting data, an 85.6% response rate was documented. Statistical techniques were applied which included parametric and nonparametric to arrive at objectively based results to draw safe conclusions.  The research findings have created significant value for practitioners in the auto industry as well as the decision-makers at the helm of policy formulation. Pragmatic inferences to be drawn from the research findings include improving the performance of the automotive sector by internalizing Porter’s diamond construct firm strategy, structure, and rivalry. Results in other industries may be tested with future research to help establish generalizability in the area.

The overriding purpose of the study was to ascertain through empirical research whether Porter’s diamond firm strategy, structure and rivalry construct influence the key automotive industry of Pakistan. The Researchers applied positivism research philosophy to help generalize the research study outcomes. Descriptive analytics was deployed to help determine the significance, direction and magnitude of the study. Primary data was obtained from senior managers working in the industry by applying a structured questionnaire for subsequent analysis. While soliciting data, an 85.6% response rate was documented. Statistical techniques were applied which included parametric and nonparametric to arrive at objectively based results to draw safe conclusions.  The research findings have created significant value for practitioners in the auto industry as well as the decision-makers at the helm of policy formulation. Pragmatic inferences to be drawn from the research findings include improving the performance of the automotive sector by internalizing Porter’s diamond construct firm strategy, structure, and rivalry. Results in other industries may be tested with future research to help establish generalizability in the area.

The prime purpose of the research was to examine, through empirical study, and help determine the government’s role as moderator in the relationship between Porter’s diamond firm strategy, structure, and rivalry construct and the firm’s performance in Pakistan’s automotive sector. In the study, the research philosophy was positivism, and a deductive approach was applied as the main reason to establish generalizations in the results. With a view to determining the magnitude, direction, and significance, researchers emphasized descriptive and inferential statistical techniques. Data was solicited from 166 sample respondents through a structured questionnaire. To establish the reliability perspective and validity context of the questionnaire constructs, a pilot study was part of the research study. Parametric and nonparametric statistical techniques also became an integral part of analysis for drawing objectively based conclusions. For moderation purposes, the tool was macro, Process v3.0 (model 1). The study findings aptly indicate that in a theoretical context, the relationship is not significant, whereas the practical perspective reflects that government policy does influence industry firms’ performance; however, the mediating role of government between Porter’s constructs, viz., firm strategy, structure, and rivalry, and industry firms’ performance documented no impact. In the constituencies of academia, automotive sector players, and policymakers, the moderation results shed light on theoretical and practical contextualization.

This study aims to enhance credit risk identification, improve loan borrower review efficiency, and increase default prediction accuracy rate using data mining and machine learning techniques. The study also employs deep learning to develop a consumer loan default prediction model that minimizes credit risks and ensures consistent development. The researchers collected data from a survey of 1000 participants, stratified into local and foreign banks, and selected the top 11 banks based on turnover and customer volume. To construct the machine learning model, Keras, a neural network library that runs on TensorFlow, was utilized. The model predicts loan applicant default likelihood. The study's practical implications demonstrate a noteworthy success rate of customer default prediction, which can significantly benefit banks. The model was evaluated on a test set of 250 records and achieved a test set accuracy of 95.2%, correctly predicting the default state of 238 out of 250 respondents.

The paper examines four main sources of financial risk exposure for four banks in the United Arab Emirates during the years 2003 to 2009. It begins by measuring stability levels for different types of financial risks using the coefficient of variation measure. The research then proceeds to investigate the nature of the relationship between financial risk and the actual reported financial performance of each company, namely the return on equity and return on assets. The findings show that all four sources of financial risk showed significant stability while both profitability indicators exhibited significant instability over the same period. Moreover, performance on the return on assets indicator was strongly and positively tied to a company’s return on equity indicator across the four banks but exhibited little or no interconnection with the four types of risks that banks are commonly exposed.