Due to globalization, environment, social, and governance (ESG) issues have gained importance over the last few decades. ESG is a worldwide issue, which clarifies that organizations throughout the world are lacking in contribution to the environment, society, and corporate governance characteristics for sustainable development. The problem of ESG spread over all stakeholders needs to be addressed. In this regard, rating agencies also have a close eye on ESG issues and have developed the methodology of score that aims to provide disclosure on ESG metrics which, in return, help investors and asset managers better differentiate between responsible and irresponsible companies. The ESG score has become an important tool among asset managers but is highly questioned in terms of reliability. The study objective was to develop machine learning algorithms to assess how balance sheet and income statement data impact the Thomson Reuters ESG score for non-financial public companies of USA, UK, and Germany from 2008 to 2020. In addition, the study also has an objective to assess which machine learning (ML) algorithm better predicts the ESG score using structural data, that is, return on assets (ROA), return on equity (ROE), earning per share (EPS), earnings before interest and taxes (EBIT), dividend yield, and net sales. The results concluded that balance sheet and income statement data are critical in explaining the ESG score, and the ANN algorithm outperforms with minimum RMSE and MAE values. All in all, the results of the study, based on the concept of artificial intelligence, bring suggestion for improvement to regulatory bodies, researchers, academia, practitioners, publicly listed companies around the globe, and last but not the least to the US, UK, and Germany markets. Moreover, it also provides suggestions for up-to-date compliance of ESG-relevant activities for boosting the firm performance.

Macroeconomic indicators are the key to success in the development of any country and are very much important for the overall economy of any country in the world. In the past, researchers used the traditional methods of regression for estimating macroeconomic variables. However, the advent of efficient machine learning (ML) methods has led to the improvement of intelligent mechanisms for solving time series forecasting problems of various economies around the globe. This study focuses on forecasting the data of the inflation rate and the exchange rate of Pakistan from January 1989 to December 2020. In this study, we used different ML algorithms like k-nearest neighbor (KNN), polynomial regression, artificial neural networks (ANNs), and support vector machine (SVM). The data set was split into two sets: the training set consisted of data from January 1989 to December 2018 for the training of machine algorithms, and the remaining data from January 2019 to December 2020 were used as a test set for ML testing. To find the accuracy of the algorithms used in the study, we used root mean square error (RMSE) and mean absolute error (MAE). The experimental results showed that ANNs archives the least RMSE and MAE compared to all the other algorithms used in the study. While using the ML method for analyzing and forecasting inflation rates based on error prediction, the test set showed that the polynomial regression (degree 1) and ANN methods outperformed SVM and KNN. However, on the other hand, forecasting the exchange rate, SVM RBF outperformed KNN, polynomial regression, and ANNs.

AI (artificial intelligence) is a significant technological advancement that has everyone buzzing about its incredible potential. The current research study evaluates the influence of supervised artificial intelligence techniques, i.e., machine learning techniques on the nonfinancial firms of Pakistan and focuses on the practical application of AI techniques for the accurate prediction of corporate risks which in turn will lead to the automation of corporate risk management. So, in this study, we used financial ratios for accurate risk assessment and for the automation of corporate risk management by developing machine learning algorithms using techniques, namely, random forest, decision tree, naïve Bayes, and KNN. A secondary data collection technique will be used. For this purpose, we collected annual data of nonfinancial companies in Pakistan for the period ranging from 2006 to 2020, and the data are analyzed and tested through Python software. Our results prove that AI techniques can accurately predict risk with minimum error values, and among all the techniques used, the random forest technique outperforms as compared to the rest of the techniques.

Researchers in the past discussed the psychological issue like stress, anxiety, depression, phobias on various forms, and cognitive issues (e.g., positive thinking) together with personality traits on traditional research methodologies. These psychological issues vary from one human to other human based on different personality traits. In this paper, we discussed both psychological issues together with personality traits for predicting the best human capital that is mentally healthy and strong. In this research, we replace the traditional methods of research used in the past for judging the mental health of the society, with the latest artificial intelligence techniques to predict these components for attaining the best human capital. In the past, researchers have point out major flaws in predicting psychological issue and addressing a right solution to the human resource working in organizations of the world. In order to give solution to these issues, we used five different psychological issues pertinent to human beings for accurate prediction of human resource personality that effect the overall performance of the employee. In this regard, a sample of 500 data has been collected to train and test on computer through python for selecting the best model that will outperform all the other models. We used supervised AI techniques like support vector machine linear, support vector machine radial basis function, decision tree model, logistic regression, and neural networks. Results proved that psychological issue data from employee of different organizations are better means for predicting the overall performance based on personality traits than using either of them alone. Overall, the novel traditional techniques predicted that sustainable organization is always subject to the control of psychological illness and polishing the personality traits of their human capital.