Predicting Risk through Artificial Intelligence Based on Machine Learning Algorithms: A Case of Pakistani Non-Financial Firms

Abstract

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 non-financial firms of Pakistan and focuses on the practical application of AI techniques for the accurate prediction of corporate risks which in tum 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 algorithm using techniques namely Random Forest, Decision tree, Polynomial regression, Artificial neural networks, SVM (Linear), SVM (RBF), Naive Bayes and KNN. Secondary data collection technique will be used. For this purpose, we collected data of non-financial companies of Pakistan for the period ranging from 2006 to 2020 and data is 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 Random Forest, ANN and Decision tree perform better than rest of the techniques.