The annual rate of cybersecurity breaches has risen in the last few years, exposing millions of records in some cases. The average data breach cost in 2021 was a massive $4.24 million. This study examines customer awareness and satisfaction with cybersecurity in the context of the digital transformation of banking in Saudi Arabia. The study is empirical and based on the data collected from 355 banking customers in Saudi Arabia. Three significant aspects of cybersecurity, including cyberattacks, phishing, and hacking, have been analyzed through various dimensions. Customer satisfaction with bank cybersecurity assistance and their expectations of technical support and services on cybersecurity has also been studied. ANOVA and bivariate regression analysis are used to study the impact of cyberattack, phishing, hacking, cybersecurity assistance, and expectations on cybersecurity’s technical awareness on customer satisfaction. The results show that digital transformation has boosted the banking sector, and users benefit from online services. However, an increase in the awareness level of customers on cyberattack, phishing, and hacking activities will influence customers’ satisfaction with digital transactions. The results also revealed that customers need more satisfaction on security level aspects from the bank’s side, and banks should provide regular training programs to safeguard customers from cyberattacks. If banks prepare more secure cybersecurity management, their long-term sustainability goals could be easily achieved.

One of the most significant areas of local government in the world is the municipality sector. It provides various services to the residents and businesses in their areas, such as water supply, sewage disposal, healthcare, education, housing, and transport. Municipalities also promote social and economic development and ensure democratic and accountable governance. It also helps in encouraging the involvement of communities in local matters. Workers of Municipalities need to maintain their services regularly to the public. The productivity of the employees is just one of the main important factors that influence the overall organizational performance. This article compares various machine learning algorithms such as XG Boost, Random Forest (RF), Histogram Gradient Boosting Regressor, LGBM Regressor, Ada Boost Regressor, and Gradient Boosting Regressor on the dataset of municipality workers. The study aims to propose a machine learning approach to predict and evaluate the productivity of municipality workers. The evaluation of the overall targeted and actual productivity of each department shows that out of 12 different departments, only 5 departments were able to meet their targeted productivity. A 3D Scatter plot visually displays the incentive given by the department to each worker based on their productivity. The results show that XG Boost performs best in comparison with the other five algorithms, as the value of R Squared is 0.71 and MSE (Mean Squared Error) is 0.01.