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The purpose of this study is to check the effectiveness of anti-money laundering (AML) regulations in Pakistan. The study investigates and analyses some key variables that may be influencing the effectiveness of anti-money regulations in Pakistan. Money laundering is most prevalent in the banking sector, as banks deals with the money’s deposition, withdrawal and transfer, therefore, it is necessary to evaluate the effectiveness of anti-money regulations on subjective judgments. It is an exploratory study in which I have tried to find the relationship and impact of three regulations, which are customer record keeping, employee training and suspicious transaction reporting on money laundering.

A sample of hundred responses has been collected from employees working in different banks located in Rawalpindi and Lahore through questionnaire. Questionnaire has been developed on the basis of different dimensions of the research variables.

It has been found that that there is an impact of employee training on money laundering in banking system. A moderate inverse relationship between employee training and money laundering and anti-money laundering regulation of customer record keeping has weak impact on money laundering in developing countries.

The research is limited to Pakistan only, and to apply the same concept in other countries, researchers need to check the financial institutions of that country as well.

It has been suggested that to stop money laundry, special budget should be allocated for the capacity building of employees through training. Timely guidance and assistance of foreign-trained instructors or experts in combating money laundering should be taken. Implementation of anti-money laundering regulations should be transparent, consistent and timely.

The purpose of the proposed model is to assist the e-business to predict the churned users using machine learning. This paper aims to monitor the customer behavior and to perform decision-making accordingly.

The proposed model uses the 2-D convolutional neural network (CNN; a technique of deep learning). The proposed model is a layered architecture that comprises two different phases that are data load and preprocessing layer and 2-D CNN layer. In addition, the Apache Spark parallel and distributed framework is used to process the data in a parallel environment. Training data is captured from Kaggle by using Telco Customer Churn.

The proposed model is accurate and has an accuracy score of 0.963 out of 1. In addition, the training and validation loss is extremely less, which is 0.004. The confusion matric results show the true-positive values are 95% and the true-negative values are 94%. However, the false-negative is only 5% and the false-positive is only 6%, which is effective.

This paper highlights an inclusive description of preprocessing required for the CNN model. The data set is addressed more carefully for the successful customer churn prediction.

In the presence of informal sector in the country, designing an energy policy and the pursuit of higher economic growth become challenging for emerging economies. These economies are usually resource starved, and the presence of underground economy leads to faulty estimates of energy demand. The authors explore the energy–growth nexus in the presence of underground economy for Pakistan, an emerging economy host to large informal sector and facing recurring energy crises.

The authors evaluate the impact of underground economy on energy demand in the presence of explanatory variables, including official gross domestic product (GDP), foreign direct investment and financial development. The authors first assess the influence of official economy on the consumption of energy. The authors investigate how energy consumption is influenced solely by underground economy. Finally, the authors evaluate the impact of true GDP on the energy consumption. The authors employ combined cointegration method of Bayer and Hanck (2013) and then apply vector error correction model.

The results reveal that official GDP, underground economy and true GDP positively and significantly affect energy consumption in both short and long run. Similarly, financial development as well as foreign direct investment enhance energy consumption. The authors find unidirectional causality between energy consumption and official GDP variables (OGDP → EC), underground economy (UE → EC) and true GDP variables (TGDP → EC) in the long run. The authors observe bidirectional causality in the short run between energy consumption and official GDP (OGDP ↔ EC) and true GDP (TGDP ↔ EC).

To the best of the authors' knowledge, no study examines the causal relationship of energy consumption and underground economy. Overall, the findings assist policymakers to consider and implement different energy-related policies considering the significant role of underground economy for energy consumption in Pakistan.