Implementation of Lean Practices in Banks: A Qualitative Research

For Pakistani banks to remain competitive in this 21st century, they have to adopt and excel in practices that have brought significant competitive advantages to firms worldwide. Among these practices, lean management has played a significant role. There is a need to develop a systematic approach to access up to what extent firms are adopting lean practices. The objective of this research is to measure the level of adoptions of lean practices in banking sector in Pakistan. Face to face structured interviews were conducted and our findings suggest that banks are not implementing lean practices in true spirit. To implement lean practices holistically, bank employees should understand the philosophy of lean

Fintech in Pakistan: a qualitative study of bank’s strategic planning for an investment in fin-tech company and its challenges

The purpose of this study is to explore the strategic considerations in the process of searching for Fin-Tech investment targets and what challenges are faced by banks while its implementation. This study has examined the problems faced by banking sectors in investing in Fin-Tech. This study used a case study methodology of 5 banks in Pakistan on convenience based sampling. The major data sources include the field notes, recording and transcription of interviews collected from branch managers. The results relate to: (1) Fin-Tech in banking sector of Pakistan (2) Banks planning for the adoption of new financial technology, (3) Criteria for selecting Fin-Tech company, (4) Technology as banking career,(5) Barriers in the implementation of Fintech in Pakistan. It is suggested that Pakistan must upgrade its Fin-Tech infrastructure besides doing other things. The government is also needed to concentrate more efforts to overcome these problems

Determinants of Financial Self Sufficiency in Microfinance Institutions: A study of Pakistan, India and Bangladesh

This study is an attempt to identify the factors that are affecting the financial self-sufficiency of MFIs in Pakistan, India and Bangladesh. Panel data on MFIs of these countries for the year 2011 to 2015 is used. This yielded balanced panel data for thirty-two MFIs, comprising of 161 observations. Our results shows that Size of MFI and Loan Portfolio to total assets has positive impact while Portfolio at Risk, Breadth of outreach, Management inefficiency and operating cost ratio has negative impact on financial self-sufficiency

Determinants of Financial Self Sufficiency in Microfinance Institutions: A study of Pakistan, India and Bangladesh

This study is an attempt to identify the factors that are affecting the financial self-sufficiency of MFIs in Pakistan, India and Bangladesh. Panel data on MFIs of these countries for the year 2011 to 2015 is used. This yielded balanced panel data for thirty-two MFIs, comprising of 161 observations. Our results shows that Size of MFI and Loan Portfolio to total assets has positive impact while Portfolio at Risk, Breadth of outreach, Management inefficiency and operating cost ratio has negative impact on financial self-sufficiency

Sports Achievement Motivation and Sports Competition Anxiety: A Relationship Study

Anxiety and Motivation are important psychological variables in sports and its need to achieve high level of competition. Without knowledge of these two variables athletes cannot give best in competition. The aim of study was to find out the relationship between Anxiety and Motivation of intervarsity Badminton players. The total sample consisted of twenty players age ranged from 17 to 25 years. Sport Competition Anxiety Test (SCAT) and Sports Achievement Motivation Test was administered to collect the data. Mean, standard deviation, and Pearsion Product Moment Correlation were computed to analyze the data at .05 level of significant. It was found that significant negative relationship between Achievement Motivation and Anxiety

MS-ADS: multistage spectrogram image-based anomaly detection system for IoT security.

The innovative computing idea of Internet-of-Things (IoT) architecture has gained tremendous popularity over the last decade, resulting in an exponential increase in the connected devices and the data processed in the IoT networks. Since IoT devices collect a massive amount of sensitive information exchanged over the traditional internet, security has become a prime concern due to the more frequent generation of network anomalies. A network-based anomaly detection system can provide the much-needed efficient security solution to the IoT network by detecting anomalies at the network entry points through constant traffic monitoring. Despite enormous efforts by researchers, these detection systems still suffer from lower detection accuracy in detecting anomalies and generate a high false alarm rate and false-negative rate in classifying network traffic. To this end, this paper proposes an efficient Multistage Spectrogram image-based network Anomaly Detection System (MS-ADS) using a deep convolution neural network that utilizes a short-time Fourier Transform to transform flow features into spectrogram images. The results demonstrate that the proposed method achieves high detection accuracy of 99.98% with a reduction in the false alarm rate to 0.006% in classifying network traffic. Also, the proposed scheme improves predicting the anomaly instances by 0.75% to 4.82%, comparing the benchmark methodologies to exhibit its efficiency for the IoT network. To minimize the computational and training cost for the model re-training phase, the proposed solution demonstrates that only 40500 network flows from the dataset suffice to achieve a detection accuracy of 99.5%