Role of Metaheuristic Optimization in Portfolio Management for the Banking Sector

2019 ◽  
pp. 198-220
Author(s):  
Soumen Mukherjee ◽  
Arpan Deyasi ◽  
Arup Kumar Bhattacharjee ◽  
Arindam Mondal ◽  
Anirban Mukherjee
Keyword(s):  
Portfolio Management ◽  
Banking Sector ◽  
Optimization Technique ◽  
Support Vector ◽  
Metaheuristic Optimization ◽  
Computational Overhead ◽  
Feature Optimization ◽  
Response Output ◽  
Bank Marketing

In this chapter, the importance of optimization technique, more specifically metaheuristic optimization in banking portfolio management, is reviewed. Present work deals with interactive bank marketing campaign of a specific Portugal bank, taken from UCI dataset archive. This dataset consists of 45,211 samples with 17 features including one response/output variable. The classification work is carried out with all data using decision tree (DT), support vector machine (SVM), and k-nearest neighbour (k-NN), without any feature optimization. Metaheuristic genetic algorithm (GA) is used as a feature optimizer to find only 5 features out of the 16 features. Finally, the classification work with the optimized feature shows relatively good accuracy in comparison to classification with all feature set. This result shows that with a smaller number of optimized features better classification can be achieved with less computational overhead.

Metaheuristic-Based Feature Optimization for Portfolio Management

2019 ◽  
pp. 109-125
Author(s):  
Arup Kumar Bhattacharjee ◽  
Soumen Mukherjee ◽  
Arindam Mondal ◽  
Dipankar Majumdar
Keyword(s):  
Portfolio Management ◽  
Credit Card ◽  
Metaheuristic Algorithms ◽  
Support Vector ◽  
Large Set ◽  
Data Set ◽  
Standard Data ◽  
Feature Optimization ◽  
Banking Institution ◽  
The Given

In the last two to three decades, use of credit cards is increasing rapidly due to fast economic growth in developing countries and worldwide globalization issues. Financial institutions like banks are facing a very tough time due to fast-rising cases of credit card loan payment defaulters. The banking institution is constantly searching for the perfect mechanisms or methods to identify possible defaulters among the whole set of credit card users. In this chapter, the most important features of a credit card holder are identified from a considerably large set of features using metaheuristic algorithms. In this work, a standard data set archived in UCI repository of credit card payments of Taiwan is used. Metaheuristic algorithms like particle swarm optimization, ant colony optimization, and simulated annealing are used to identify the significant sets of features from the given data set. Support vector machine classifier is used to identify the class in this two-class (loan defaulter or not) problem.

BAT algorithm based feature selection: Application in credit scoring

2021 ◽  
pp. 1-10
Author(s):  
Diwakar Tripathi ◽  
B. Ramachandra Reddy ◽  
Y.C.A. Padmanabha Reddy ◽  
Alok Kumar Shukla ◽  
Ravi Kant Kumar ◽  
...  
Keyword(s):  
Feature Selection ◽  
Fitness Function ◽  
Credit Scoring ◽  
Optimization Technique ◽  
Predictive Performance ◽  
Bat Algorithm ◽  
Vital Role ◽  
Support Vector ◽  
Comparative Performance

Credit scoring plays a vital role for financial institutions to estimate the risk associated with a credit applicant applied for credit product. It is estimated based on applicants’ credentials and directly affects to viability of issuing institutions. However, there may be a large number of irrelevant features in the credit scoring dataset. Due to irrelevant features, the credit scoring models may lead to poorer classification performances and higher complexity. So, by removing redundant and irrelevant features may overcome the problem with large number of features. In this work, we emphasized on the role of feature selection to enhance the predictive performance of credit scoring model. Towards to feature selection, Binary BAT optimization technique is utilized with a novel fitness function. Further, proposed approach aggregated with “Radial Basis Function Neural Network (RBFN)”, “Support Vector Machine (SVM)” and “Random Forest (RF)” for classification. Proposed approach is validated on four bench-marked credit scoring datasets obtained from UCI repository. Further, the comprehensive investigational results analysis are directed to show the comparative performance of the classification tasks with features selected by various approaches and other state-of-the-art approaches for credit scoring.

Organizational Justice and Employee Sustainability: The Mediating Role of Organizational Commitment

2020 ◽  
Vol 3 (3) ◽  
pp. 12-22
Author(s):  
Mehreen Fatima ◽  
Zeeshan Izhar ◽  
Zaheer Abbas Kazmi
Keyword(s):  
Organizational Commitment ◽  
Organizational Justice ◽  
Developing Country ◽  
Design Methodology ◽  
Direct Relationship ◽  
Banking Sector ◽  
Mediating Role ◽  
Research Gap ◽  
The Impact

Purpose- The primary purpose of the study is to determine the impact of organizational justice (OJ) on employee sustainability. Along with that, it also describes how organizational commitment mediates this direct relationship. This study includes all dimensions of OJ which are distributive, procedural and interactional (interpersonal & informational) within the context of a developing country (Pakistan). Design/Methodology- This study has considered employees working in the banking sector of Pakistan. Two hundred ten questionnaires were received back from employees. Regression analysis was used to analyze direct relationships between variables, while smart partial least squares (PLS) were used for mediation analysis. Findings- Results demonstrated that all hypothesis were accepted and it was also confirmed that organizational commitment (OC) mediates the direct relationship between OJ and employee sustainability (ES). Originality/value- Multidimensional construct of organizational justice was tested in this study, in the context of a developing country (Pakistan), to address the research gap.

Role of The Halal Industry in Recovering the National Economy in Covid-19 Pandemic

2020 ◽  
Vol 8 (2) ◽  
pp. 202-214
Author(s):  
Cucu Susilawati
Keyword(s):  
National Economy ◽  
Banking Sector ◽  
Developed Countries ◽  
Online News ◽  
Financial Industry ◽  
National Economic ◽  
Halal Food ◽  
Vigorous Growth ◽  
Business Sectors

The outbreak of the Covid-19 pandemic in Indonesia is attacking not only public health but also the economy. The presence of Covid-19 has many important impacts on developed countries. There are at least four industries most impacted by this pandemic, including households, MSMEs, companies and the financial industry. However, the halal industry is believed to be more resilient to the Covid-19 pandemic. This durability is because of the principles attributed to the halal sector, namely the importance of fairness, balance and openness. The author’s goal is therefore to carry out more in-depth research on the role of the halal industry in supporting the national economy, which is under pressure because of the COVID-19 pandemic. This type of study is a literature review with a material analysis approach that explores the conditions of the halal industry in Indonesia in depth. The material received is as books, published information, and online news. The findings of this study reveal that there are three halal business sectors that are believed to be more vulnerable to the Covid-19 pandemic in order to facilitate national economic recovery. Halal finance, halal food and halal fashion industries are among them. Halal finance from both the banking sector and the Islamic stock market has proved to be more robust than the mainstream financial sector. Besides guaranteed halal food, its wellbeing is also guaranteed, and halal fashion is now on the rise as Muslim fashion is increasingly innovative and global. We believe the three of them to have experienced vigorous growth, and also to continue to draw customers. And also after the Covid-19 pandemic, these three sectors could survive. Thus the halal industry also contributes to Indonesian economy.

ANALYSIS OF APPLICATION OF BLOCKCHAIN TECHNOLOGIES FOR THE BANKING SECTOR

2017 ◽  
Author(s):  
Svitlana Ilkovych ◽  
◽  
Maryna Korol ◽  
Keyword(s):  
Banking Sector ◽  
Blockchain Technology ◽  
Current State ◽  
Global Banking ◽  
Banking Institutions ◽  
Pros And Cons ◽  
Further Development

The article considers the essence of blockchain technology and the possibility of its application in the banking sector. The current state of development and application of blockchain technologies in various industries is analyzed. The pros and cons of using blockchain technologies for the banking sector are identified. Emphasis is placed on the role of blockchain technologies in the further development of the banking sector. The most promising directions of development of this technology are considered. Particular attention is paid to examples of the use of blockchain technology by global banking institutions.

Application Of Technology Acceptance Model Of Unified Payment Interface Services Adoption In Mysuru City: An Exploratory Study

2019 ◽  
Vol 118 (6) ◽  
pp. 25-35
Author(s):  
Vinay S
Keyword(s):  
Technology Acceptance ◽  
Technology Acceptance Model ◽  
Structural Equation ◽  
Banking Sector ◽  
Primary Data ◽  
Equation Modeling ◽  
Security Issues ◽  
Acceptance Model ◽  
Structured Questionnaire

Continuous development of technological innovations especially in the banking sector have stirred competition which has changed the way businesses operate resulting in the introduction of Unified Interface Payment (UPI) services. This study was conducted in order to analyse the adoption of UPI services through Technology Acceptance Model (TAM) in Mysuru. Objectives of study were framed to determine the demographic factors that influence the practices of UPI by the customers, to examine the role of banks in integrating UPI services and products and to assess the various security issues affecting the usage of UPI services by Mysuru customers. Based on these objectives a structured questionnaire was prepared and primary data was collected from 165 respondents. Data was analysed making use of SPSS and other models namely Structural Equation Modeling with Analysis of Moment Structures (AMOS) Software. Finally the researchers identifies that there is a need for convergence of customer’s preference for safe and easy banking transactions. This study revealed that the customer’s model have to be well integrated for progress in UPI operations.

The Role of Mobile Money in Moderating Financial Exclusion: A Tanzanian Experience

2020 ◽  
Vol 2 ◽  
pp. 1-24 ◽  
Author(s):  
Deogratius Joseph Mhella
Keyword(s):  
Developing Countries ◽  
Financial Institutions ◽  
Banking Sector ◽  
Research Question ◽  
Microfinance Institutions ◽  
Mobile Money ◽  
Access To Credit ◽  
Financial Exclusion

Prior to the advent of mobile money, the banking sector in most of the developing countries excluded certain segments of the population. The excluded populations were deemed as a risk to the banking sector. The banking sector did not work with cash stripped and the financially disenfranchised people. Financial exclusion persisted to incredibly higher levels. Those excluded did not have: bank accounts, savings in financial institutions, access to credit, loan and insurance services. The advent of mobile money moderated the very factors of financial exclusion that the banks failed to resolve. This paper explains how mobile money moderates the factors of financial exclusion that the banks and microfinance institutions have always failed to moderate. The paper seeks to answer the following research question: 'How has mobile money moderated the factors of financial exclusion that other financial institutions failed to resolve between 1960 and 2008? Tanzania has been chosen as a case study to show how mobile has succeeded in moderating financial exclusion in the period after 2008.

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