Diffusion and Adoption of Credit Cards in Indian Banking Sector

2018 ◽  
Vol 1 (2) ◽  
pp. 1-20
Author(s):  
Dr. Mandeep Kaur ◽  
Dr.Kamalpreet Kaur
Keyword(s):  
Credit Card ◽  
Banking Sector ◽  
Credit Cards ◽  
Annual Reports ◽  
Banking Services ◽  
Time Period ◽  
Dichotomous Variable ◽  
Reserve Bank ◽  
The Status ◽  
Reserve Bank Of India

The study emphasizes on the identification of factors, which may have influenced the banks to adopt credit cards along with their traditional banking services. Bank specific variables were investigated to deepen the understanding on the diffusion and adoption of credit cards. The data relating to sampled banks’ characteristics have been collected from database of Reserve Bank of India. To know about the status of the bank regarding its adoption of credit card, the websites and annual reports of the banks were explored during different intervals of time period of the study. The study considers the dependent variable i.e. adoption of credit cards as dichotomous variable, whether or not a bank renders the credit card services, denoting 1 if the bank has adopted credit card otherwise 0. The logistic regression has thus been applied to get the valid and reliable results. The empirical findings reveal that, size, non-interest income, non performing assets, profitability, age and market share of the bank are the variables which have contributed significantly in the diffusion and adoption of credit cards.

scholarly journals Demonetisation Impact over Banking Loans & Advances: A Case Study

2019 ◽  
Vol 7 (4) ◽  
pp. 51-64
Author(s):  
Narsaiah Neralla
Keyword(s):  
Financial Services ◽  
Banking Sector ◽  
Study Data ◽  
Ecosystem Dynamics ◽  
Banking Services ◽  
Legal Tender ◽  
The Status ◽  
The Government ◽  
Case Study Data

The demonetisation footstep by the Government of India twisted complicated influences in the economy. Complete sectors of the economy had faced and produced mixed sensation results over the decision of demonetisation. India’s financial services struggled with demonetisation; on the other hand demonetisation affects utmost over the banking sector because it is substantial influenced services to transform money circulation in an Indian economy. Eradicating components of currency notes from circulation in an economy is demonetisation. It is as the processes of components of money are denied the status of legal tender. Consequently, ceased currency notes will not be account as valid currency in an economy. The term ‘demonetization’ is an instrument to shrink Inflation, Black Money, Corruption and terror funding, this step discourages a cash dependent economy in India. Government of India drive towards demonetisation has given a strong push to the popularity of digital banking and made helps with the alternative arrangements of e-banking and e –wallet to trade and commerce. Exploring the demonetisation emergence in an economy and impact on banking services ecosystem dynamics, this study take an abductive approach anchored in over 4 years of case study data regarding. The present study foremost intention is to be analysing the demonetisation impact over banking loans and advances. In this regard the present study is to be examining the pre demonetisation and post demonetisation period.

scholarly journals DETECTING CREDIT CARD FRAUD USING MACHINE LEARNING ALGORITHMS

InterConf ◽  
2021 ◽  
pp. 393-403
Author(s):  
Olexander Shmatko ◽  
Volodimir Fedorchenko ◽  
Dmytro Prochukhan
Keyword(s):  
Machine Learning ◽  
Financial Services ◽  
Credit Card ◽  
Banking Sector ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Credit Cards ◽  
Internet Banking ◽  
Machine Learning Algorithms ◽  
Credit Card Fraud

Today the banking sector offers its clients many different financial services such as ATM cards, Internet banking, Debit card, and Credit card, which allows attracting a large number of new customers. This article proposes an information system for detecting credit card fraud using a machine learning algorithm. Usually, credit cards are used by the customer around the clock, so the bank's server can track all transactions using machine learning algorithms. It must find or predict fraud detection. The dataset contains characteristics for each transaction and fraudulent transactions need to be classified and detected. For these purposes, the work proposes the use of the Random Forest algorithm.

Marketing of Banking Services in India

2017 ◽  
pp. 107-133
Author(s):  
Rakhi Arora
Keyword(s):  
Banking Sector ◽  
Technological Development ◽  
Banking System ◽  
Foreign Banks ◽  
Indian Society ◽  
Reserve Bank ◽  
Nationalised Banks ◽  
The Government ◽  
Indian Banks ◽  
Reserve Bank Of India

Banking sector plays an important role in Indian Financial Sector.It has a long history that has gone through various stages of development after Liberalization, Privatization, and Globalization (LPG) has taken place. The Indian banking sector is broadly classified into scheduled banks and non-scheduled banks. The scheduled banks are those included under the 2nd Schedule of the Reserve Bank of India Act, 1934. The scheduled banks are further classified into: nationalised banks; State Bank of India and its associates; Regional Rural Banks (RRBs); foreign banks; and other Indian private sector banks, which are controlled and governed by Reserve Bank of India (Central Bank of India) and Ministry of Finance. In this era, the government has issued licenses to the new entrants to establish new banks to serve the Indian society. This chapter focuses on to show the various undergone phases of Indian banking system, growth of deposits and credits, technological development in Indian banking sector, services provided by the Indian banks, benefits and challenges faced by the Indian banks.

scholarly journals A Study of Non Performing Advances and It’s Management With Special Reference to Punjab National Bank

2021 ◽  
pp. 507-512
Author(s):  
Dr. Martha Sharma
Keyword(s):  
Banking Sector ◽  
Negative Impact ◽  
Capital Adequacy ◽  
Reserve Bank ◽  
Capital Adequacy Ratio ◽  
National Bank ◽  
Performance Area ◽  
Definition Of ◽  
Reserve Bank Of India

Banking industry plays an important role in the development of an economy. Banks have become very cautious in extending loans. The reason being mounting non-performing assets (NPAs). NPAs put negative impact on the profitability, capital adequacy ratio and credibility of banks. It is defined as a loan asset, which has ceased to generate any income for a bank whether in the form of interest or principal repayment. As per the prudential norms suggested by the Reserve Bank of India (RBI), a bank cannot book interest on an NPA on accrual basis. In other words, such interests can be booked only when it has been actually received. Therefore, this has become what is called as a ‘critical performance area’ of the banking sector as the level of NPAs affects the profitability of a bank. This paper touches upon the meaning and consequently the definition of a non-Performing asset, the conceptual framework of non-performing assets, classification of loan assets and provisions. The study also evaluates the adverse effect of non-performing assets on the return on total assets of Punjab National Bank Limited for the period 2013 to 2015, 2016-17, and 2019-20. Particularly discussing some remedial measures taken up by the Bank to overcome this situation of NPA.

scholarly journals Klasifikasi Penentuan Pengajuan Kartu Kredit Menggunakan K-Nearest Neighbor

2020 ◽  
Vol 22 (1) ◽  
pp. 73-82
Author(s):  
Yogiek Indra Kurniawan ◽  
Tiyssa Indah Barokah
Keyword(s):  
Credit Card ◽  
Nearest Neighbor ◽  
Credit Cards ◽  
K Nearest Neighbor ◽  
The Status ◽  
Using Data

A credit card is a device payment issued by the bank certain made of plastic and useful as a tool payment on credit carried out by the owner of the card or in accordance with the name of listed in a credit card is on when making purchases goods or services. The problems facing in giving a credit cards to customers bank that have signed up is difficult to determine the category of a credit cards in accordance with the customer bank. By doing this research is expected to facilitate the bank or the analysis to determine the category of a credit card to customers bank right. The research used is by applying methods K-Nearest Neighbor to classify prospective customers in the making a credit card in accordance with the category of  customers by using data customers at the Bank BNI Syariah Surabaya. A method K-Nearest Neighbor used to seek patterns on the data customers so established variable as factors supporters in the form of gender, the status of the house, the status, the number of dependants (children), a profession and revenue annually. The results of this research shows that an average of the value of precision of 92%, the value of recall of 83%, and the value of accuracy of 93%. Thus, this application is effective to help analyst credit cards in classifying customers to get credit cards that appropriate criteria.

scholarly journals A GLIMPSE INTO THE LEVEL OF FINANCIAL INCLUSION IN TRIPURA

2020 ◽  
pp. 1-5
Author(s):  
Sayan Saha ◽  
Kiran Shankar Chakraborty
Keyword(s):  
Financial Services ◽  
Composite Index ◽  
Secondary Data ◽  
State Level ◽  
Pilot Project ◽  
Financial Inclusion ◽  
Vulnerable Groups ◽  
Annual Reports ◽  
Banking Services ◽  
Reserve Bank

The term ‘Financial Inclusion’ signifies a process of ensuring delivery of financial services as well as banking services to the vulnerable groups at the point of need, adequately at an affordable cost. The concept of ‘Financial Inclusion’ was accentuated in 2003 by Kofi Annan, former General Secretary of United Nations. Such, efforts were undertaken by the Reserve Bank of India (RBI) in 2005 and the said policy as already mentioned in a pilot project was first implemented by Indian Bank. Probably, by implementing such policy resolution a vast section of the rural disadvantaged people in India was gradually coming under the ambit of formal banking services. The main aim of this paper is to assess the level of financial inclusion in Tripura based on composite Index. The study conducted in the four districts of Tripura state. The present study relies on secondary data. Secondary data collected from State Level Bankers’ Committee Reports, NEDFi databank, Economic Reviews and RBI Annual Reports. Through this paper Index of Financial Inclusion (IFI) has been used to assess the level of financial inclusion in Tripura.

scholarly journals BANKING SECTOR ORIENTED FINANCIAL INCLUSION IN INDIA: A LONG TERM PERSPECTIVE

2020 ◽  
pp. 42-59
Author(s):  
Sana Pathan ◽  
Archana Fulwari
Keyword(s):  
Financial Services ◽  
Banking Sector ◽  
Financial Inclusion ◽  
Inclusive Growth ◽  
Long Run ◽  
Reserve Bank ◽  
The Government ◽  
Improve Access ◽  
Reserve Bank Of India

Financial Inclusion is an emerging concept. The objective of the government behind 100 percent Financial Inclusion is to have inclusive growth in India. Several initiatives have been taken by the Government of India and the Reserve Bank of India to improve access to financial services. To measure the effectiveness of these initiatives there is need to measure the extent of Financial Inclusion. Financial Inclusion can be measured by gauging the progress in access to and usage of a range of products and services of financial institutions over time. The present study sought to propose an index to measure the extent of banking sector oriented Financial Inclusion in India over a period of time rather than a cross-section study which has been the focus of many a studies. The study used more specific indicators of banks-centric financial inclusion dimensions to gauge the long run trend in Financial Inclusion in India. The results indicate that there is much improvement in Financial Inclusion in India since the implementation of financial sector reforms.

India’s banks will struggle to inspire confidence

2020 ◽  
Keyword(s):  
Private Sector ◽  
Banking Sector ◽  
The State ◽  
Regulatory Oversight ◽  
Content Type ◽  
Shadow Banks ◽  
Reserve Bank ◽  
High Level ◽  
Bad Debt ◽  
Reserve Bank Of India

Subject Problems in India's banking sector. Significance The Reserve Bank of India (RBI) earlier this month stepped in to rescue imperilled Yes Bank. The private sector lender had accumulated a high level of bad debt. Impacts Indian borrowers will be increasingly distrustful of shadow banks as well as banks. The State Bank of India could come under strain owing to its need to support Yes Bank financially. The RBI will come under growing pressure to improve its regulatory oversight of the banking sector.

The Devil’s Laugh at Corporate Governance: The Case of Punjab and Maharashtra Co-operative Bank (PMC Bank) India

2021 ◽  
pp. 097468622110473
Author(s):  
Ambuj Gupta
Keyword(s):  
Corporate Governance ◽  
Banking Sector ◽  
Banking System ◽  
Chief Executive ◽  
Banking Supervision ◽  
Loan Portfolio ◽  
Reserve Bank ◽  
Crony Capitalism ◽  
Insolvency Proceedings ◽  
Reserve Bank Of India

The trust of depositors in the Indian banking system was shaken in September 2019 when the five-page confession letter written by Joy K Thomas, Managing Director and Chief Executive Officer of Punjab and Maharashtra Co-operative Bank (PMC Bank), one of the ten largest co-operative banks in India revealed gross financial irregularities, collusion and fraud in banking operations of PMC Bank from 2008 onwards. The Reserve Bank of India (RBI) came into swift action and placed curbs on routine banking activities and restricted the withdrawal of money to a limited amount. Succumbing to the shock, depositors protested at several places and even, eleven depositors lost their lives. With a huge exposure of 73% of the overall loan portfolio to a single borrower, Housing and Development Infrastructure Ltd (HDIL) & group companies, that too facing insolvency proceedings, the recovery of full money was almost impossible. The malice at PMC Bank is the classic case of crony capitalism, collusion and fraud, and failure of corporate governance. The case draws important lessons for reforming co-operative banking sector and strengthening banking supervision in the country.

scholarly journals Predicting Credit Card Approval of Customers Through Customer Profiling using Machine Learning

2020 ◽  
Vol 9 (4) ◽  
pp. 552-557
Keyword(s):  
Machine Learning ◽  
Statistical Models ◽  
Credit Card ◽  
Banking Sector ◽  
Current Trend ◽  
Credit Cards ◽  
Credit Score ◽  
Accuracy Ratio ◽  
Customer Profiling ◽  
Learning Machine

In the banking sector, every banking infrastructure contains an enormous dataset for customers’ credit card approval which requires customer profiling. The customer profiling means collection of data related to what customers need. It depends on customers’ basic information like field of work, address proof, credit score, salary details, etc. This process mainly concentrates on predicting approval of credit cards to customers using machine learning. Machine Learning is the scientific study of algorithms and statistical models that computers use to perform specific tasks without any external instructions or interference. In the current trend this process is possible using many algorithms like “K-Mean, Improved K-Mean and Fuzzy C-Means”. This helps banks to have an high profitability to satisfy their customers. However, the currently prevailing system shows an accuracy percentage of about 98.08%. The proposed system aims at improvising the accuracy ratio while using only few algorithms.

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