SAARJ Journal on Banking & Insurance Research最新文献_第7页

Managing difficult employees creatively: common problems uncommon solutions 创造性地管理难相处的员工:常见的问题不常见的解决方案

SAARJ Journal on Banking & Insurance Research Pub Date : 1900-01-01 DOI: 10.5958/2319-1422.2016.00001.1

B. S. Hothi, Harjit Singh

引用次数: 0

Impact of frauds on the performance of indian public sector banks: An empirical analysis 欺诈对印度公共部门银行绩效的影响:实证分析

SAARJ Journal on Banking & Insurance Research Pub Date : 1900-01-01 DOI: 10.5958/2319-1422.2017.00014.5

Dhanraj Sharma, Ruchita Verma

{"title":"Impact of frauds on the performance of indian public sector banks: An empirical analysis","authors":"Dhanraj Sharma, Ruchita Verma","doi":"10.5958/2319-1422.2017.00014.5","DOIUrl":"https://doi.org/10.5958/2319-1422.2017.00014.5","url":null,"abstract":"Banks is a pivot around which the whole economy clusters and plays a significant role in the development of an economy. Fraud is a dimension of corruption which has been enrooted in almost all economies of the world and has affected financial sector as whole and banking sector is not an exception to this. Considering the treacherous effect of the fraud on the banking sector, the present study is aims to analyze the impact of the fraud on the performance of the Indian public sector banks. Here the performance is taken in terms of Return on Assets (ROA), Return on Equity (ROE) and Return on Investment (ROI) which are serving as dependent variable. On the other hand frauds are considered as Severity of Frauds (SOF) and Frequency of Frauds (FOF) which are serving as independent variable. The data of 11 years i.e. (2005–2015) is taken into account which is collected from the India Stat. The base year for the purpose of evaluation is taken as 2005, as this is the year in which a master circular was issued by the RBI containing guidelines/instructions to the bank on the procedure to be followed in dealing with forged notes detected at the counters of banks’ branches. Accordingly the following 26 public sector banks fall under the scope of the study. For the purpose of analysis, fixed and random effect model of panel data technique is used. The collected data is analyzed with help of statistical software E-Views. The p-value of F-Test is.000 which is less than.05 accordingly we reject the null hypothesis that the frequency and severity of frauds in the public sector banks have no significant impact on their Return on Assets (ROA), Return on Equity (ROE) and Return on Investment (ROI).. Alternatively there is significant impact of frequency and severity of frauds on ROA, ROE and ROI in the Indian public sector banks.","PeriodicalId":436614,"journal":{"name":"SAARJ Journal on Banking & Insurance Research","volume":"37 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114006455","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Investment pattern and awareness of mutual funds among government employees vis-à-vis private employees 政府雇员相对于-à-vis私营雇员的共同基金投资模式及意识

SAARJ Journal on Banking & Insurance Research Pub Date : 1900-01-01 DOI: 10.5958/2319-1422.2016.00016.3

Swarnalata R. Yajmanya

引用次数: 0

Monetary mobilization of commercial bank with special reference to Eastern region of India 商业银行的货币动员——以印度东部地区为例

SAARJ Journal on Banking & Insurance Research Pub Date : 1900-01-01 DOI: 10.5958/2319-1422.2017.00022.4

S. Ganapathy, Thangam Alagarsamy, M. Raguraman

引用次数: 0

A study on Indian insurance in the Global Scenario 全球情境下的印度保险研究

SAARJ Journal on Banking & Insurance Research Pub Date : 1900-01-01 DOI: 10.5958/2319-1422.2017.00029.7

B. Parashuramulu, C. Raju

引用次数: 0

Prediction of credit risks in lending bank loans using machine learning 利用机器学习预测银行贷款中的信用风险

SAARJ Journal on Banking & Insurance Research Pub Date : 1900-01-01 DOI: 10.5958/2319-1422.2019.00003.1

Mohit Lakhani, Bhavesh Dhotre, Saurabh Giri

{"title":"Prediction of credit risks in lending bank loans using machine learning","authors":"Mohit Lakhani, Bhavesh Dhotre, Saurabh Giri","doi":"10.5958/2319-1422.2019.00003.1","DOIUrl":"https://doi.org/10.5958/2319-1422.2019.00003.1","url":null,"abstract":"1,2,3Student, Dept. of IT Engineering, NMIMS College, Maharashtra, India ---------------------------------------------------------------------***--------------------------------------------------------------------Abstract Looking at the current scenario there are huge risks involved for Banks to provide Loans. So as to reduce their capital loss; banks should assess and analyse credibility of the individual before sanctioning loan. In the absence of this process there are many chances that this loan may turn in to bad loan in near future. Due to the advanced technology associated with Data mining, data availability and computing power, most banks are renewing their business models and switching to Machine Learning methodology. Credit risk prediction is key to decision-making and transparency. In this review paper, we have discussed classifiers based on Machine and deep learning models on real data in predicting loan default probability. The most important features from various models are selected and then used in the modelling process to test the stability of binary classifiers by comparing their performance on separate data.","PeriodicalId":436614,"journal":{"name":"SAARJ Journal on Banking & Insurance Research","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117030793","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Insurance market analysis methods: case-study from uzbekistan 保险市场分析方法:以乌兹别克斯坦为例

SAARJ Journal on Banking & Insurance Research Pub Date : 1900-01-01 DOI: 10.5958/2319-1422.2018.00004.8

Abdurakhmanov Ilyas

引用次数: 0

How covid 19 pandemic affecting loan emi repayment- A study on Indian Banks covid - 19疫情如何影响贷款还款——对印度银行的研究

SAARJ Journal on Banking & Insurance Research Pub Date : 1900-01-01 DOI: 10.5958/2319-1422.2021.00023.0

K. K. Tiwari, Rashmi Somani

引用次数: 1

A study on customer's perception towards debit card users in public and Private sector Banks at Dharmapuri District 达摩普里区公共和私营银行客户对借记卡用户看法的研究

SAARJ Journal on Banking & Insurance Research Pub Date : 1900-01-01 DOI: 10.5958/2319-1422.2017.00026.1

S. Krishnan, C. Kumar

引用次数: 0

Financial emergency due to demonetisation-A case study of Hyderabad town 废钞导致的财政危机——以海得拉巴镇为例

SAARJ Journal on Banking & Insurance Research Pub Date : 1900-01-01 DOI: 10.5958/2319-1422.2017.00019.4

Prabhakar Pudari

{"title":"Financial emergency due to demonetisation-A case study of Hyderabad town","authors":"Prabhakar Pudari","doi":"10.5958/2319-1422.2017.00019.4","DOIUrl":"https://doi.org/10.5958/2319-1422.2017.00019.4","url":null,"abstract":"The Government of India announced that the Rs 500 and Rs. 1000 denominated currency notes will cease to be legal tender. The present demonetization of currency decision has taken after a long period of 38 years by the Government of India on November 2016. Earlier demonetization was implemented in 1978 by withdrawing Rs 1000, Rs 5000, and Rs 10, 000 notes that were in circulation. The move was targeted towards tackling black money, corruption and terrorism. Through this study an attempt is made to analyze the operational measures announced by the Government of India to know the pros and cons of demonetization and to know the views of various categories of people on demonetization of high denominational notes in India. The long, anxious, and frustrating wait by people outside banks and ATMs across the country since the morning of 9th November, 2016 is an inevitable consequence of the decision to demonetize notes of Rs.500 and Rs.1000. When 86% of the value of notes in circulation turns suddenly invalid, as it did with Prime Minister Shree Narendra Modi's “Surgical Strike announced on 8th November, 2016, a certain degree of disruption and pain is unavoidable. But the question is whether this chaos could have been anticipated and managed better than it has been. Replacement of the demonetized notes is a time consuming exercise that requires planning of the highest order. The experience of the last few months’ shows that preparation was lacking and the transition could have been handled much better.","PeriodicalId":436614,"journal":{"name":"SAARJ Journal on Banking & Insurance Research","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126748796","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0