scholarly journals BIG DATA ANALYTICS IN BANKING AND FINANCIAL SERVICES SECTOR

2020 ◽  
pp. 92-96
Author(s):  
Dr.P.V. Mohini ◽  
Mr. Rohit Kumar Srivastav
Keyword(s):  
Big Data ◽  
Financial Services ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Big Data Analysis ◽  
Valuable Data ◽  
Services Sector ◽  
Financial Services Sector ◽  
The People ◽  
Potential Benefits

‘He who holds the wealth of information, holds the solution to the toughest of the situations’ this quote is very much apt for the recent ongoing scenario, where there is an invisible struggle going on among the organizations as well as nations in the search for more and more information. Now, when the Banking and Financial services sectors are put under the scanner, it becomes evident that they are sitting on top of a humungous heap of valuable data. This data can be used for the betterment and advancement of the industry as well as the people. While it is good to have large amount of data available, it will be termed a big pile of trash if it is not analyzed properly and the results obtained from it are not put to use. With the adoption of Big Data analytics into the banking and financial services, many obvious as well as concealed problems can be addressed to and even solved quickly. The main objective of this paper is to highlight the meaning of Big Data analysis, study the types of data analytics with respect to Banking and Financial services sector and to showcase the potential benefits of embracing Big Data analytics into the Banking & Financial services sector. KEYWORDS: Information, Banking and Financial services, Advancement, Big Data, Data Analytics

scholarly journals A multilevel approach to big data analysis using analytic tools and actor network theory

2018 ◽  
Vol 20 (1) ◽  
Author(s):  
Tiko Iyamu
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Network Theory ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Actor Network Theory ◽  
Big Data Analysis ◽  
Data Sets ◽  
Multilevel Approach ◽  
Actor Network

Background: Over the years, big data analytics has been statically carried out in a programmed way, which does not allow for translation of data sets from a subjective perspective. This approach affects an understanding of why and how data sets manifest themselves into various forms in the way that they do. This has a negative impact on the accuracy, redundancy and usefulness of data sets, which in turn affects the value of operations and the competitive effectiveness of an organisation. Also, the current single approach lacks a detailed examination of data sets, which big data deserve in order to improve purposefulness and usefulness.Objective: The purpose of this study was to propose a multilevel approach to big data analysis. This includes examining how a sociotechnical theory, the actor network theory (ANT), can be complementarily used with analytic tools for big data analysis.Method: In the study, the qualitative methods were employed from the interpretivist approach perspective.Results: From the findings, a framework that offers big data analytics at two levels, micro- (strategic) and macro- (operational) levels, was developed. Based on the framework, a model was developed, which can be used to guide the analysis of heterogeneous data sets that exist within networks.Conclusion: The multilevel approach ensures a fully detailed analysis, which is intended to increase accuracy, reduce redundancy and put the manipulation and manifestation of data sets into perspectives for improved organisations’ competitiveness.

scholarly journals Peran Otoritas Jasa Keuangan Dalam Perlindungan Konsumen Electronic Banking Pada PT. Bank Rakyat Indonesia (Persero) Tbk. Cabang Sigli

2019 ◽  
Vol 3 (3) ◽  
pp. 459-468
Author(s):  
Nazaruddin Nazaruddin
Keyword(s):  
Dispute Resolution ◽  
Empirical Research ◽  
Financial Services ◽  
Government Regulation ◽  
Services Sector ◽  
Financial Services Sector ◽  
Banking Service ◽  
Article 9 ◽  
The People ◽  
The Subject

Pasal 9 huruf c Undang-Undang Nomor 21 Tahun 2011 tentang Otoritas Jasa Keuangan (selanjutnya disebut UU OJK) menyatakan bahwa untuk melaksanakan tugas pengawasan OJK, mempunyai wewenang melakukan pengawasan, pemeriksaan, penyidikan, perlindungan konsumen, dan tindakan lain terhadap lembaga jasa keuangan, pelaku, dan/atau penunjang kegiatan jasa keuangan, sebagaimana dimaksud dalam peraturan perundang-undangan di sektor jasa keuangan. Pasal 28 huruf a UU OJK menyatakan bahwa untuk perlindungan konsumen dan masyarakat, OJK berwenang melakukan tindakan pencegahan kerugian konsumen dan masyarakat, salah satunya memberikan informasi dan edukasi kepada masyarakat atas karakteristik sektor jasa keuangan, layanan, dan produknya. Namun pada kenyataannya, pelaksanaan edukasi yang dilakukan oleh pihak perbankan tersebut tentu saja berada di bawah pengawasan OJK, sehingga secara tidak langsung OJK pun bertanggung jawab terhadap risiko penggunaan produk e-banking yang dapat merugikan konsumen. Jenis penelitian yang digunakan dalam penelitian ini adalah penelitian yuridis empiris yaitu jenis penelitian yang meneliti dan menelaah efektivitas suatu peraturan perundang-undangan. Hasil penelitian menujukkan Tanggung Jawab OJK terhadap konsumen yang mengalami kerugian akibat penggunaan layanan e-banking  adalah melakukan pendampingan bagi konsumen dan sebagai fasilitator dalam rangka melakukan gugatan ganti kerugian terhadap bank dengan jalan Eksternal Dispute Resolution, baik melalui litigasi maupun non litigasi.The Government Regulation No. 21 of 2011 Article 9 (c) regarding the Financial Services Authority (hereinafter referred to as UU OJK) states that in order to carry out the supervision other task to the financial services instituition the subject and/or the supporting financial services activities, as referred to the regulation about financial services activity. Article 28 (a) of UU OJK also states that in protection of consumers and people, OJK authorized to act in preventing costumer and people loss by providing information as well as education for the people regarding the characteristic of the financial services sector, the services and the products. In fact, however, the execution of the educating process done by the bank is under the supervision of OJK so OJK is indirectly responsible for the risk of e-banking products usage that harm consumers. This type of research used in this research is juridical empirical research that examines the types of research and study the effectiveness of laws. The result of the result indicated that the responsibility of OJK to the consumer who suffered losses by the e-banking service is by providing assistance and act as a facilitator in pursuing a lawsuit to get compensation from the bank by external dispute resolution, both by litigation and non-litigation.

Big Data Analytics Tools and Platform in Big Data Landscape

2018 ◽  
pp. 80-89
Author(s):  
Mohd Imran ◽  
Mohd Vasim Ahamad ◽  
Misbahul Haque ◽  
Mohd Shoaib
Keyword(s):  
Big Data ◽  
Comparative Analysis ◽  
Comparative Study ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Big Data Analysis ◽  
Apache Hadoop ◽  
Pros And Cons ◽  
The Comparative Study ◽  
Analytical Tools

The term big data analytics refers to mining and analyzing of the voluminous amount of data in big data by using various tools and platforms. Some of the popular tools are Apache Hadoop, Apache Spark, HBase, Storm, Grid Gain, HPCC, Casandra, Pig, Hive, and No SQL, etc. These tools are used depending on the parameter taken for big data analysis. So, we need a comparative analysis of such analytical tools to choose best and simpler way of analysis to gain more optimal throughput and efficient mining. This chapter contributes to a comparative study of big data analytics tools based on different aspects such as their functionality, pros, and cons based on characteristics that can be used to determine the best and most efficient among them. Through the comparative study, people are capable of using such tools in a more efficient way.

Data Science and Big Data Analytics in Financial Services

Web Services ◽  
2019 ◽  
pp. 1301-1329
Author(s):  
Suren Behari ◽  
Aileen Cater-Steel ◽  
Jeffrey Soar
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Financial Services ◽  
Data Science ◽  
Big Data Analytics ◽  
Disruptive Innovation ◽  
Big Data Analysis ◽  
Future Trends ◽  
The Right

The chapter discusses how Financial Services organizations can take advantage of Big Data analysis for disruptive innovation through examination of a case study in the financial services industry. Popular tools for Big Data Analysis are discussed and the challenges of big data are explored as well as how these challenges can be met. The work of Hayes-Roth in Valued Information at the Right Time (VIRT) and how it applies to the case study is examined. Boyd's model of Observe, Orient, Decide, and Act (OODA) is explained in relation to disruptive innovation in financial services. Future trends in big data analysis in the financial services domain are explored.

Big Data Analytics Tools and Platform in Big Data Landscape

2022 ◽  
pp. 622-631
Author(s):  
Mohd Imran ◽  
Mohd Vasim Ahamad ◽  
Misbahul Haque ◽  
Mohd Shoaib
Keyword(s):  
Big Data ◽  
Comparative Analysis ◽  
Comparative Study ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Big Data Analysis ◽  
Apache Hadoop ◽  
Pros And Cons ◽  
The Comparative Study ◽  
Analytical Tools

The term big data analytics refers to mining and analyzing of the voluminous amount of data in big data by using various tools and platforms. Some of the popular tools are Apache Hadoop, Apache Spark, HBase, Storm, Grid Gain, HPCC, Casandra, Pig, Hive, and No SQL, etc. These tools are used depending on the parameter taken for big data analysis. So, we need a comparative analysis of such analytical tools to choose best and simpler way of analysis to gain more optimal throughput and efficient mining. This chapter contributes to a comparative study of big data analytics tools based on different aspects such as their functionality, pros, and cons based on characteristics that can be used to determine the best and most efficient among them. Through the comparative study, people are capable of using such tools in a more efficient way.

Business Analytics and Big Data

2015 ◽  
pp. 1-11 ◽  
Author(s):  
Dennis T. Kennedy ◽  
Dennis M. Crossen ◽  
Kathryn A. Szabat
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Quantitative Methods ◽  
Business Processes ◽  
Predictive Analytics ◽  
Big Data Analytics ◽  
Database Systems ◽  
Three Dimensions ◽  
Business Analytics ◽  
The People

Big Data Analytics has changed the way organizations make decisions, manage business processes, and create new products and services. Business analytics is the use of data, information technology, statistical analysis, and quantitative methods and models to support organizational decision making and problem solving. The main categories of business analytics are descriptive analytics, predictive analytics, and prescriptive analytics. Big Data is data that exceeds the processing capacity of conventional database systems and is typically defined by three dimensions known as the Three V's: Volume, Variety, and Velocity. Big Data brings big challenges. Big Data not only has influenced the analytics that are utilized but also has affected technologies and the people who use them. At the same time Big Data brings challenges, it presents opportunities. Those who embrace Big Data and effective Big Data Analytics as a business imperative can gain competitive advantage.

scholarly journals Hidden big data analytics issues in the healthcare industry

2019 ◽  
Vol 26 (2) ◽  
pp. 981-998 ◽  
Author(s):  
Kenneth David Strang ◽  
Zhaohao Sun
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Data Analytics ◽  
Research Method ◽  
Big Data Analytics ◽  
Big Data Analysis ◽  
Healthcare Industry ◽  
Statistical Control ◽  
Subject Matter Experts ◽  
Journal Articles

The goal of the study was to identify big data analysis issues that can impact empirical research in the healthcare industry. To accomplish that the author analyzed big data related keywords from a literature review of peer reviewed journal articles published since 2011. Topics, methods and techniques were summarized along with strengths and weaknesses. A panel of subject matter experts was interviewed to validate the intermediate results and synthesize the key problems that would likely impact researchers conducting quantitative big data analysis in healthcare studies. The systems thinking action research method was applied to identify and describe the hidden issues. The findings were similar to the extant literature but three hidden fatal issues were detected. Methodical and statistical control solutions were proposed to overcome the three fatal healthcare big data analysis issues.

Research of Big Data Processing Platform

2014 ◽  
Vol 484-485 ◽  
pp. 922-926
Author(s):  
Xiang Ju Liu
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Data Analysis ◽  
Network Architecture ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Big Data Analysis ◽  
Computing Platform ◽  
Operational Characteristics ◽  
Analysis Platform

This paper introduces the operational characteristics of the era of big data and the current era of big data challenges, and exhaustive research and design of big data analytics platform based on cloud computing, including big data analytics platform architecture system, big data analytics platform software architecture , big data analytics platform network architecture big data analysis platform unified program features and so on. The paper also analyzes the cloud computing platform for big data analysis program unified competitive advantage and development of business telecom operators play a certain role in the future.

Sign in / Sign up

Export Citation Format

Share Document