Monitoring banking system connectedness with big data

2019 ◽  
Vol 212 (1) ◽  
pp. 203-220 ◽  
Author(s):  
Galina Hale ◽  
Jose A. Lopez
Keyword(s):  
Big Data ◽  
Proceedings ◽  
2021 ◽  
Vol 74 (1) ◽  
pp. 24
Author(s):  
Eduard Alexandru Stoica ◽  
Daria Maria Sitea

Nowadays society is profoundly changed by technology, velocity and productivity. While individuals are not yet prepared for holographic connection with banks or financial institutions, other innovative technologies have been adopted. Lately, a new world has been launched, personalized and adapted to reality. It has emerged and started to govern almost all daily activities due to the five key elements that are foundations of the technology: machine to machine (M2M), internet of things (IoT), big data, machine learning and artificial intelligence (AI). Competitive innovations are now on the market, helping with the connection between investors and borrowers—notably crowdfunding and peer-to-peer lending. Blockchain technology is now enjoying great popularity. Thus, a great part of the focus of this research paper is on Elrond. The outcomes highlight the relevance of technology in digital finance.


Author(s):  
Alireza Bolhari

Competency matters. Social media, customer transactions, mobile sensors, and feedback contents are all piled up with data. This might be unstructured and complex data in voluminous quantity, often called Big Data. However, if this Big Data is managed, it might bring competency for organizations. This chapter introduces the must-know concepts and materials for organizational managers who face Big Data. Through the chapter, Big Data is defined and its emergence over the time is reviewed. The four Vs model in Big Data literature and its link to a banking system is analyzed. The chapter concludes by making a managerial awareness concerning ethical issues in Big Data. This is of high priority in public sectors as data relies for every individual in the society.


Author(s):  
Girija Attigeri ◽  
Manohara Pai M M ◽  
Radhika M Pai

Growth and development of the economy is dependent on the banking system. Bad loans which are Non-Performing Assets (NPA) are the measure for assessing the financial health of the bank. It is very important to control NPA as it affects the profitability, and deteriorates the quality of assets of the bank. It is observed that there is a significant rise in the number of willful defaulters. Hence systematic identification, awareness and assessment of parameters is essential for early prediction of willful default behavior. The main objective of the paper is to identify exhaustive list of parameters essential for predicting whether the loan will become NPA and thereby willful default. This process includes understanding of existing system to check NPAs and identifying the critical parameters. Also propose a framework for NPA/Willful default identification. The framework classifies the data comprising of structured and unstructured parameters as NPA/Willful default or not. In order to select the best classification model in the framework an experimentation is conducted on loan dataset on big data platform. Since the loan data is structured, unstructured component is incorporated by generating synthetic data. The results indicate that neural network model gives best accuracy and hence considered in the framework.


Web Services ◽  
2019 ◽  
pp. 2060-2074
Author(s):  
Alireza Bolhari

Competency matters. Social media, customer transactions, mobile sensors, and feedback contents are all piled up with data. This might be unstructured and complex data in voluminous quantity, often called Big Data. However, if this Big Data is managed, it might bring competency for organizations. This chapter introduces the must-know concepts and materials for organizational managers who face Big Data. Through the chapter, Big Data is defined and its emergence over the time is reviewed. The four Vs model in Big Data literature and its link to a banking system is analyzed. The chapter concludes by making a managerial awareness concerning ethical issues in Big Data. This is of high priority in public sectors as data relies for every individual in the society.


2019 ◽  
Vol 2 (1) ◽  
pp. 2
Author(s):  
Panagiotis Roumeliotis

The strong competition and digital revolution impose to the banks their profound transformation demanding a high cost on investments. The banking system should continue, at the same time, its efforts to disengage from non-productive assets as well as to reduce the running costs. Moreover, banks should consider becoming more flexible and productive, by taking advantage of the opportunities of the new digital economy and by ameliorating accordingly the offered services. The current paper discusses and analyses the challenges for the banks, regarding the technological evolution, the digitization of banking services and the technologies of blockchain and big data.


2018 ◽  
pp. 01-41 ◽  
Author(s):  
Galina Hale ◽  
◽  
Jose A. Lopez ◽  

ASHA Leader ◽  
2013 ◽  
Vol 18 (2) ◽  
pp. 59-59
Keyword(s):  

Find Out About 'Big Data' to Track Outcomes


2014 ◽  
Vol 35 (3) ◽  
pp. 158-165 ◽  
Author(s):  
Christian Montag ◽  
Konrad Błaszkiewicz ◽  
Bernd Lachmann ◽  
Ionut Andone ◽  
Rayna Sariyska ◽  
...  

In the present study we link self-report-data on personality to behavior recorded on the mobile phone. This new approach from Psychoinformatics collects data from humans in everyday life. It demonstrates the fruitful collaboration between psychology and computer science, combining Big Data with psychological variables. Given the large number of variables, which can be tracked on a smartphone, the present study focuses on the traditional features of mobile phones – namely incoming and outgoing calls and SMS. We observed N = 49 participants with respect to the telephone/SMS usage via our custom developed mobile phone app for 5 weeks. Extraversion was positively associated with nearly all related telephone call variables. In particular, Extraverts directly reach out to their social network via voice calls.


2017 ◽  
Vol 225 (3) ◽  
pp. 287-288
Keyword(s):  

An associated conference will take place at ZPID – Leibniz Institute for Psychology Information in Trier, Germany, on June 7–9, 2018. For further details, see: http://bigdata2018.leibniz-psychology.org


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