Research on Financial Technology Ability Evaluation of Global Systemically Important Banks Based on Data Mining

2021 ◽  
Author(s):  
Kexin Yu
2018 ◽  
Vol 4 (2) ◽  
pp. 159-80
Author(s):  
Reijer Hendrikse ◽  
David Bassens ◽  
Michiel Van Meeteren

The rise of financial technology (FinTech) engenders novel business models through integrating financial services and information and communication technologies (ICT). Digital currencies and payments, data mining, and other FinTech applications threaten to radically overhaul the financial sector. This article argues that, while we are becoming aware of how technology giants such as Apple Inc. are making inroads into financial services, we need to become more sensitive to how financial incumbents mimick ICT firms while aiming to neutralize the FinTech challenge. Practices from Silicon Valley are spilling over into ‘traditional’ finance through a process we dub Appleization. We illustrate how incumbents aim to remain indispensable amidst rapid digitization. Mimicking tech strategies, financial incumbents resort to transforming legacy ICT systems into integrated platforms, cultivating entrepreneurial ecosystems where startups are ‘free’ to compete whilst effectively being locked into the incumbent's orbit. We illustrate this by comparing Apple’s business features (locking-in developers, customers and state into a hybrid business model based on a synergy between hardware, software and data-driven platform components) with emerging practices in the financial industry. Our analogy suggests that the Appleization of finance might radically transform, yet not undercut the oligopolistic position of financial incumbents.


Author(s):  
Prasdika Prasdika ◽  
Bambang Sugiantoro

In the digital era like today the growth of data in the database is very rapid, all things related to technology have a large contribution to data growth as well as social media, financial technology and scientific data. Therefore, topics such as big data and data mining are topics that are often discussed. Data mining is a method of extracting information through from big data to produce an information pattern or data anomaly 


2020 ◽  
Author(s):  
Mohammed J. Zaki ◽  
Wagner Meira, Jr
Keyword(s):  

2010 ◽  
Vol 24 (2) ◽  
pp. 112-119 ◽  
Author(s):  
F. Riganello ◽  
A. Candelieri ◽  
M. Quintieri ◽  
G. Dolce

The purpose of the study was to identify significant changes in heart rate variability (an emerging descriptor of emotional conditions; HRV) concomitant to complex auditory stimuli with emotional value (music). In healthy controls, traumatic brain injured (TBI) patients, and subjects in the vegetative state (VS) the heart beat was continuously recorded while the subjects were passively listening to each of four music samples of different authorship. The heart rate (parametric and nonparametric) frequency spectra were computed and the spectra descriptors were processed by data-mining procedures. Data-mining sorted the nu_lf (normalized parameter unit of the spectrum low frequency range) as the significant descriptor by which the healthy controls, TBI patients, and VS subjects’ HRV responses to music could be clustered in classes matching those defined by the controls and TBI patients’ subjective reports. These findings promote the potential for HRV to reflect complex emotional stimuli and suggest that residual emotional reactions continue to occur in VS. HRV descriptors and data-mining appear applicable in brain function research in the absence of consciousness.


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