3C TIC. Cuadernos de desarrollo aplicados a las TIC. ISSN: 2254-6529
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Considering the inux of FinTech industry invoices upon the enlargement of
Islamic banking as an element of the nancial system, economic and mathematical
modeling was carried out. The indicators to be modeled are presented in Table 2.
Table 2. Input data for modulation.
Years
Islamic
Banking
Assets
(Billion USD)
Total
investment
activity in
FinTech
(Capital
invested
($B))
Total
investment
activity in
FinTech
(Deal count)
Global
venture
capital
activity in
FinTech with
corporate
participation
(Capital
invested
($B))
Global
venture
capital
activity in
FinTech with
corporate
participation
(Deal count)
Global private
investment in
blockchain &
cryptocurrency
(Capital
invested ($B))
Global private
investment in
blockchain &
cryptocurrency
(Deal count)
Y Х1 Х2 Х3 Х4 Х5 X6
2013 1565 18,9 1,132 0,8 102 0,2 69
2014 1445 45,4 1,543 2,5 137 0,7 144
2015 1604 67,1 1,925 8,5 225 0,5 134
2016 1675 63,4 1,893 11,6 287 0,6 159
2017 1721 50,8 2,165 10,3 327 4,8 218
2018* 1809 111,8 2,196 23,1 357 4,5 494
Source: (Pollari & Ruddenklau, 2018).
Initially, a correlation matrix is created to specify the grade of penetration of the
selected X on the studied Y (Table 3).
Table 3. Correlation matrix.
Y Х1 Х2 Х3 Х4 Х5 X6
Y 1
Х1 0,704093 1
Х2 0,755271 0,793208 1
Х3 0,886938 0,946588 0,827307 1
Х4 0,902747 0,786041 0,949344 0,900661 1
Х5 0,758852 0,546898 0,740252 0,696453 0,787007 1
X6 0,761314 0,893595 0,695939 0,924876 0,758369 0,774287 1
The correlation matrix shows the level of inuence of the selected factors on
the object of study. According to it, the factors X1-X6 have the values of the
correlation coecients greater than 0.5 and are accepted for the study, however,
among them, there is a multi-linearity above 0.7. So, by correlation, multi-