Regulatory technologies in traditional industries as an element of the digital transformation: perspectives for development
Study of digital business transformation. Improving the efficiency of administrative management. The main objectives of the application of regulatory technologies. Assessing the risks of data leaks, finding ways to reduce financial losses from fraud.
Рубрика | Экономика и экономическая теория |
Вид | статья |
Язык | английский |
Дата добавления | 03.05.2023 |
Размер файла | 330,6 K |
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Аннотация
Регуляторные технологии в традиционных отраслях как элемент цифровой трансформации: перспективы развития
А. И. Алтынов, Ю. В. Туровец
Финансовый сектор является флагманом цифровизации, демонстрируя возможности внедрения цифровых технологий другим отраслям. Регуляторные технологии стали одним из ключевых направлений развития финансового сектора в связи с постоянным ростом мошенничества, а также увеличением объема и сложности процедур по установлению соответствия законодательным требованиям.
Ввиду схожести проблем к новым решениям проявляют интерес и компании традиционных отраслей. Регуляторные технологии становятся сквозным элементом цифровизации, позволяющим эффективно управлять растущей административной нагрузкой, снижать возникающие риски. Внедрение регуляторных решений может служить косвенным индикатором эффектов цифровой трансформации бизнеса, среди которых -- рост регуляторной нагрузки.
В работе выявлены основные задачи применения регуляторных технологий, актуальные для широкого круга традиционных отраслей экономики (сектора телекоммуникаций, торговли, обрабатывающей промышленности, транспорта, строительства, энергетики и ЖКХ, здравоохранения, государственного управления).
С учетом полученных результатов проведена оценка перспектив развития регуляторных технологий в указанных отраслях экономики на основе предложенного композитного индекса, который включает девять индикаторов, сгруппированных по четырем блокам, измеряющим финансовые потери от мошенничества и утечек данных, административную нагрузку, связанную с соответствием законодательным требованиям, а также уровень использования цифровых технологий. Результаты анализа показывают, что наибольший потенциал применения регуляторных технологий, помимо сектора услуг (финансы, телекоммуникации, торговля), сосредоточен в обрабатывающей промышленности и здравоохранении в связи с высокой частотой мошенничества, утечкой данных, растущими издержками и новыми регуляторными требованиями на фоне все более масштабного использования цифровых решений.
Ключевые слова: регуляторные технологии, финансовые услуги, цифровая трансформация, традиционные сектора, композитный индекс.
Appendix 1
Selection of the leading regulators and banks for the analysis
Table 1. Selection of the leading countries in FinTech
Country |
The global Fintech Index 2020 (Rank) |
The global Financial Centres Index 29 (Rank) |
KPMG Fintech100 2019 (Rank) |
Average |
Rank |
|
USA |
1 |
1 |
1 |
1 |
1 |
|
UK |
2 |
2 |
2 |
2 |
2 |
|
Singapore |
3 |
5 |
7 |
5 |
3 |
|
Germany |
11 |
9 |
6 |
8.7 |
4 |
|
Switzerland |
5 |
10 |
11 |
8.7 |
5 |
|
China |
21 |
3 |
3 |
9 |
6 |
|
Canada |
9 |
11 |
9 |
9.7 |
7 |
|
Australia |
8 |
18 |
5 |
10.3 |
8 |
|
Japan |
22 |
7 |
7 |
12 |
9 |
|
South Korea |
18 |
16 |
9 |
14.3 |
10 |
|
Netherlands |
6 |
28 |
11 |
15 |
11 |
|
France |
16 |
25 |
7 |
16 |
12 |
|
Sweden |
7 |
31 |
10 |
16 |
13 |
|
Ireland |
17 |
21 |
11 |
16.3 |
14 |
|
Luxembourg |
23 |
17 |
10 |
16.7 |
15 |
|
Spain |
13 |
33 |
8 |
18 |
16 |
|
Israel |
12 |
41 |
8 |
20.3 |
17 |
|
Italy |
24 |
26 |
11 |
20.3 |
18 |
|
Denmark |
20 |
34 |
11 |
21.7 |
19 |
|
Belgium |
26 |
36 |
9 |
23.7 |
20 |
|
Lithuania |
4 |
62 |
10 |
25.3 |
21 |
|
Finland |
14 |
56 |
10 |
26.7 |
22 |
|
India |
15 |
65 |
4 |
28 |
23 |
|
Austria |
25 |
54 |
11 |
30 |
24 |
|
Brazil |
19 |
77 |
7 |
34.3 |
25 |
Based on: [Fintech100..., 2019; The Global Fintech Index..., 2019; The Global Financial..., 2021].
Table 2. Selecting financial organizations for analysis
Bank |
Assets (Billions of US dollars), December, 2020 |
Normalized* |
ROA (%), September, 2020 |
Normalized |
Market cap. (Billions of US dollars), July, 2019 |
Normalized |
ROE (%), September, 2020 |
Normalized |
Average |
|
1 |
2 |
3 |
4 |
5 |
6 |
7 |
8 |
9 |
10 |
|
Industrial and Commercial Bank of China |
4913 |
1.00 |
0.77 |
0.85 |
295.65 |
0.79 |
9.04 |
0.87 |
0.88 |
|
JP Morgan Chase |
3386 |
0.56 |
0.77 |
0.85 |
368.78 |
1.00 |
9.00 |
0.86 |
0.82 |
|
China Construction Bank |
3652 |
0.63 |
0.91 |
1.00 |
207.98 |
0.54 |
10.45 |
1.00 |
0.79 |
|
Agricultural Bank of China |
4008 |
0.74 |
0.69 |
0.76 |
181.49 |
0.47 |
8.80 |
0.84 |
0.70 |
|
Bank of America |
2820 |
0.39 |
0.68 |
0.75 |
279.73 |
0.75 |
6.77 |
0.65 |
0.63 |
|
Bank of China |
3627 |
0.63 |
0.60 |
0.66 |
151.15 |
0.38 |
6.96 |
0.67 |
0.58 |
|
Mitsubishi UFJ Financial Group |
3354 |
0.55 |
0.37 |
0.41 |
66.20 |
0.14 |
6.89 |
0.66 |
0.44 |
|
Citigroup |
2234 |
0.22 |
0.51 |
0.56 |
163.58 |
0.42 |
5.64 |
0.54 |
0.44 |
|
BNP Paribas |
2946 |
0.43 |
0.29 |
0.32 |
59.36 |
0.12 |
6.38 |
0.61 |
0.37 |
|
Credit Agricole |
2386 |
0.27 |
0.32 |
0.35 |
34.89 |
0.05 |
8.05 |
0.77 |
0.36 |
|
HSBC Holdings |
2956 |
0.43 |
0.21 |
0.23 |
169.47 |
0.44 |
3.09 |
0.30 |
0.35 |
|
Sumitomo Mitsui Financial Group |
2139 |
0.20 |
0.37 |
0.41 |
49.85 |
0.10 |
6.78 |
0.65 |
0.34 |
|
Mizuho Financial Group |
2094 |
0.18 |
0.27 |
0.30 |
36.95 |
0.06 |
6.17 |
0.59 |
0.28 |
|
Banco Santander |
1829 |
0.11 |
0.34 |
0.37 |
75.47 |
0.17 |
4.99 |
0.48 |
0.28 |
|
Wells Fargo |
1922 |
0.13 |
0.08 |
0.09 |
214.34 |
0.56 |
0.88 |
0.08 |
0.22 |
|
Barclays |
1846 |
0.11 |
0.21 |
0.23 |
33.26 |
0.05 |
4.08 |
0.39 |
0.20 |
|
Japan Post Bank |
2140 |
0.20 |
0.13 |
0.14 |
39.91 |
0.07 |
2.68 |
0.26 |
0.17 |
|
Societe Generale |
1632 |
0.05 |
0.10 |
0.11 |
21.27 |
0.02 |
2.13 |
0.20 |
0.09 |
|
Deutsche Bank |
1627 |
0.05 |
n/a |
n/a |
15.77 |
n/a |
n/a |
n/a |
0.01 |
|
Groupe BPCE |
1463 |
n/a |
n/a |
n/a |
n/a |
n/a |
n/a |
n/a |
n/a |
Note:* -- all values are normalized within the mini-max formula. Based on: [The World's largest banks..., 2020; Macrotrends, 2021].
Appendix 2
Approach for measuring regtech relevance in the sectors
Table 1. The Index of RegTech relevance in the selected sectors (abs. val.)
Sector |
Losses from fraud |
Losses from data leakages |
Compliance burden |
Digital technologies adoption |
||||||
Index 1 |
Index 2 |
Index 3 |
Index 4 |
Index 5 |
Index 6 |
Index 7 |
Index 8 |
Index 9 |
||
ACFE 2020 Global Study, number of fraud cases |
ACFE 2020 Global Study Median loss, thousands of US dollars |
Data leaks by industry, %(2019) |
Average total cost of a data breach, millions of US dollars |
Cost of GDPR Compliance, millions of US dollars (2018) |
Estimated inefficiencies associated with reduced access to data in US, billions of US dollars |
Dell Digital Maturity Index (2018) |
Average security automation deployment status, % of companies with fully deployed |
OECD Taxonomy of sectors by digital intensity |
||
Telecommunications3 |
133b |
200c |
18.30 |
5.04d |
40.3 |
5.00 |
50 |
28.00e |
4.0 |
|
Finance |
386 |
100 |
8.80 |
5.85 |
30.9 |
4.00 |
48 |
27.00 |
4.0 |
|
Healthcare |
149 |
200 |
18.30 |
7.13 |
19 |
5.00 |
45 |
23.00 |
2.0 |
|
Trade |
116 |
108 |
7.10 |
4.60 |
29.1 |
10.50 |
46 |
27.00 |
3.0 |
|
Manufacturing |
211 |
337 |
6.40 |
4.99 |
29.4 |
5.00 |
45 |
20.00 |
2.5 |
|
Energy and utilities |
111 |
219 |
8.80 |
6.39 |
24.8 |
7.00 |
46 |
20.00 |
1.0 |
|
Transport |
65 |
150 |
6.40 |
3.58 |
24.3 |
14.50 |
48 |
18.00 |
1.0 |
|
Construction |
80 |
200 |
8.80 |
3.86 |
19.07 |
5.00 |
47 |
21.00 |
1.0 |
|
Public administration |
195 |
100 |
14.90 |
1.08 |
14.5 |
5.00 |
41 |
17.00 |
3.0 |
|
Notes: a -- “Telecommunications” sector cover technology and telecommunications companies due to differences in data sources; b -- for the telecommunication value (133) we sum the values for technology (66) and telecommunications (67) from [Report to the Nations..., 2020];c -- average of technology value (150) and telecommunications value (250) from [Report to the Nations..., 2020]; d -- Value for “Technology” sector is taken from [Cost of a Data Breach Report, 2020];e -- Value for “Technology” sector is taken from [Cost of a Data Breach Report, 2020]. Based on: [Digital Transformation Index..., 2018; Data leaks..., 2019; Measuring the Digital Transformation..., 2019; The Costs of an Unnecessarily..., 2019; Report to the Nations..., 2020; Cost of a Data Breach Report, 2020; Chander et al., 2021]. |
Table 2. The Index of RegTech relevance in the selected sectors (normalized values)
Sector |
Losses from fraud |
Losses from data leakages |
Compliance burden |
Digital technologies adoption |
Overall index |
||||||
Index 1 |
Index 2 |
Index 3 |
Index 4 |
Index 5 |
Index 6 |
Index 7 |
Index 8 |
Index 9 |
|||
Telecommunications |
0.21 |
0.42 |
1.00 |
0.65 |
1.00 |
0.10 |
1.00 |
1.00 |
1.00 |
6.38 |
|
Finance |
1.00 |
n/a |
0.20 |
0.79 |
0.64 |
n/a |
0.78 |
0.91 |
1.00 |
5.31 |
|
Healthcare |
0.26 |
0.42 |
1.00 |
1.00 |
0.17 |
0.10 |
0.44 |
0.55 |
0.33 |
4.28 |
|
Trade |
0.16 |
0.03 |
0.06 |
0.58 |
0.57 |
0.62 |
0.56 |
0.91 |
0.67 |
4.15 |
|
Manufacturing |
0.45 |
1.00 |
n/a |
0.65 |
0.58 |
0.10 |
0.44 |
0.27 |
0.50 |
3.99 |
|
Energy and utilities |
0.14 |
0.50 |
0.20 |
0.88 |
0.40 |
0.29 |
0.56 |
0.27 |
n/a |
3.24 |
|
Transport |
n/a |
0.21 |
n/a |
0.41 |
0.38 |
1.00 |
0.78 |
0.09 |
n/a |
2.87 |
|
Construction |
0.05 |
0.42 |
0.20 |
0.46 |
0.18 |
0.10 |
0.67 |
0.36 |
n/a |
2.43 |
|
Public administration |
0.40 |
n/a |
0.71 |
n/a |
n/a |
0.10 |
n/a |
n/a |
0.67 |
1.88 |
Based on: [Digital Transformation Index.... 2018; Data leaks.... 2019; Measuring the Digital Transformation..., 2019; The Costs of an Unnecessarily..., 2019; Cost of a Data Breach Report, 2020; Report to the Nations..., 2020; Chander et al., 2021].
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