1. Cashless Society – utopia or future PhD candidate Damir Sindik, CBCG
Overview of trends in World, EU and Montenegro

2. Pro et contra: Cash vs Cashless
Cash pro: legal tender, easy to use, direct settlement, immediate transfer of value, anonymity, safe haven, etc.
Cash contra: counterfeited, ready cash is also fairly easy to steal
Cashless pro: individual does not need to carry cash with him or her everywhere, facilitate change when transaction is of odd amount, no risk of receiving counterfeit currency, easier to track the black money and illegal transactions, long distance payment for goods and services
Cashless contra: prevailingly non-anonymous usage, need for supporting infrastructure – agents, not available to everyone and everywhere

3. World trends in non-cash transactions
Global non-cash transactions volumes grew at 10.1% in to reach billion
Non-cash transactions are estimated to accelerate at a compound annual growth rate (CAGR) of 12.7% globally with emerging markets growing at 21.6% from
Global electronic wallet (e-wallet) transaction volumes are estimated to be about 41.8 billion in 2016, comprising almost 8.6% of all non-cash transactions
Source: World Payments Report 2018 – Capgemini & BNP Paribas

4. World non-cash statistic 2012-2016.
Source: World Payments Report 2018 – Capgemini & BNP Paribas

5. Payment instruments and trends
Source: World Payments Report 2018 – Capgemini & BNP Paribas

6. World non-cash transaction distribution
Source: World Payments Report 2018 – Capgemini & BNP Paribas

7. New challenges and opportunities
Source: World Payments Report 2018 – Capgemini & BNP Paribas

8. Level of Crypto-friendliness worldwide
Source: World Payments Report 2018 – Capgemini & BNP Paribas

9. Mobile Payments (2018.) – millions

10. Mobile phone subscriptions (2016.)
Source: World Cash Report 2018 – G4S Cash Solutions & Payments Advisory Group (Utrecht, NL) Data source: The World Bank

11. World non-cash FORECAST
Source: World Payments Report 2018 – Capgemini & BNP Paribas

12. Non-cash payments increases level of transparency in Economy
Source: World Payments Report 2018 – Capgemini & BNP Paribas

13. European Union (EU)

14. EU - Payments statistics: 2017 (ECB)
The total number of non-cash payments in the EU increased by 7.9% - to 134 billion, in 2017 compared with the previous year.
Card payments accounted for 52% of the total number of non-cash payments in the EU, while credit transfers accounted for 24% and direct debits for 19%
The number of payment cards issued (812 million) represented around 1.6 payment cards per EU inhabitant
Around 57 billion transactions were processed by retail payment systems in the EU with an amount of €44.0 trillion.
Source: European Central Bank (ECB) - SDW and Eurostat data

15. EU non-cash payments overview
Source: ECB

16. LVPS in EU (2017.)
Source: ECB

17. Retail Payment Systems in EU (2017.)
Source: ECB

18. EU cash payment statistics at POS
Source: World Cash Report 2018 – G4S Cash Solutions & Payments Advisory Group (Utrecht, NL)

19. EU cash withdrawals on ATM
Source: World Cash Report 2018 – G4S Cash Solutions & Payments Advisory Group (Utrecht, NL)

20. Development of basic non-cash payment conditions and future outlook
Source: World Cash Report 2018 – G4S Cash Solutions & Payments Advisory Group (Utrecht, NL)

21. Montenegro Payment System (MPS)
Data Source: Centralna banka Crne Gore – CBCG (Central Bank of Montenegro)
Web link:

22. Volume of MPS – 3D overview
Source: Centralna banka Crne Gore – CBCG
* Research, calculation and analysis are performed by author with usage of MATLAB R2019a program package (MathWorks), and do not necesarly represent the views and stanpoints of CBCG.
** Interpolation of source data is achieved by using Prof. Cleve Moler algorithm – MATLAB Makima Piecewise Cubic Hermite Interpolation Algorithm.

23. Volume of MPS – plane projections
Source: Centralna banka Crne Gore – CBCG

24. Number of transactions in MPS – 3D overview
Source: Centralna banka Crne Gore – CBCG

25. Number of transactions in MPS – plane projections
Source: Centralna banka Crne Gore – CBCG

26. VAR log data estimates of MPS and Impulse Response Function (IRF)
Source: Centralna banka Crne Gore – CBCG
* Research, calculation and analysis are performed by author with usage of EVIEWS 10 program package (IHS Markit), and do not necesarly represent the views and stanpoints of CBCG.

27. VAR HD Cholesky Weights estimation (stochastic) and VAR Equation
Source: Centralna banka Crne Gore – CBCG

28. Artificial Intelligence (AI) analysis of MPS – machine learning with deep learning algorithm training (Hyper Parameter Optimization)
Source: Centralna banka Crne Gore – CBCG
* Research, calculation and analysis are performed by author with usage of MATLAB R2019a program package (MathWorks), and do not necesarly represent the views and stanpoints of CBCG.

29. Questions & Answers
Thank You!