1. Fiscal Control in the Digital Economy l Roma Raul Zambrano - CIAT September 2015 1

2. Digital Economy Devices connected to the Internet. PC and mobile. App stores 27 average applications 900 million users of a single messaging application 12 among the first one hundred most valuable companies are from the technology sector. Including the first 3. Global markets 2

3. B2C models On-line sales of traditional goods (Amazon, virtual stores of brick and mortar stores, airline tickets) On-line sales of digital goods (iTunes) Streaming services (Netflix, Spotify) Low/no cost applications Free up-to “Freemium”. Functionality, capacity, virtual goods, features. Ads sold to third parties. Selling information, untreated or otherwise. E-mail, storage, note taking, virtual machines, photography, IM, micro-blogging, video, travel agencies. 3

4. B2B models Platform interconnection Integration of logistics Corporate activities Cloud Services SaaS, PaaS, IaaS Ej. (Office 365, Salesforce; Operations based on off-the-country sites Operations with related parts. 4

5. C2C models Operations supported by third party apps or services Sales of physical goods (eBay, Urgente24) Apps to support services in the sharing economy (Uber, Airbnb) Some major characteristics True global markets Potentially different jurisdictions for each participant Regulation frameworks Different psychology for C2C operations. 5

6. Some new things The value of data Specialization of content and advertisement Contribution to value created within processes Large network effect ¿What is the actual value of data ? ¿When can it be determined? ¿Can they constitute some kind of primary resource? The value of content Free applications with in-app purchases 3D printers “Downloaded goods” 6

7. Digital money and cryptocurrencies Intermediary payment systems. (Ej. Paypal) Money-like items within environments (Games, Tokens within entertainment apps Bliive) Cryptocurrencies No central point of control Limit of the number of total possible bitcoins Anonymity is a primary goal Usually identified with illegal operations 7

8. A “small” evolutions 8

9. Internet of things 9

10. Challenges for the Tax Administrations The usual problems. Exacerbated. Companies created to “operate” fictitious transactions Fraudulent mechanisms to get refunds Difficulties with informal operations. International Taxation and BEPS issues Gray borders and dark sites Difficulties to determine geographic locations. VPNs, Dark Web, Cryptocurrencies 10

11. Options and alternatives More data, closer to transactions. Third – party reported information Electronic documents (Electronic invoice and withholding certificates) EFOS and EDOS in SAT Mexico Concurrent control in Brazil Transparent existent information Pre-filled returns Actions triggered by returns processing Electronic mailboxes Truly anonymous lead systems Specialized applications for auditors 11

12. Network analysis 12

13. Visualization 1 8 1 1 6 6 8 2 9 2 8 9 8 2 3 6 3 7 5 6 7 3 5 3 1 3 7 5 8 4 9 4 2 8 0 4 1 1 8 7 9 8 1 1 5 1 7 0 3 8 2 4 3 6 4 4 1 3 1 6 7 9 6 0 0 4 1 3 7 8 8 4 3 7 4 4 2 4 3 2 6 9 8 4 9 6 4 4 7 1 2 2 9 7 8 0 8 2 4 5 3 1 13

14. Visualization 1 8 1 1 6 6 8 2 9 2 8 9 8 2 3 6 3 7 5 6 7 3 5 3 1 3 7 5 8 4 9 4 2 8 0 4 1 1 8 7 9 8 1 1 5 1 7 0 3 8 2 4 3 6 4 4 1 3 1 6 7 9 6 0 0 4 1 3 7 8 8 4 3 7 4 4 2 4 3 2 6 9 8 4 9 6 4 4 7 1 2 2 9 7 8 0 8 2 4 5 3 1 14

15. Non-compliance prediction Linear and logistic regression 15

16. Non-compliance prediction (2) Machine learning and data mining Exploratory analysis Pattern recognition Decision-trees prediction Neural networks Clustering 16

17. Human resources New skills and knowledge Change management Deal with new and sophisticated risks Digital economy. Great responsibilities For example, data from electronic invoices. 17

18. A simple practical example Sent emails: 300 000. Opened 84% The time when the mail was sent matters? Hour, day of the week, month? Does a different sender has a different effect. Kind of domain? (specific vs. general) Length of text? 18

19. A simple but practical example (2) 19

20. A simple but practical example (3) 20

21. A simple practical example Sent emails: 300 000. Opened 84% The time when the mail was sent matters? Hour, day of the week, month? Does a different sender has a different effect. Kind of domain? (specific vs. general) Length of text? Develop a fitted model for each instance Maximize the function to reach each person at the best moment. 21

22. Thanks. www.ciat.org @rulizv linkedIn: Raul Zambrano www.ciat.orgRaul Zambrano 22