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Fiscal Control in the Digital Economy l Roma Raul Zambrano - CIAT September 2015 1 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 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 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 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 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 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 A “small” evolutions 8 Internet of things 9 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 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 Network analysis 12 Visualization 18116682928982363 75673531375849428 04118798115170382 43644131679600413 78843744243269849 64471229780824531 13 Visualization 18116682928982363 75673531375849428 04118798115170382 43644131679600413 78843744243269849 64471229780824531 14 Non-compliance prediction • Linear and logistic regression 15 Non-compliance prediction (2) • Machine learning and data mining • Exploratory analysis • Pattern recognition • Decision-trees prediction • Neural networks • Clustering 16 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 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 A simple but practical example (2) 19 A simple but practical example (3) 20 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 Thanks. www.ciat.org @rulizv linkedIn: Raul Zambrano 22
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