We Chat, They Watch

26

September

2022

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Imagine living in the most advanced and restrictive content-filtering Internet regime, also known as the Great Firewall, in the world: welcome to mainland China. As of the implementation by the Chinese government in 2008, the country blocks Internet users in China from visiting quite an extensive list of foreign websites (Chandel et al., 2019). Such giant censoring and controlling mechanisms ensure that both online and offline the same political values, ideals, and standards are enforced on the population while maintaining China’s infrastructure independent of outsiders (Economy, 2021). Anyhow, with no Twitter, no Facebook, no Google, or YouTube, the internet vacuum was eventually filled in Chinese copycats of those platforms. 

One of them is WeChat, which is a super app combining all functions and different Western platforms in one application. What once emerged as a voice messaging platform transformed into a ubiquitous app, with over 1.29 billion active users per month (Statista, 2022). Essentially, within the app there is a variety of functions, basically containing anything you may want to do online, ranging from audio and text messaging, WeChat moments (which is similar to Instagram or Facebook’s timeline), order food delivery, to booking a doctor appointment, reading news, and paying bills (Boyd, 2019). All in one single, integrated application, a convenient and transformative technology you’d say. You’re basically never leaving the app because there is no urge to.

Sounds pretty great, right? Well, using one app to do all these activities enables WeChat to collect a staggering volume of personal data, that Google, Amazon, and Facebook all combined have not been able to achieve. This offers WeChat an immensely powerful and unique position in the market, as it possesses one of the most valuable data resources (Wu & Chingman, 2022). Nevertheless, still positioned within the Great Firewall, WeChat must share its data with China’s cybersecurity law to support the ruling of the Chinese Communist Party to control its citizens and censor speech (Wu & Chingman, 2022). Though previous evidence shows no censorship among users not registered under China-based phone numbers, experiments oppose the opposite, as it reveals that even non-Chinese accounts are subject to surveillance (Knockel et al., 2022). So, be aware because they are watching. 

Have you ever used WeChat, or are you familiar with it? I use it occasionally to communicate with family members residing in China and to text my parents because they rather use WeChat than WhatsApp. However, I’ve never known that WeChat has been such a big and powerful, but censoring app deeply integrated into users’ daily lives. No wonder why I see so many Chinese people using it. Would you say it is a super app? Or…

References

Boyd, C. (2019, January 21). WeChat: The evolution and future of China’s most popular app. Medium. Retrieved 25 September 2022, from https://medium.com/swlh/wechat-the-evolution-and-future-of-chinas-most-popular-app-11effa5639ed

Chandel, S., Jingji, Z., Yunnan, Y., Jingyao, S., & Zhipeng, Z. (2019, October). The Golden Shield Project of China: A Decade Later—An in-Depth Study of the Great Firewall. 2019 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC). https://doi.org/10.1109/cyberc.2019.00027

Economy, E. C. (2021, July 7). The great firewall of China: Xi Jinping’s internet shutdown. The Guardian. Retrieved 25 September 2022, from https://www.theguardian.com/news/2018/jun/29/the-great-firewall-of-china-xi-jinpings-internet-shutdown

Knockel, J., Parsons, C., Ruan, L., Xiong, R., Crandall, J., & Deibert, R. (2022, January 24). How International Users Unwittingly Build up WeChat’s Chinese Censorship Apparatus. The Citizen Lab. Retrieved 26 September 2022, from https://citizenlab.ca/2020/05/we-chat-they-watch/

Statista. (2022, July 27). Number of active WeChat messenger accounts Q2 2011-Q1 2022. Retrieved 25 September 2022, from https://www.statista.com/statistics/255778/number-of-active-wechat-messenger-accounts/

Wu, Y., & Chingman, Y. (2022, September 22). WeChat warns users their likes, comments and histories are being sent to China. Radio Free Asia. Retrieved 26 September 2022, from https://www.rfa.org/english/news/china/wechat-09082022183307.html

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Gotta ban them all!

26

September

2021

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Ever since cryptocurrencies have existed, they have been a controversial topic mostly because of their volatility, the electricity needed to mine them, and the legality of the transactions behind them. After several (unsuccessful) attempts to stop cryptocurrency activity in China, the government has passed a bill this Friday, the 24th of September, to make all crypto financial transactions illegal (BBC News, 2021).

China’s first attempt to ban cryptocurrencies dates back to 2013. Eight years later they have decided to take more extreme measures and the results are still to be seen. It’s easy to understand why it is not an easy task, the technology behind crypto is untraceable, and China is responsible for 46% of the world’s current computing processing power used for mining (Al Jazeera, 2021). Nonetheless, the measures seem to be working, as in 2019 this value was up to 75%.

Why is this happening?

Cryptocurrencies’ worth is translated in individual empowerment and in a form of freedom. These are 2 concepts that are not well aligned with CCP’s usual policies. The government would rather hold the population and, most importantly, their information solely under their control, and anything that provides an alternative to that is considered a threat. However, the government justifies these measures with environmental issues and population protection from a dangerous volatile market (Al Jazeera, 2021). The People’s Bank of China claims that “[virtual currency-related business activities] seriously endangers the safety of people’s assets” (BBC News, 2021).

Effects

Despite the drops this news caused in the market the following days, the rest of the world doesn’t seem to care too much about this ban. Ulrik K.Lykke, an executive director at crypto hedge fund ARK36 wrote in an email: “While each time this [China’s crackdown] happens, the markets react with a price drop, each time the effect is smaller and more short-lived. The ‘China bans Bitcoin’ story has gained almost a meme-like status in the Bitcoin community because of this.” (Yue, 2021). The truth is the market has survived every ban news since 2013, and since then, Bitcoin’s value has skyrocketed from just 196 US$ in Oct 2013 to 44,755 US$ in Sep 2021 (Statista, 2021). Cryptocurrencies seem to have come to stay, and China’s efforts to ban their power, just give its supporters more reason to believe it is fulfilling its job of empowering individual freedom for the world.

References

BBC News. (2021, September 24). China declares all crypto-currency transactions illegal. https://www.bbc.com/news/technology-58678907

Al Jazeera. (2021, September 24). Bitcoin slumps as China bans all cryptocurrency transactions. Business and Economy News | Al Jazeera. https://www.aljazeera.com/economy/2021/9/24/bbbitcoin-slumps-as-china-bans-all-cryptocurrency-transactions

Yue, F. (2021, September 25). China’s crypto ban has almost achieved a “meme-like status,” but here are the lingering impacts. MarketWatch. https://www.marketwatch.com/story/chinas-crypto-ban-has-almost-achieved-a-meme-like-status-but-here-are-the-lingering-impacts-11632512981

Statista. (2021, September 24). Bitcoin (BTC) price history up until September 24, 2021. https://www.statista.com/statistics/326707/bitcoin-price-index/

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AI-enabled China’s Social Credit System: in-depth analysis

5

October

2020

5/5 (1)

Automation has transformed every aspect of modern individuals’ lives. Trivial tasks that used to take a person hours to complete, can now be performed within a matter of seconds due to technological advancements. Artificial Intelligence (AI) is one such advancement of technology that is paving the way for the prevalence of automation in every industry. The ability of AI to perform tasks autonomously is primarily possible due to its ability to be able to process large amounts of data and infer patterns and conclusions within this data, thus effectively learning tasks by itself. However, the procedures used by the AI to analyze the data are initially inputted by an administrator in the form of algorithms and statistical models. An algorithm is essentially a set of rules and the process to be followed by the machine/computer to perform a calculation/action. Modern automation stripped to its core, is a collection of algorithms and related statistical models programmed by an administrator. Due to the increased adoption of the internet, algorithms have become integrated into every aspect of our lives.

The financial credit system used in many western countries can be seen as an example of how algorithms govern our lives. The system involves gathering financial data relevant to an individual from multiple sources, followed by an algorithm that analyses the likelihood of an individual defaulting on a loan. The data gathered primarily consists of previous debts taken, payment deductibles not met and other forms of credit taken up by the individual in the past. After the careful analysis of this data, the algorithm calculates a score for the individual, the credit score. This score is then used by banks, insurance companies, and other financial institutions to determine the creditworthiness of the individual when he/she requests their services (Petrasic & Saul, 2017). In China, such a system exists not only to determine a citizen’s financial credit score, but it expands to all aspects of a citizen’s life by judging citizens’ behavior and trustworthiness, known as the Social Credit System, introduced in 2014. The Social Credit System will have a complete database on all Chinese citizens by 2020, which will be collected from a variety of sources. This scale of data collection is possible in China as Baidu, Alibaba and Tencent are the major providers of internet infrastructure in the country; they work closely with the Chinese Communist Party (Kobie, 2019). The majority of the digital footprint left by Chinese citizens is on infrastructure established by these companies thereby making it easy for the Chinese Communist Party to access its citizens’ data. This sharing of data between private companies and the government is not commonly heard of in China’s western counterparts and shows the importance of data protection laws enforced in those countries. The implementation of the Social Credit System has numerous effects on the country and citizens on economic and social levels.

On an economic level, the algorithms that facilitate the Social Credit System help bridge a major institutional gap that is the underdeveloped financial credit system in China. As mentioned earlier, the financial credit system utilizes algorithms to calculate a credit score to determine the creditworthiness of individuals. Such credit checks can make it more difficult or even deny individuals to access credits. Often, these credit checks focus on only certain aspects such as the timely manner in which we pay our debts (Petrasic & Saul, 2017). This is simply not enough to determine the creditworthiness of individuals as there are other factors at play as to why individuals pay their debts over a certain time period as they do. The commercial credit systems such as the Sesame Credit (developed by Ant Financial Services Group) can therefore be seen as more valuable in determining the creditworthiness of individuals. The Sesame credit score is arguably a better predictor of trustworthiness, as the scores take a broad range of important factors into account. This will prove to be very beneficial for the financial institutions as they will have the highest level of guarantee that the credit extended will be in safe hands. At the same time though, the citizen with a low rating will not be eligible for large loans and will be asked to pay a very high interest rate. Thus, effectively positioning the algorithm behind the Social Credit System as the decisive entity on whether a citizen can be eligible for a loan or not. The argumentation behind the decision to allow an algorithm to govern the credit eligibility of the citizens states that, due to the restrictions placed on the citizen with a lower score, it would motivate them to be better citizens thus achieving a better score. However, citizens with a lower social credit score than a certain threshold may be subject to more restrictions. For example, citizens with low social credit scores are restricted access to certain services such as (quality) education or (quality) transportation. On a social level, the Social Credit System may give rise to social segregation, where citizens with low social credits are exempted from social activities as well as leading to reduced interactions between citizens with higher social credits and those with lower social credits. Moreover, on the work floor, people with low social credit scores may fail to get a promotion because of their scores. The combined effect of restricted access to education, social segregation as well as limited career prospects, can lead to the next generation of those citizens, who have low social credits, being given unfair chances to increase their social credits, and, as a result, their quality of life. Questions arise whether algorithms account for bridging the social inequality gap or if it even strengthens it (Ebadi, 2018).

References

Ebadi, B. (2018). Artificial Intelligence Could Magnify Social Inequality. Centre for International Governance Innovation. Retrieved from https://www.cigionline.org/articles/artificial-intelligence-could-magnify-social-inequality

Kobie, N. (2019). The complicated truth about China’s social credit system. Wired. Retrieved from https://www.wired.co.uk/article/china-social-credit-system-explained

Petrasic, K., & Saul, B. (2017). Algorithms and bias: What lenders need to know. White & Case. Retrieved from https://www.whitecase.com/publications/insight/algorithms-and-bias-what-lenders-ne ed-know

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Ant Group: What is behind the success of the most valuable FinTech in the world?

27

September

2020

5/5 (1)

The Chinese FinTech Ant Group has been in the headlines lately due to its IPO filing in the Shanghai and Hong Kong stock exchange. The parent company of Alipay is aiming at a staggering valuation of above 200 billion USD, which could potentially be the biggest public filing in history.  Just to put it under perspective, this valuation would make Ant Group more valuable than the iconic American investment bank Goldman Sachs. But how did this 6-year company rose to this status so quickly? Notably, the fact that it is a spin-off of Chinese e-commerce behemoth Alibaba has definitely played a key role. Nevertheless, its data intelligence powered business model has been of equal importance.

It all started in 2004 when Alibaba launched Alipay, which back then was an escrow account system, whose underlying goal was to provide security and trust in the still novel online marketplace. Propelled by innovation and the underdeveloped financial infrastructure in China, Alibaba quickly saw the opportunity and turned Alipay in a digital finance super-app, offering services ranging from digital payments and investments to instant credit applications. To support their new banking capabilities Alibaba launched MYbank, one of the first private banks in China.

Screenshot 2020-09-27 at 10.04.49

Figure 1: Alipay’s user interface (Ant Group, 2020)

Fast forward a few years and Alipay is now a fully-fledged payment network, handling a volume of 17 trillion USD in transactions between June 2019 and June 2020 in China. Yu’e bao (‘leftover treasure’ in Chinese) – Alipay’s deposit/money market fund- was, at some point, the largest fund of its sort having over 200 billion USD in Assets under Management. Powered by their Zhima credit score, Alipay enabled about 300 billion USD in consumer and SME credit. Moreover, Alipay also offers its proprietary state-of-the-art cloud-based core banking system as a platform to other financial institutions.

So what was Alibaba’s contribution to all of this? Well, naturally it is easier to market services to an established customer base. On top of this, the e-commerce platform also presented the perfect initial uses cases, that have now long left the boundaries of the digital world and are as well ubiquitous throughout daily life in China (see Figure 2). Most importantly, as Alibaba has evolved into a “hub firm”, the enormous amount of data collected about customers has shaped much of the services at Alipay. Taking SME lending as an example, Alipay started providing microloans to sellers at Alibaba’s marketplaces relying on a credit assessment which was not based on the borrower’s credit history, as most traditional banks do. Instead, as small businesses in China are often informal and do not have a well-documented credit history, Alipay based their metrics on factors derived from their behavior at Alibaba’s marketplaces. These could be for example the seller’s reputation or how long the seller spends on the business. The outcome and repayment behaviors could then be used as real-time feedback to improve the accuracy of the credit assessment. Moreover, as the service grew, more data points could be integrated, polishing the algorithm in a never-ending feedback loop.

Screenshot 2020-09-27 at 12.25.02

Figure 2: Alipay use cases (Ant Group, 2020)

With this in mind, although Alibaba played a key role, the capabilities developed turned Ant Group into a data powerhouse and ecosystem of its own. For instance, Ant financial’s algorithms incredible accuracy even enabled them to redefine the lending business: Unlike traditional lenders, Ant Financial does not underwrite any of the loans it originates. Instead, it only focuses on assessing borrowers’ credit risk and connecting them to loans provided by third parties. By doing this, it outsources all the related risks and can still capture significant value by becoming the banking marketplace in China.

As discussed, Ant Group has risen to a giant in the financial ecosystem in China by making intelligent use of Alibaba’s vast amount of data to develop its own business model. Today the synergies among the two “hubs” are still ever reinforcing, but as Ant Group’s technology and capabilities continue to grow, other markets and revenue opportunities might arise.

What are your thoughts? What do you think will be Ant Group’s next big strategic move?

 

References

Ant Group, 2020. Application Proof (Prospectus), Hong Kong: Ant Group.

Hansen, S., 2020. What We Learned From Ant Group’s New IPO Filing. [Online]
Available at: https://www.forbes.com[Accessed 26 September 2020].

Iansiti, M. & Lakhani, K. R., 2018. Managing our hub economy. Harvard Business Review, September-October, pp. 17-17.

King, B., 2018. Bank 4.0: Banking everywhere, never at a bank. Kindle Edition. Marshall Cavendish International (Asia) Pte Ltd.

Zeng, M., 2018. Smart Business. 1st Edition ed. Boston: Harvard Business Review Press.

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Mobile payment, why China and not Europe?

6

September

2018

5/5 (1) Mobile payment is developing in a rapid pace in mainland China. Mobile transactions between January and October 2017 amounted to a total of US$12.77 trillion and increased by roughly a third compared to previous year.1 Mobile payment enabled Chinese consumers to make electronic transactions at any level, ranging from purchasing your breakfast at a small roadside food stall to paying your monthly rent. The rapid adoption of mobile payment is typical for China, while consumers in developed regions such as Western Europe or the US seem to be reluctant to incorporate mobile payment in their daily lives in a such a way as it occurs in China.

 

Mobile payment has trouble with positioning itself in developed regions due to the presence of a solid banking infrastructure. China has made a remarkable development since its opening up in 1978 and has ‘skipped’ the banking evolution during its process in becoming a developed country. In contrast to the Chinese situation, the majority of the people in the aforementioned regions already possess a debit card, credit card or both when they got in touch with mobile payment. Combined with a well-established payment infrastructure, there is little need for these people to switch to another payment platform. In fact, it may even become a burden to do so.

 

For example, in the Netherlands a debit card will assure you to pay closely to anywhere in the country, as cash machines and cash devices are nearly all over the place. In case you would leave your cash and debit card at home, as a consumer you run a great risk of unable to buy anything because mobile payment platforms are generally not supported.

 

Mobile payment has been successful in China, but only because it passed the banking evolution. In developed countries, banking infrastructure enjoyed the time to settle in society and people’s daily activities. The financial sector is however catching up with people’s smartphone behavior by introducing mobile debit card functionality to smartphones.2,3 Although we see an alternative mobile payment platform emerge, one that is closely intertwined with the banking infrastructure, it will not replace the debit card in the short term.

 

Sources

1http://www.chinadaily.com.cn/a/201802/19/WS5a8a8e42a3106e7dcc13d08f.html

2https://www.rabobank.nl/particulieren/betalen/contactloos-betalen/rabo-wallet/

3https://www.ing.nl/particulier/mobiel-en-internetbankieren/mobiel-betalen/index.html

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No Cash, No Card, No Phone, Just Your Face – The New Age of Payment Technology

27

September

2017

5/5 (1)

Just a few years ago bank card scanners were introduced, eliminating the pin-code function. Just a year ago phone scanning payment systems were introduced. Just a few months ago companies introduced face-scan payments. Although facial recognition is nothing new, its accuracy has only just become sufficient to secure financial transactions. The advancement of payment systems has been exponential over the past years, and is expected to only intensify in the future.

Chinese companies are currently using face-detecting systems to synchronize many daily tasks. These systems can be used to authorize payments, access facilities and even track down criminals, being described as beneficial for both surveillance and convenience.

Face payment systems are something so advanced for the most of us that I will take a minute to explain how it works. Chinese start-up Face ++ has already begun realizing this new payment opportunity. Through scanning one’s face, one gets placed in a database. The database allows over 83 points of one’s face to be tracked and analyzed simultaneously. In tracking this “unique” points on one’s face, the system can recognize a client in the database and access their accounts to authorize payments, access facilities and even track down criminals. (Knight, 2017) In order to avoid people tricking the technology they have also implemented a “liveness” algorithm to ensure that people are not using a photo or video of someone else. (Wang, 2017) The entire system works through users uploading their “live” faces into the database, integrating facial biometric data and tracking the 83 mentioned points from various angles. (Stray, 2017)

Face ++ technology’s popularity has been growing rapidly, with approximately 120 million people in China making use of the Face++ app to confirm their payments. (Stray, 2017) The company has already partnered with commercial banks such as Ant Financial and has already been incorporated into various apps in China, such as Alipay, in order to complete payments. (Knight, 2017) The popularity of face-payment systems has even spread to restaurant chains in China. Chinese high-tech versions of KFC, known as KPro, installed face-scanners earlier this month, being the first physical store in the world to use facial recognition software for processing payments (Wang, 2017). Deep learning and artificial intelligence techniques are employed for image recognition in order to increase identification reliability. (Knight, 2017)

The initial benefit of such facial recognition payments was to increase customer convenience, thereby enhancing customer experience. As these payments have become more abundant, their benefits have become more noticeable. Facial-recognition payments are believed to ease the customer experience as customers will no longer have to carry wallets or phones with them. Furthermore, the experience is believed to be more “personalized” as the payment systems can recognize who a customer is, how often they have been somewhere, and their ordering preferences. Facial-recognition payment software is being found to be beneficial not only for payments and convenience, but also for surveillance. Local governments in China are currently using the Face ++ technology to identify criminals from surveillance cameras. (Stray, 2017)

However, the risks associated with facial-recognition payments are the reason why these payments have not yet achieved success in Western countries. The main risk, which you may have already guessed, is the privacy issues at hand. Implementation of such a system which is reliant on a large database may be difficult in countries outside of China. China already has a large database of ID card photos of its inhabitants, which lowers the intensity of possible privacy issues, as inhabitants already have their information stored in a large, government controlled database. One’s personal data: finances, whereabouts, habits, frequently visited locations, etc. are all stored in a database. If this database were to be accessed by someone outside of the allocated authorities, then there could be a tremendous problem. Furthermore, everywhere one goes, their personal data shows up, how safe is this? People are often hesitant in sharing data, especially regarding financial data. In storing all of this data in one large database, there is a high risk of this ending up public.

Furthermore, it is believed that the integration of facial-recognition software into our daily activities will greatly transform everything from policing to the way people act everyday. (Knight, 2017) In making our day-to-day interactions more mechanical, will we soon lose normal social skills that we use when going to the grocery store, or the bank? It has been proven that people receive endorphins from social contact (Krach, 2010), where will we now receive these endorphins from? Will people turn into worlds of isolation?

Furthermore, if people become used to walking around without wallets, what will they do when faced with other situations in countries where privacy regulations are such that these payments are not possible, or not feasible?  In India they are still struggling with mobile wallets, it will take decades before they are able to implement such a facial-recognition system. How will people go about having a “back-up” for when travelling to less advanced countries if they have gotten rid of all of their wallets?

Although facial-recognition is not a new concept, and facial-recognition payments sound convenient, there are many aspects which must be considered before implementing this system across more countries, especially across countries with more privacy limitations.

“We hope one day in the future people can go out without their cell phones or wallets” – Dong Liyun (Wang, 2017). What do you think, will this technology soon be implemented in Europe? How do you think the EU will deal with the aforementioned privacy issues? How do you think facial-recognition will change society?

References

Knight, W. (2017). In China, you can pay for goods just by showing your face. [online] MIT Technology Review. Available at: https://www.technologyreview.com/s/603494/10-breakthrough-technologies-2017-paying-with-your-face/ [Accessed 27 Sep. 2017].

Krach, S. (2010). The rewarding nature of social interactions. [online] Available at: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2889690/ [Accessed 27 Sep. 2017].

Stray, K. (2017). Paying With Your Face++. [online] Coin Telegraph. Available at: https://cointelegraph.com/news/paying-with-your-face [Accessed 27 Sep. 2017].

Wang, J. (2017). Pay with your face at this KFC restaurant in China. [online] CNNMoney. Available at: http://money.cnn.com/2017/09/01/technology/china-alipay-kfc-facial-recognition/index.html [Accessed 27 Sep. 2017].

 

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