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Economics

تحدي تمويل شركات التكنولوجيا الكبرى

Apr 9, 2021 Barry Eichengreen

بيركلي ــ في عام 2009، في خضم الأزمة المالية العالمية، لاحظ رئيس مجلس الاحتياطي الفيدرالي السابق بول فولكر أن الإبداع المالي الوحيد المنتج اجتماعيا خلال السنوات العشرين السابقة كان ماكينة الصراف الآلي. ويتساءل المرء ماذا كان فولكر ليرى في تسونامي الإبداعات المالية الـمُـمَـكَّـنة رقميا اليوم، من منصات الدفع عبر الأجهزة المحمولة إلى الخدمات المصرفية عبر الإنترنت والإقراض من نظير إلى نظير.

ربما كان فولكر ليشعر بالاطمئنان: فمثلها كمثل ماكينة الصراف الآلي، تقدم العديد من هذه الابتكارات فوائد ملموسة في ما يتصل بخفض تكاليف المعاملات. ولكن بصفته منتقدا للشركات المالية الضخمة، كان فولكر ليشعر بالقلق أيضا إزاء دخول بعض شركات التكنولوجيا الشديدة الضخامة إلى هذا القطاع. أسماء هذه الشركات مألوفة بقدر ما تنتشر خدماتها في كل مكان: شركة التجارة الإلكترونية العملاقة أمازون في الولايات المتحدة، وشركة خدمات الرسائل "كاكاو" في كوريا، ومنصة المزادات والتجارة عبر الإنترنت Mercado Libreفي أميركا اللاتينية، وشركتا التكنولوجيا العملاقتان "علي بابا" وTencentفي الصين.

تتولى هذه الكيانات الآن فعليا كل ما يتعلق بالتمويل. فتقدم شركة أمازون القروض للشركات الصغيرة والمتوسطة الحجم. وتقدم شركة كاكاو نطاقا كاملا من الخدمات المصرفية. وتقدم منصة Ant Financialالتابعة لشركة علي بابا ومنصة WeChatالتابعة لشركة Tencentوفرة من المنتجات المالية، بعد أن توسعتا بسرعة إلى الحد الذي أصبحتا معه في الآونة الأخيرة هدفين لحملة تنظيمية صارمة من جانب الحكومة الصينية.

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Adolfo Villanueva Apr 11, 2021

Algorithmic processes are not at all naturally black box, there are thousands, if not millions, of algorithmic processes that are absolutely transparent, in avionics, early warning systems, etc., for example. The kind of neural networks used in AI are far more black box, that's true; if one ads the secretive and evasive nature of the financial system there is a full fledged black box, with "legal" walls and moats added. It's not easy, but technical savvy and political will can deal with it. As for big tech, they are mostly good old fashioned monopolies undercovered by bedazzling IT. What is Amazon but catalog sales on steroids...
- Prashant Kotak Apr 12, 2021
  
  Notwithstanding that neural nets are very effective a pattern matching, they are inherently 'black-box' - it is not for example, currently possible to determine exactly how a neural net reaches the decisions it does, for example: https://www.technologyreview.com/2017/04/11/5113/the-dark-secret-at-the-heart-of-ai/
  
  I used to know postgrads at Brunel Univ in the early 80s in neural nets, when I had many conversations about this - I was told at the time, the information streams traversing the feedforward and feedback layers were gobbledegook. Processing power at the time was toytown, and the expectation was it would eventually become possible to decipher exactly what the diffuse forms of knowledge stored as 'weightings' across the networks of perceptrons were doing, when computing power increased significantly - because like all processes run on Universal Turing Machines (which is what the vast majority of computing devices are), neural nets are completely deterministic. (As it happens though, the Brunel researchers were implementing neural nets directly in hardware, as well as running them on computers.)
  
  Neural networks have a peculiar parallel to human expert decision making and knowledge, which is little discussed - certainly no non-tecchies I know have ever fully understood this point - they will look at you blankly if you raise it. However, tecchies who think about the nature of knowledge and algorithmic vs human decision making, and the question if all human processing is ultimately algorithmic or not, will know instantly what I am talking about. Like neural nets, top-end human expertise equally cannot tell you exactly what it does to reach the conclusions it does. An illustration of this, is that there are a small number of traders who will make money in all circumstances and all kinds of markets. However, when their professed methods are turned into algorithms, they significantly underperform the trader. This happens even if the trader instigated the process of turning the processes into trading system algorithms. Investigation of the point at which the trader and the algorithm differ always yields an extra layer of "rules" - 'oh yeah, if a is happening and b or c is looking like the case, then in that circumstance you should look at x and do y and z instead' - and there is *always* another layer, it's like pulling on a string that keeps coming. This says several interesting things, one of them being, it seems not only can human experts not fully articulate the deeper levels of their expertise, they are in effect confabulating stories about what they do, even to themselves.
  
  Neural nets appear to be some kind of multi-dimensional function approximators, bypassing mass scale number crunching, and instead arriving at sophisticated heuristic answers - in this at least they appear to be doing something similar to what human expertise does.
  
  Hooking back to the article and your comment, I believe this is a losing battle: existing political governance structures where humans make the decisions, will *never* be able to react to technology driven change fast enough to keep control over technology. What is likely to happen instead in response, is that decision making will be increasingly handed over to ever more sophisticated adaptive algorithms, some of this in the name of ‘fairness’, as human decisions will come to be seen as ever more error prone in the face of rising systems complexity.
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Adrian Poster Apr 9, 2021

Security issues are seen as the biggest drawback of online banking. Although financial institutions work intensely to prevent any data loss, as it may jeopardise their whole business, bank account information is considered to be one of the main targets for hackers and various computer viruses.
http: //www.confidus.com/en/banks/account-management/online/
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https://prosyn.org/IJPbHNsar

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