Snippings

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Rama and other women the slum told Krishna that she must get her own Aadhar ID card, secure a smart phone (usually from one of her new employers), open a bank account (preferably in a bank that is not that accessible in rural areas) and secretly deposit a part of her earnings in there.

‘My drunk husband doesn’t know’: Informal income, phone-banking and female migrant workers in Kolkata, India – Aftermoney.dk:

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Later this year, with the Galaxy S20, users will be able to safely store their most important ID documents directly on their mobile device.1 The solution will also give authorities a streamlined process to issue digital IDs.

Users can request to create an eID using just their smartphone. Once relevant authorities verify the request, the eID will be automatically stored and isolated in a secure location on the device.

From How Samsung Is Replacing Your ID Documents With the Galaxy S20 – Samsung Global Newsroom:

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The flood of money is due to the US government doling out trillions of dollars to help its citizens with the economic hardships brought on by COVID-19 lockdowns; an unlimited bond buying program by the Federal Reserve; and people hoarding money because of uncertain times.

From Cash is king: US citizens putting more money in banks than ever – Decrypt:

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A device implementing a system for using a verified claim of identity may include at least one processor configured to receive a response vector corresponding to a verified claim of a user of a device, the verified claim comprising plural data fields to identify the user and being a digital certificate signed by a server, the verified claim being associated with the device, the response vector comprising, for each field of the plural data fields, a confidence score indicating a likelihood that the field is accurate. The at least one processor may be further configured to receive, from the device, a request for a service, determine, in response to receiving the request, that service is to be provided to the device based on the response vector and the verified claim, and provide the service to the device based on the determining.

From United States Patent Application: 0200213311:

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Customers typically pay the same price whether they use cash or a card. Economists say this equates to a transfer from users of cash to users of cards. How much? On average a cash-using household pays $149 a year and a card-using household receives $1,133, according to a 2010 study from the Federal Reserve Bank of Boston.

From The Credit-Card Fees Merchants Hate, Banks Love and Consumers Pay – WSJ:

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The Early Warning team is at the top of the list. This is where account ownership and verification should reside

From Open Banking, Open Payments and Trust Networks – Noyes Payments Blog:

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Eliminating wasteful penny production would be an improvement over the status quo. But penny production is merely a symptom of a much deeper problem: the lack of competition in coinage.

From Where Have All the Coins Gone? | Cato @ Liberty:

But who could compete? I’ve got an idea that I put forward a few years ago. It was ignored at the time, but now I think ti deserves to be reconsidered.

 

Who could produce coins more cheaply than the mint? Chinese counterfeiters. Let’s give them a chance.

 

Here’s my reasoning.

Tom Noyes’ typically accurate comment on this

From a payment scheme perspective open payments typically means leveraging a low cost clearing network (ex SEPA, ACH, Debit, …etc) or cryptocurrency with enhanced risk or identity services.

From Open Banking, Open Payments and Trust Networks – Noyes Payments Blog:

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Open is a terrible business model, but a fantastic technical one. Tip toe into any “open” effort as a form of intelligence gathering. Standardizing messages will enable merchants (and banks) to deliver new forms of payments within embedded processes and systems.

From Open Banking, Open Payments and Trust Networks – Noyes Payments Blog:

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Deep-fake images — a portmanteau word from “deep learning” for machine learning and “fake” — are generated with the help of computer models, so-called Generative Adversarial Networks, GANs for short. Two algorithms work together in these networks: the first algorithm creates random images based on certain input data. The second algorithm needs to decide whether the image is a fake or not. If the image is found to be a fake, the second algorithm gives the first algorithm the command to revise the image — until it no longer recognizes it as a fake.

From Which Face is Real? Using Frequency Analysis to Identify “Deep-Fake” Images:

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