In 2020, the Bank for International Settlements (BIS) in Basel, a kind central institution of central banks, published a study in which it praised the Indian government datababj Aadhar. Aadhar is a horror of all data protectionists. It involved imposing on over a billion people a biometrically underpinned unique citizen number that they have to use for their interactions with the state and with many private companies. The potential for surveillance is enormous. The justification: financial inclusion and (forced) incorporation into the formal sector of the economy.
In January 2022, the BIS followed up with another study that can be interpreted as promoting the Chinese social credit model with the argument of financial inclusion.
The promise of financial inclusion …
The BIS described as best parctice examples how new entrants to the credit market in China and Hong Kong are using data from social media platforms and applying “advanced data analytics” to it. The big advantage, it said, is financial inclusion, which is (mostly poorer) people and small businesses getting credit that couldn’t get it easily before.
The traditional reason for the reluctance to lend to these people has been that they can offer little or no collateral and have no credit history to instill trust in lenders. The lack of collateral could be replaced by giving potential lenders as much information as possible about potential loan customers – in other words, by making the latter as transparent as possible.
The argument seems convincing on the surface. True, if credit risk could be largely eliminated through better data, then the banks could in theory give out much more credit. Everyone would then be able to get more credit, even those who have been excluded so far because the risk was too high. But in practice it is very different.And this difference makes creditworthiness a relative concept, with a loser for every winner.
… and the bitter reality
The central bank, or whoever is responsible for controlling the money supply, cannot allow the banks to put more money into circulation at will. After all, money in circulation is essentially created by the lending of banks. From a macroeconomic perspective, the central bank therefore controls the creation of credit. If too much credit is created, the central bank makes refinancing more expensive for the banks by raising the key interest rate or by withholding money needed for refinancing.
As a consequence, the level of creditworthiness of customers does not determine how much credit banks hand out. Rather, relative creditworthiness determines who gets how much of the total credit volume that the central bank allows and who goes away empty-handed.
If this is the case, then the question arises: if some previously disadvantaged people are better off by being made transparent and thus having their credit risk is reduced, who is made worse off?
This question was recently answered by Andreas Fuster from the Swiss Finance Institute, Paul Goldsmith-Pinkham from the Yale School of Management and Tarun Ramadorai from Imperial College London in the leading economics journal Journal of Finance. For the paper “Predictably Unequal? The Effects of Machine Learning on Credit Markets“, they examined the question by theoretic modelling as well as with a simulation and with real data.
The result is: the main winners are those who have already been able to obtain credit relatively easily and cheaply. For them, credit interest rates are falling particularly steeply.
Among the winners are also some of those who have not been served with loans in the past, but who can now obtain loans from the formal sector. These loans come at relatively high interest rates, but that is better than being dependent on even more expensive informal money lenders and loan sharks. This is the group on which the advocates of cash abolition and data striptease chose to focus their attention on entirely.
The losers are those for whom (relative) credit conditions deteriorate because of better data on what they do and have. These are mainly people who had access to the formal financial sector before, but who had to pay relatively high interest rates and were restricted in the size of the loans they could get, because they were considered only marginally creditworthy.
As some people from this group are advancing to a group of higher creditworthiness due to better data, the average creditworthiness of the people remaining in this basket deteriorates. They have to pay even higher interest rates and become even more credit restricted.
The few small entrepreneurs and private borrowers who are helped by what is called financial inclusion are thus outnumbered by many small entrepreneurs and poor households for whom credit availability and interest rates deteriorate even further.
Thus, financial inclusion is not a valid argument for cash elimination or total surveillance.
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