Buy, buy, buy! We want to buy even we cannot afford it. Therefore if you need money you should ask somebody to lend you. This ”somebody” (whoever she is, your friend
or a bank) has a single question: ”May I trust the borrower will repay the loan?” In a world when people did not live in the ocean of data the potential lenders characterized qualitatively the borrowers. “He looks a nice, reliable guy, well, I think he will repay… in addition, he promised to pay this and this percent of interest”. Owners of corner grocery stores developed skills for century to classify clients, as reliable and not reliable. I find interesting, but not surprising that the oldest credit reporting agency in the USA emerged from the grocery business. Cator Woolford was a a grocer in Chattanooga, Tennessee. He collected data from his customers, produced a book, and sold copies of the book to the local Retail Grocer’s Association. Based on his success, together with his younger brother Guy, a lawyer, opened in Atlanta a very small business, they called ”Retail Credit Company”. This small business evolved what we call now Equifax Inc. (one of the three giant consumer credit bureaus, the other two are Experian, and TransUnion), which
collects and process information over 800 million individual consumers.
When people should judge other people’s character, it is truly truly subjective. Granting or denying loans or credit requests was very from being objective, and age-, gender- or race-based discriminations happened again and again. To help the decision makers by quantitative analysis was a big step towards objectivity. William R Fair (1923-1996) and Earl Isaac (1921-1983) started to build mathematical models for predicting the behaviour of the potential borrowers. An initial version of a Credit Application Scoring Algorithm was introduced in 1958. This algorithm generated three possible behaviors: the borrower will pay on time, will pay with delay, or not pay at all. The Fair Isaac Corporation has been established, and developed and algorithm and software to calculate what became the famous/infamous FICO score.
Stay tuned! (How a credit score is calculated, and how objective it is?}
From RANKING to REPAIR! 
Something that I’ve been thinking about a lot since class on Tuesday is the extent to which biases have been encoded within credit scoring algorithms as opposed to dissipating like it was thought they would. For example, credit scoring algorithms do not include race as an explicit factor in the generation of a credit score, but they do take into account things like prior access to credit and previous borrowing history. Given the fact that income and wealth inequality have been systematically enabled through federal policies that have denied minorities access to credit, is it truly the case that race isn’t a significant factor in credit scoring algorithms? Since reading Cathy O’Neil’s Weapons of Math Destruction, I have been thinking a lot about the ways in which supposedly ignored characteristics still appear within other data sets, given that factors like race, age, sex, etc. can reasonably be predicted by the occurrences of other factors. I am curious to see if there is any data on whether or not credit scores have racial, ethnic, age, sex, etc. bias.
Furthermore, I am very interested in societies like China, where credit scores are moving away from purely monetary/financial factors and are beginning to encompass things like relationships, spending habits, occupation, etc. and the potential for even harsher biases to become encoded within these algorithms and reinforce preexisting inequalities.
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