History of plastic cards development
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Modern payment cards encompass a bewildering array of consumer technologies, from credit and debit cards to stored-value and loyalty cards. But what unites all of these financial media is their connection to recordkeeping systems. Each swipe sends data hurtling through invisible infrastructures to verify accounts, record purchase details, exchange funds, and update balances. With payment cards, banks and merchants have been able to amass vast archives of transactional data. This information is a valuable asset in itself. It can be used for in-house data analytics programs or sold as marketing intelligence to third parties. This research examines the development of payment cards in the United States from the late 19th century to present, drawing attention to their fundamental relationship to identification, recordkeeping, and data mining. The history of payment cards, I argue, is not just a history of financial innovation and computing; it is also a history of Big Data and consumer surveillance. This history, moreover, provides insight into the growth of transactional data and the datafication of money in the digital economy.
In 2002, an executive at Canadian Tire, a Toronto-based national retailer, made some surprising discoveries about the store’s credit customers. Close analysis of the store’s card data revealed statistical correlations between how customers used their cards and how they paid their bills. Among other things, the executive learned that customers who purchased bird seed and protective felt pads for the feet of chairs tended to be reliable bill payers. By contrast, those who bought cheap motor oil and chrome skull auto accessories were more likely to miss payments. “If you show us what you buy,” the executive boasted to a New York Times reporter, “we can tell you who you are, maybe even better than you know yourself” (Duhigg, 2009). Several years later, marketing executives at Target, the American mass retailer, were making similar discoveries in a now infamous data-mining program. By linking the store’s guest identification numbers with purchase records, analysts were able to predict which shoppers were pregnant. Transactional data revealed correlations between pregnancy and a variety of purchases, including prenatal vitamin supplements, unscented lotion, and giant bags of cotton balls
