… Financial companies have the option of using data-guzzling technologies that make the observation of shopping habits look downright primitive. A plethora of information gathered from social media, digital data brokers and online trails can be used to mathematically determine the creditworthiness of individuals, or to market products specifically targeted to them.
The degree to which such algorithms are utilised by mainstream banks and credit card companies is unclear, as are their inputs, calculations and the resulting scores. While many types of data-driven algorithms have been criticised for opacity and intrusiveness, the use of digital scorecards in finance raises additional issues of fairness. Using such information to make predictions about borrowers can, critics say, become self-fulfilling, hardening the lines between the wealthy and poor by denying credit to those who are already associated with not having access to it.
“You can get in a death spiral simply by making one wrong move, when algorithms amplify a bad data point and cause cascading effects,” says Frank Pasquale, a professor of law at University of Maryland and author of a book on algorithms called The Black Box Society.
I’ve said before that I am incredibly proud of this Financial Times piece exploring the impact of big data on finance and equality. Researching this kind of topic is challenging because details on the use of big data remain murky – even more so when it comes to banks and financial companies. For that reason, much of the discussion remains theoretical, although it’s hard not to believe that this is the direction we are heading when you read that Google – a company notorious for using big data to personalise ads and search engine results in the name of advertising dollars- is now trialling money transfers. The British bank Barclays has reportedly also begun selling aggregated customer data to third-party companies.
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