Gender bias in credit scoring AI? In recent weeks a number of Apple Card users in the US have been reporting that they and their partners have been allocated vastly different credit limits on the branded credit card, despite having the same income and credit score (see BBC article). Steve Wozniak, a co-founder of Apple, tweeted that his credit limit on the card was ten times higher than his wife’s, despite the couple having the same credit limit on all their other cards.
Earlier I wrote another post about predicting the spend of a single known customer. There is a related problem which is predicting the total spend of all your customers, or a sizeable segment of them.
Why do we need to predict customer spend? You may have read my previous post about customer churn prediction. Another similar problem that’s just as important as predicting lost customers, is predicting customers’ daily expenditure.
Long forms reduce conversion rates You may have had the experience of filling out a long form on a website. For example, creating an account to make a purchase, or applying for a job, or renewing your car insurance.
Why is face recognition everywhere? Face recognition technology has existed for quite some time, but until recently face analysis was not accurate enough for most purposes. Now it seems that face recognition is everywhere:
Click here to see a live online demo of the neural network forensic stylometry model described in this article. In 2013 JK Rowling, the author of the Harry Potter series, published a new detective novel under the pen name Robert Galbraith. She wanted to publish a book without the hype resulting from the success of the Harry Potter books.
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