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Are You Creditworthy? The Algorithm Will Decide.

#artificialintelligence

Money2020, the largest finance tradeshow in the world, takes place each year in the Venetian Hotel in Las Vegas. At a recent gathering, above the din of slot machines on the casino floor downstairs, cryptocurrency startups pitched their latest coin offerings, while on the main stage, PayPal President and CEO Dan Schulman made an impassioned speech to thousands about the globe's working poor and their need for access to banking and credit. The future, according to PayPal and many other companies, is algorithmic credit scoring, where payments and social media data coupled to machine learning will make lending decisions that another enthusiast argues are "better at picking people than people could ever be." There's now a whiff of a hope that big data might finally shore up the risky business of consumer credit. Credit in China is now in the hands of a company called Alipay, which uses thousands of consumer data points -- including what they purchase, what type of phone they use, what augmented reality games they play, and their friends on social media -- to determine a credit score.



The $100 Trillion Opportunity: The Race To Provide Banking To The World's Poor

#artificialintelligence

Two years ago, Amylene Dingle lived with her husband and 7-year-old daughter in Payatas, an impoverished Manila neighborhood with the largest open dump site in the Philippines. Her husband worked on the security staff in a government building, earning 4,000 pesos a week, the equivalent of $80. She had always wanted to start a business, but she was unemployed, had no money saved, no credit history and couldn't get a credit card or a bank loan. Dingle's fortunes took a dramatic turn after she responded to a Facebook ad for Tala, a Santa Monica-based startup that makes small loans through a smartphone app. After granting Tala access to her phone, through which the app cleverly parses mobile data to assess a borrower's risk, she got a 30-day, $20 loan. She paid 15% interest and used the money to buy cold cuts, hamburgers and hot dogs. She marked them up 40% and sold them door-to-door, earning $4 in profit after paying back the interest and a small processing fee.