The heady world of finance has been turned on its head in the decade since the global financial crisis (GFC) in 2007–8. Lehman Brothers, a leading investment bank, collapsed, its rival Bear Stearns was sold, and many others were so close to bankruptcy that only controversially generous bailout packages could save them. As familiar names from the banking world struggled, China stepped into the limelight, becoming the world’s biggest source of capital. Today four of the world’s top five banks are Chinese. Tech giants such as Apple, Amazon and Facebook have become the most valuable companies and attract the best talent. Why suit up for Goldman Sachs when you could wear flip-flops and t-shirts at Facebook? Financial technology (fintech) companies, from Ant Financial in China to Square in the United States, have broken into the consumer-banking space like a wrecking ball.
From asset management to banking and insurance, AI is transforming the way financial institutions make decisions. Consumer-facing interfaces can understand a customer’s mood over the phone, institutions can monitor economic activity using satellite imagery, and machines are becoming as capable as humans in identifying investment opportunities. Yet, financial institutions in Asia are still behind the curve. They lack the basic digital infrastructure to collect the data that empowers AI, and struggle to build the right digital models. What are the opportunities for businesses to achieve competitive advantage in AI? How will machine learning continue to disrupt the sector through to 2020? And how can regulators prepare for decision-making machines?