Three big questions about AI in financial services
To ride the rising wave of AI, financial services companies will have to navigate evolving standards, regulations and risk dynamics—particularly regarding data rights, algorithmic accountability and cybersecurity. The success of artificial intelligence (AI) algorithms hinges on the ability to gain easy access to the right kind of data in sufficient volume. Put more simply, AI depends on good data. Even Google—which is famous for the pioneering work in AI that underpins its standard-setting search-based advertising business—makes no bones about the critical role of data in AI. Peter Norvig, Google’s director of research, has said: “We don’t have better algorithms, we just have more data.” Companies increasingly realize that data is critical to their success—and they are paying striking sums to acquire it. Microsoft’s US$26 billion purchase of the enterprise social network LinkedIn is a prime example. But other technology companies are also seeking to acquire data-related assets, typically to acquire more than just identity-linked information from social media sources by focusing instead on vast troves of anonymized consumer data. Think, for example, of Oracle pursuing an M&A-led strategy for its Oracle Data Cloud data aggregation service, or IBM buying, within the past two years, both The Weather Company and Truven…
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Three big questions about AI in financial services
