Frequently described as the world's most important number because it underpins trillions of dollars of transactions, the London interbank offered rate (Libor) has persisted until now despite a scandal that caused lasting reputational damage to the entire financial system. Libor is the key interest rate benchmark for mortgages, loans and contracts but it has been tainted since 2012 when it emerged that banks had misstated their Libor rate submissions, often in collusion, to make better returns. The controversy led to at least five traders going to jail in the UK, and US and UK regulators extracting penalties totalling about $10bn. Regulators want Libor phased out by December 31 2021, and banks are pivoting to alternative risk-free rates such as Sonia (sterling overnight interbank average rate). Its demise is already a headache for law firms and banking clients, which must examine hundreds of thousands of legal contracts containing references to the Libor rate and then rewrite and "repaper" them to ensure they include the new reference rates.

When the fintech sector moves to using artificial intelligence on an industrial scale, the winners are unlikely to be today's big tech companies. Think startups that you've probably not heard of before. Or at least that's according to Eigen Technologies -- the UK-based natural language processing startup, which today announced ING as a second, strategic financial services investor, joining Goldman Sachs. The amount of money in the deal is pretty modest, $5m, rounding up Eigen's Series B funding round to $42m. They are solving for a talking kitchen appliance." But the deal gives an insight on how the finance industry is planning to use technologies like natural language processing, the branch of artificial intelligence (AI) that allows machines to analyse written and spoken speech. Thank you for subscribing to the newsletter! Members of the Sifted community get deeper insights and introductions. So far, AI has mostly been a big tech, big data game. Algorithms have been taught to deal with unprocessed information in text, images, audio and video by training them on vast amounts of data. The more data you can access, the better your AI, making this a game dominated by the likes of Google, IBM, Facebook and Amazon. But a handful of small, nippy startups are finding ways to train AI with small data sets, an approach that works better for specialist and regulated businesses. "The traditional machine learning approach is to throw a lot of data at the problem, to give the programme 100,000 examples.