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Predictive analytics and machine learning in finance

As the Future Finance Forum continues into the afternoon of Day 2 at FundForum in Berlin the audience could be lured into thinking they were about to sit through a sober discussion about big data and analytics cutting through the noise of multiple and disparate data points.

However, when Luke Ellis, president of one of the world’s largest hedge funds, the Man Group, starts the talks saying he is frightened almost every time he gets in a cab in a major city and he can’t wait until “every car in every city is a driverless car” it is hard to pay attention to sober Oxford-based researchers showing the audience a better way to read charts and graphs.

“I would be much happier having a car driving me,” says Ellis. However, taxis aren’t the only old world occupation on Ellis’ hit list. He went on to say that the Man Group’s greatest expense are the “expensive fund managers” inside the building. And he was looking forward to the day when they will be replaced by computers. “Believing that tech can only do the simple and boring is getting the wrong end of the stick,” says Ellis. “Tech can do the really interesting stuff.

Panels before this at FundForum tended to repeat the party line that so-called robo-advisors were merely ‘a part’ of an overall financial management package – being too simple to compete with the fair superior advice and management led by humans. Pity the poor fund managers at Man Group for there are those who are counting the days until the robots take your job.

Strategies around using data to make better and more informed decisions have been in the works in the financial services industry for some time. However, the tipping point, says Lars Hamberg, head of big data analytics & fund selection, AFAM says that the breakthrough happened “when computers started learning how to read.”

"Pity the poor fund managers at Man Group for there are those who are counting the days until the robots take your job."

Hamberg points to earlier experiments with using social media sentient analysis - so-called Twitter hedge funds - which had little success causing many in the industry to “give up” on taking advantage of this data.

Hamberg laments why the “big media companies” like Google are the front runners in behavioural analytics and big data when “banks know everything about us”. Banks have been “filing away data on us for years and they do nothing with it,” he complains.

However, the notion of big data analytics and machine learning only existing in a black box may have something to do with the resistance felt by many big financial firms. Dr. Anthony Ledford, chief scientist, Man AHL Research Laboratory, Oxford University thinks the answer is to “open up the black box” and to show how much more robust and comprehensive analytics using machine learning can be compared to traditional modelling. Ledford illustrated these comparisons with various slides during his talk.

But the core to buying into the new robot future may not be headlining grabbing statements about the desire for driverless cars (or manager-less funds) but in showing how it works in a business context.

Valter Trevisiani, group chief insurance officer, Generali Group took the audience on a customer journey through car insurance, smart home environments and health. As an insurer, gathering the right type of customer data, whether that be how safe someone drives a car or takes care of their body, is the key to providing tailored insurance products to customers.

According to Trevisiano, “Behavioural-based insurance products are the future.”

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