Coronavirus » Data and AI helping prep finance function for black swans

Data and AI helping prep finance function for black swans

Major unexpected events can wreck havoc on the finance function, but there are measures to mitigate future damage

Data analysis and artificial intelligence (AI) use can support early detection of so-called “black swan” events, according to tech vendors.

“Sophisticated AI algorithms can use information from past events to make accurate predictions, identify trends and analyse current market shifts,” Daniele Grassi, CEO of Axyon AI said over email.

“At the same time, AI can be used to identify anomalies in the behaviour of markets, spotting deep-current changes early on. And the more data to hand, the better the results from the AI.”

“Of course, these steps alone won’t negate the impact of an unexpected event which completely changes the investment landscape in an instant,” Grassi continued. “However, they will provide an enhanced understanding of the market, so that firms can create models of the potential repercussions of major geopolitical, financial, or environmental events.”

Analysing data from previous black swans, such as the September 11 attacks and the 2008 recession, can also offer insight into what to expect from the markets. However, this is not always accurate; Goldman Sachs predicted that the US economy was recession-proof, which has been proven false.

Simon Bittlestone, CEO of financial analytics firm Metapraxis, said via email that finance teams should use data to build a scenario model of their business, allowing them to see the impact of different decisions.

“With a scenario model capability, businesses will have the mechanism and the data to hand to identify the right actions to take immediately and, most importantly, communicate the decision clearly and effectively across the business,” Bittlestone said. “This can be the difference between acting in time to save the business and failing.”

In context of the current pandemic, this means extrapolating as much information as possible from data and AI programmes, even after the black swan event has occurred.

In particular, generative adversarial networks (GANs) can be helpful to stress test the market, which in turn can support investors’ decisions on pumping cash into the company. However, none of these will provide absolute guarantees to avoid black swans, particularly when it comes to keeping a company afloat.

“If I look at a bank, or any other corporation, and they go down—they go bust primarly because they don’t have cash,” Xavier Pujos, managing partner at Sionic says. “Cash in the black swan environment, or in any extreme scenario, becomes key.”

For companies operating on capital, Pujos says there are new considerations for CFOs to take on board when navigating black swans. In particular, they should be learning from stress tests, investigating cash resources and securing cash wherever possible.

“There’s always lessons to be learned, and there’s always things to be analysed and information to be retained,” Pujos says. “I think the very extreme scenario has to be taken with a pinch of salt—they are not going to reproduce in the same way.”

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