Machine learning is a facet of Artificial Intelligence (AI) and a data analysis method based on the idea that computer systems and machines can learn from data by identifying patterns and making decisions without excessive human intervention. The global machine learning market is estimated to grow from USD 1.41 Billion in 2017 to USD 8.81 Billion by 2022. Machine learning can be used in diverse industries and the finance sector is one of those. To precisely enable financial establishments to identify suspicious activity and prevent fraud is again the functionality of machine learning. The shopping recommendations you receive based on your online activity and purchase history result from machine learning.

A dedicated writer and digital evangelist. Are you aware of how the buying and selling of stocks were carried out when there was no internet or computers? Back then, stock exchanges had active trading floors filled with brokers and traders. To make a trade or a purchase, they had to shout or use hand signals to alert others about their buy or sell orders. It looked a whole lot like an auction at a fish market today. But then came computers and the internet to change the game completely.

In 2019, the world fretted that algorithms now know us better than we know ourselves. No concept captures this better than surveillance capitalism, a term coined by American writer Shoshana Zuboff to describe a bleak new era in which the likes of Facebook and Google provide popular services while their algorithms hawk our digital traces. Surprisingly, Zuboff's concern doesn't extend to the algorithms in financial markets that have replaced many of the humans on trading floors. Automated algorithmic trading took off around the beginning of the 21st century, first in the US but soon in Europe as well. One important driver was high-frequency trading, which runs at blinding speeds, down to billionths of a second.

Around the world, and particularly in Australia, energy traders are trying to get their minds, and their algorithms, around the complexities of trading in variable wind and solar projects and super-fast battery storage installations. Maybe they should give up now, and hand it over to artificial intelligence. US-based software-as-a-service platform provider AMS says automated trading systems for batteries and renewable energy projects using deep learning and artificial intelligence can out-compete the best human traders, by around a factor of five. With the deployment of large-scale energy storage systems occurring at an ever-increasing rate, this is critical – not just for the ability to make money out of the markets, but also for the ongoing operation of the National Electricity Market itself. Traditional generators only need to maximise their generation during periods of sufficiently high energy prices.

The public perception of stock exchanges remains little changed since the Big Bang deregulated the financial markets. Anytime a trading floor is depicted in a film or TV series, you can expect to see a group of men frantically waving their arms and shouting at a bank of screens showing the ups and downs of share prices. But the reality is different. Around three-quarters of exchanges on the NYSE and Nasdaq are now carried out by algorithms - computer programs designed to follow a particular set of rules - and not human traders. The last stock exchange in Scotland closed in 1973.

Perceptions around AI in finance are changing, as skepticism gives way to Fear of Missing Out among value managers. Here Kris Longmore of Quantify Partners Pty Ltd looks at how the robots are changing the way we look for alpha. JP Morgan's recently released 280-page report Big Data and AI Strategies – Machine Learning and Alternative Data Approaches to Investing paints a picture of a future in which alpha is generated from data sources like social media, satellite imagery and machine-classified company filings and news releases. Get the entire 10-part series on Ray Dalio in PDF. Save it to your desktop, read it on your tablet, or email to your colleagues.

Stock predictions made by machine learning are being deployed by a select group of hedge funds that are betting that the technology used to make facial recognition systems can also beat human investors in the market. Computers have been used in the stock market for decades to outrun human traders because of their ability to make thousands of trades a second. More recently, algorithmic trading has programmed computers to buy or sell stocks the instant certain criteria is met, such as when a stock suddenly becomes cheaper in one market than in another -- a trade known as arbitrage. Machine learning, an offshoot of studies into artificial intelligence, takes the stock trading process a giant step forward. Pouring over millions of data points from newspapers to TV shows, these AI programs actually learn and improve their stock predictions without human interaction.

At its height back in 2000, the US cash equities trading desk at Goldman Sachs's New York headquarters employed over 600 traders who bought and sold stock on the orders of the investment bank's largest clients. Today there are just two left and automated trading programs have taken over the rest of the work, supported by 200 computer engineers explains Marty Chavez, the company's deputy CFO and former CIO at a Harvard symposium on automations impact on the financial services sector held last month. The experience of Goldman's New York traders is just one early example of the rise of automation, that's increasingly taking over Wall Street, that began with the rise in computerised trading, but which has accelerated over the past five years, moving into more areas of finance that were once dominated by humans. Now, Chavez, who will become Goldman's new CFO in April, says other areas of trading such as currencies and even investment banking are all moving in the same direction and it's bad news for the banks thousands of employees. According to Coalition, a UK firm that tracks the industry, nearly 45 percent of all trading is now done electronically, and not to be left out Wall Street's hastily rolling out new automation technology, such as artificial intelligence (AI) to replace its high earners.

Yuji Honkawa knew the humans were losing by April 2010. No matter how fast he sent orders to be filled at the Tokyo Stock Exchange, a machine beat him. Unemployed after 20 years dealing in equities at seven different brokerages, the 47-year-old Honkawa watched as the market sped up and automated traders went from generating 10 percent of orders at the start of 2010 to as much as 72 percent last year. Among the men and women he competed with to get prices for clients, 80 percent have left the industry, he estimates. "It's kind of like the Terminator," Honkawa said in an interview.

This article was written By Henri Waelbroeck, Head of Research at Portware. Execution management has matured from laying the foundation for electronification by automating repetitive workflows to extracting progressively more value from the infrastructure as it evolves. Each generation in execution management technology has pushed automation one level higher in the decision hierarchy. The FM London Summit is almost here. Today, we are seeing the dawning of the next generation of execution management systems: one where AI works with the trader to combine the best of quantitative optimization (at speed and at scale) and the trader's domain knowledge.