Artificial intelligence (AI) and machine learning techniques are finding their way into financial services. Ranging from operational efficiencies to more effective detection of fraud and money laundering, firms are embracing techniques that find patterns, learn from them and can subsequently act on signals coming out of large volumes of data. According to Martijn Groot, VP Marketing and Strategy, Asset Control, the most promising, and potentially lucrative, use cases are in investment management though. Among the groups that benefit most are hedge fund managers and other active investors who increasingly rely on AI and machine learning to analyse large data sets for actionable signals that support a faster; better-informed decision-making process. Helping this trend is the increased availability of data sets that provide additional colour and that complement the typical market data feeds from aggregators, such as Bloomberg or Refinitiv, range from data gathered through web scraping, textual analysis of news, social media and earnings calls. Data is also gathered through transactional information from credit card data, email receipts and point of sale (POS) data.

Get the Deloitte Insights app. Since February 2020, there has been a dramatic shift in the operating environment of financial markets, with increased volatility, repricing of assets, and transitions of favored asset classes. Uncertainty abounds for investment managers. According to one hypothetical stress scenario, individual managers may have seen assets under management fluctuate by up to one-third in the United States as outflows and valuation changes have affected many during the pandemic.1 Even before the emergence of COVID-19, the situation for investment managers appeared ripe for change.

Some industry experts argue that machine learning (ML) will reverse an increasing trend toward passive investment funds. But although ML offers new tools that could help active investors outperform the indexes, it is unclear whether it will deliver a sustainable business model for active asset managers. Let's start with the positives A form of artificial intelligence, ML enables powerful algorithms to analyze large data sets in order make predictions against defined goals. Instead of precisely following instructions coded by humans, these algorithms self-adjust through a process of trial and error to produce increasingly more accurate prescriptions as more data comes in. ML is particularly adaptable to securities investing because the insights it garners can be acted on quickly and efficiently.

From 2010 heading this way the use of artificial intelligence (AI) by active managers has been increasing in a most absorbing manner. To use the Roman historian Suetonius, "AI investing is not going away." In a 2017 conference organised by J. P. Morgan, the bank asked 237 investors about big data and machine learning, and the resulting data found that "70 per cent thought that the importance of these tools (of AI) will gradually grow for all investors. And a further 23 per cent said they expected a revolution, with rapid changes to the investment landscape". But this investor interest with AI also signals a certain frustration with current active, and specifically quant, managers and the nascent promise shown by AI hedge funds.

Fintech Limeglass has launched to'atomise' research by using technology to tag reports in real-time so that they can be easily and quickly searched by the recipient at a granular level. Rowland Park, chief executive and co-founder of Limeglass, said in a report this week that the research market needs innovation as the majority of reports are still being consumed by the buy side as multiple page PDF and HTML documents sent by email, despite advances in technology. Park has more than 30 years' experience in the research industry and founded and grew start-ups IDEA Global and 4CAST, which focused on macroeconomics, policy and financial markets intelligence. Park wrote in the report that financial decision making rests on a three-legged stool – market data; breaking news; and research, which provides wider context for decision making. "The development of tools to better handle market data and breaking news have transformed, and continue to transform, the way activity in the financial markets is conducted," he added.

A top Air Force pilot who has been battling flight simulators since the 1980s was outgunned by an intelligent machine. To the chagrin of cab drivers and delivery boys, Domino's started testing self-driving cars. And exchange-traded funds, long seen as a disruptive technology by those in the industry, are displacing active managers. What these machines will do next--pick stocks, set up asset-allocation plans--was a running theme at the Morningstar ETF conference last week.

The robo analysts are here. Having demonstrated their analytical rigor and objectivity, are they set to overtake robo advisors in the race to transform wealth management? Today, the wealth management industry finds itself on the brink of a chasm. In the same way online trading disrupted the distribution of investment advice, big data analytics and machine learning will disrupt how financial advice and the research behind it is created. The way firms respond to the new technologies transforming how research is performed and investment advice is applied will determine their future success or failure.

Robots are predicted to steal the jobs of millions of workers across the world over the next few years, as technology replaces human shop assistants, teachers, accountants and potentially even taxi drivers. Artificial intelligence and automation are also expected to revolutionise the investment industry, especially in the area of robo-advice. This is where algorithms are used to suggest funds to investors, replacing some human financial advisers in the process. Less spoken about is the impact robotics and artificial intelligence could have on the role of the portfolio manager. But commentators believe many fund managers' jobs could come under threat as developments are made in technology.

Research released late last year suggested that almost two thirds (62%) of a survey of 90 German institutional investors predict greater usage of artificial intelligence (AI) for short-term decision-making, and 17% for medium-term investment decisions. Universal Investments, the asset manager behind the research, said that "the industry's future thus certainly appears to be closely linked with the strategic use of AI". Of course, "greater usage" could be a marginal rise, but I think that an increase in the use of AI is almost inevitable. It's certainly true that the cost of investment management that can be done by a machine has reduced, to the extent that human active managers now need to really deliver value. AI is also not purely the domain of the passive fund; active managers will increasingly deploy these tools to supplement and support their decision support.