The search for alpha can take us to some unusual places -- perhaps none more so than the 13th century works of Thomas Aquinas. His philosophical maxim, "Finitum non capax infiniti" -- "The finite cannot comprehend the infinite" -- makes a compelling argument for an entirely undifferentiated source of alpha: artificial intelligence. To apply this postulate to AI: While there are some types of AI that humans can comprehend, there are others that, because of their complexity and high dimensionality, are beyond the ken of human intelligence. There are clear signs that we have reached a tipping point where certain types of AI have surpassed the human mind: The finite (humans) cannot comprehend the infinite (advanced AI). Yet because of a deep-seated industry bias that investment results must be explainable, investors have been slow to accept the superhuman capabilities of advanced AI and, as a result, are failing to consider unique sources of alpha that could provide better investment outcomes.

Digital technologies are transforming wealth management. You needn't go to a bank or an investment advisory firm any more to figure out where you could get the best returns on your money. Just log into a wealth tech app. It's hassle-free, involves a lot less paperwork, has lower entry fee, and is much more intuitive than traditional processes. Startups in India have not only built apps with great user interfaces, but they are also creating algorithms and data science techniques to automate the entire investment process.

It's not just salespeople, traders, compliance professionals and people formatting pitchbooks who risk losing their banking jobs to technology. It turns out that private equity professionals do too. A new study by a professor at one of France's top finance universities explains how. Professor Thomas Åstebro at Paris-based HEC says private equity firms are using artificial intelligence (AI) to push the limits of human cognition and to support decision-making. He found that funds that have embraced AI are using decision support systems (DSS) across the investment decision-making process, including to source potential targets for investments before rivals.

At the center of the growing digital economy is data. Data is to the 21st century what oil was to the 20th century. In every industry, it are the companies that can use data effectively that succeed. And investing is no different. In their search for alpha generating ideas, investment managers are increasingly turning to sources of alternative financial data. But what is alternative data and how does it give fund managers an edge? The returns generated by investors can be classified as either alpha or beta.

Artificial Intelligence, robotics and automation are all themes which are becoming more prevalent within today's society, and for investors, certainly have a lot of potential. We do not yet fully understand and are unable to predict the true impact of these technological advancements, yet the speed at which business and operational transformation is taking place via the implementation of these digital technologies is staggering. Artificial intelligence (AI) is a branch of computer science which is allowing companies to move to a new standard of analysing data and helping them to garner more value from their assets, both physical and digital. By utilising rapidly growing datasets, businesses are able to drive innovation, increase efficiency and empower this data to generate societal and corporate profits. Robotics have been around for some time with UNIMATE being the first robot to be used on a production line in 1962.

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.

AI is making strides at many levels in the world of investment management. Investors may already be riding the wave of artificial intelligence, unaware of the many ways they've been integrated. There are three main levels where AI is making a mark, says Amit Gupta, a managing director in Accenture's capital market industry group. At the first level, firms are using AI in back-office administrative tasks like net asset value calculations, reconciliation, settlement operations. At the second level, they use it in front-office tasks like client targeting and management, profiling of clients, personalization of service.

The artificial segmentation of an investment management process into a workflow with silos of offline human operators can restrict silos from collectively and adaptively pursuing a unified optimal investment goal. To meet the investor's objectives, an online algorithm can provide an explicit incremental approach that makes sequential updates as data arrives at the process level. This is in stark contrast to offline (or batch) processes that are focused on making component level decisions prior to process level integration. Here we present and report results for an integrated, and online framework for algorithmic portfolio management. This article provides a workflow that can in-turn be embedded into a process level learning framework. The workflow can be enhanced to refine signal generation and asset-class evolution and definitions. Our results confirm that we can use our framework in conjunction with resampling methods to outperform naive market capitalisation benchmarks while making clear the extent of back-test over-fitting. We consider such an online update framework to be a crucial step towards developing intelligent portfolio selection algorithms that integrate financial theory, investor views, and data analysis with process-level learning.

We have witnessed a permanent shift in the role that data and technology are playing in investment decision-making. Idea generation techniques that had mainly been seen as emerging or experimental are now increasingly being adopted as mainstream. However, one of the biggest challenges for asset managers is how to incorporate, assimilate and integrate many of these techniques into the daily investment processes of the various investment teams. Regardless of the approach taken, data and how it is integrated and analyzed is going to play an increasingly pivotal role across all investment strategies. I will touch upon some key themes in this blog, but will go into more detail in a series to follow.

The AI Pioneers in Investment Management report from CFA Institute explores global best practices in the application of artificial intelligence (AI) and big data technology in the investment process. Since its launch last year, the report has inspired various compelling inquiries from readers and event participants that are worth addressing. Below are some of the frequently asked questions (FAQs) along with my responses. Please continue to send us your queries and comments by email or in the comments section below, and I will be sure to share and answer those that could benefit the wider audience. We believe an organization's competencies in investments and technology are complementary rather than competing.