The power of advanced AI lies in its apparent capability to thoroughly explore data, find recurring patterns, and make intelligent decisions at a high intensity. The whole essence of adopting AI-enabled techniques is to reduce human errors to the bare minimum. AI investment systems are designed to replicate the decision-making process of human portfolio managers. Traditional quant models also predict market movements by scouring through historical data. DeFi Finance, Rocket Vault, and Gain Dao have opted to fuse elements of traditional finance with AI-powered quantitative techniques. It is only a matter of time before institutional investors start to integrate the capability of AI and automation.

image

Brian Wallace Hacker Noon profile picture

@brianwallaceBrian Wallace

Founder @ NowSourcing. Contributor @ Hackernoon, Advisor @GoogleSmallBiz, Podcaster, infographics

Artificial intelligence and machine learning are not just buzzwords but critical building blocks for software, so much so that automated solutions are fast becoming fashionable. While we are experiencing a great deal of AI disruptions in several industries, the movement is facing a bit of resistance in the investment landscape.

Notably, institutional investors erroneously relegate AI to the status of helpmate to human intelligence. Very few asset management companies are ready to fully embrace AI and machine learning, or ML, systems without subjecting them to traditional quant models that have mostly restricted their effectiveness. In this article, we will explore the status of AI in the investment landscape and discuss how institutional investors could get more out of it.

The AI Dilemma

The power of advanced AI lies in its apparent capability to thoroughly explore data, find recurring patterns, and make intelligent decisions at a high intensity. If we go by this definition, then there is little or no room for human inputs. The whole essence of adopting AI-enabled techniques is to reduce human errors to the bare minimum. Therefore, it makes no sense to continue to cling to legacy methods.

Perhaps, we can link this resistance to the universal belief that it is impossible to overhaul human elements from investing techniques. It is commonly believed among the so-called ML quantitative finance pioneers that there is a long way to go before advanced AI can emerge as an independent primary portfolio management system.

At best, AI is predominantly being used to enhance the human components of quantitative finance so that traditional…

Continue reading: https://hackernoon.com/why-institutional-investors-need-advanced-ai-7xo37pd?source=rss

Source: hackernoon.com