Why Institutional Investors Need Advanced AI | Hacker Noon

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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 investment models can meet the speed and scale requirements of the current financial markets. Simply put, AI investment systems are designed to replicate the decision-making process of human portfolio managers at a high frequency. For now, there is little or no zest to experiment and implement deep learning AI systems that hunt data, interpret it, adapt to market changes, and execute investment decisions independently. 

Conservatives often argue that capital markets are too complex and random for AI to navigate without the input of human judgment. While this argument has some merits, it fails to appreciate the worldview of AI and how its perception of complexity is different from ours. If sophisticated deep learning systems cannot identify patterns that fit into a particular market scenario, what is the guarantee that traditional investment strategies would? 

Also, some argue that the unprecedented and unpredictable impacts of covid on capital markets highlight the limitations of ML-based quantitative financial models that heavily depend on historical data. Interestingly, traditional quant models also predict market movements by scouring through historical data. Hence, when it comes to uncharted market conditions, it is hard to navigate unscathed, irrespective of the type of analytics and prediction system in play. 

Ultimately, advanced AI models are safer bets because they not only certify the scale and speed required by institutional investors but also go a step further to eliminate human errors, adapt to changing market conditions, and manage risks.

Is AI Any Good In Emerging Markets Like DeFi?

Platforms such as DeFi Finance are expanding the functionalities of DeFi to enhance automation and capitalize on the distributed and open nature of automated market makers. It has taken a regulated approach to AMM and created a sustainable environment where institutional investors, DeFi, and advanced AI predictive analytics can thrive together. The novelty of this solution lies in its systemic response to the prevalent limitations of decentralized exchanges, including instability and an unregulated market.  

While there is already a high usage of smart techniques to track and take advantage of mathematical models of AMMs and the arbitrage opportunities they provide, self-running and deep learning techniques are not widely used. This is due to the nascent nature of the ecosystem, which translates to a limited data pool from which ML prediction systems can feed off. 

There is also the high uncertainty resulting from the unregulated state of the DeFi market. The combination of these two limitations has restricted the operations of institutional investors in this emerging financial landscape. However, thanks to the hybrid model of DeFi Finance, Rocket Vault, and Gain Dao, there is a way around these barriers. 

Notably, these solutions have opted to fuse elements of traditional finance and DeFi such that institutional and mainstream investors can fully explore the opportunities of open finance with conventional investment models, particularly AI-powered quantitative techniques. As a result of the emergence of hybrid finance, institutional investors’ use of AI is no longer constricted by insufficient high-quality historical on-chain data. 

It is only a matter of time before institutional investors start to integrate the analytics capability of AI and the automation-inclined nature of blockchain. The possibility of bringing AI to DeFi promises to establish machine learning as a core component of the decentralized finance market. 

Regardless of the type of market in question, AI unlocks new trading and investment opportunities while reducing risks significantly. Although there is an apparent reluctance to trust the efficiency of this technology completely, the proliferation of advanced AI systems that are void of human components is inevitable.

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