Introduction

Technology has become part of almost every aspect in our lives nowadays. Artificial Intelligence (AI) which plays a major role in this, has evolved dramatically in recent decades. Google Maps uses AI to dynamically learn traffic patterns and create efficient routes, smart phones use AI to recognize faces and verbal commands, AI enables efficient spam filters in email programs, smart assistants such as Alexa, and recommendation engines. These are just some obvious examples of familiar technologies that leverage AI’s capabilities. AI applications can be found in virtually every industry today, from marketing to healthcare to finance.

What is Artificial intelligence?

Artificial intelligence (AI) generally refers to the ability of machines to exhibit human-like intelligence and a degree of autonomous learning. An example would be machines solving a problem without the use of hard-coded software containing detailed instructions.

Artificial intelligence is a suite of technologies, enabled by adaptive predictive power and exhibiting some degree of autonomous learning, that dramatically advance the ability to:

  • Recognize patterns
  • Anticipate future events
  • Create good rules
  • Make good decisions
  • Communicate with other people

To put it another way, AI is a suite of technologies and capabilities which, when adopted, can enable firms to dramatically deliver new kinds of value and reshape operating models.

The Evolution

Artificial intelligence can be categorized into three basic stages of development.

Basic AI or Artificial Narrow Intelligence (ANI) is limited in scope and restricted to just one functional area. AlphaGo, a computer program that plays the board game Go, is an example.

Advanced AI or Artificial General Intelligence (AGI) is advanced and usually covers more than one field, such as power of reasoning, abstract thinking, or problem solving on par with human adults.

Autonomous AI or Artificial Super Intelligence (ASI) is the final stage of intelligence expansion in which AI surpasses human intelligence across all fields. This stage of AI is not expected to be fully developed for several decades.

Artificial intelligence on Investment

Over the past 20 years, investment management has undergone an aggregation revolution, where advances in information technology have sped up the depth, width and speed of information reaching investors. In the next five years, investment management will go through an analytical revolution, AI and investing will come together and revolutionise the way that investment information is analysed, packaged and presented to investors.

This will change the face of investment management, with professional investors able to make informed investment decisions faster and will for the first time give private investors access to the same advanced stock selection and portfolio construction tools as the professionals.

The impact

The Artificial Intelligence can now bring a whole new perspective to investment decision making. The power of AI is its ability to tirelessly look for, combine, and distil signals from masses of noisy data already available in the marketplace. By bringing out ‘interesting’ insights, whether to confirm or enhance a suspected salient point or by identifying one that might have been overlooked otherwise, AI is the humble ‘idiot-savant’ that can usefully take on the tedious data-intensive work that humans are not best suited for.

By deploying AI to analyse investment information, the investor can perform advanced stock screening, instantly identifying and triaging the handful of stock opportunities within global markets that both fit one’s investment preference and carry a higher than average chance of outperforming in the future. This can happen instantly, drawing from sources ranging from financial information providers (e.g. Reuters), to official filings (e.g. the SEC’s Edgar database), to big data aggregators (e.g. Google), to personal data (e.g. record of past trade decisions).

For investors

  • Risk Mitigation: With the AI enabled services, the investors will be able to visualise and control real-time, the active risks embedded in a portfolio, as well as access suggestions to mitigate unwanted exposures, all without the need for a PhD in Maths. Investors will now be able to easily target and monitor the specific risks they know something about and intend to profit from (e.g. a turnaround at a manufacturing firm), without unknowingly over-exposing their portfolio to sources of uncertainty they know nothing about (e.g. the result of an election or future levels of interbank lending rates). The key benefit of such risk control and transparency is better returns and more learning.
  • Real time alerts on investments: The AI engine may alert the investor, often before most of the price decline, to consider selling all or part of a holding, or reducing exposure to a risk factor, or indeed looking into the recent news flow of a company for possible reasons to turn more bearish (or bullish) on the stock. Such a development vastly contributes to reducing investor biases with less vigilance required (less decision fatigue), less false alarms (less over-trading), and more balanced perspectives (less overconfidence), all contributing to better investor performance.
  • Simple delivery of complex data source: Modern user interface and data analytics facilitate the delivery of complex combinations of data sources, and cloud-based technology makes it cheap to do so. All the above can also be applied to inform investors’ fund selection and performance monitoring, so rather than relying on top-down backward-looking analysis, AI can deliver instant insights based on a bottom-up analysis of the funds’ holdings

For Investment companies

  • Relationship mapping: Identifying non-intuitive relationships between securities and market indicators.
  • Alternative datasets: Analyzing alternative data such as weather forecasts and container ship movements, monitoring search engines for words on specific topics to structure hedging strategies.
  • Automated insight: Reading earnings transcripts to assess management sentiment.
  • Growth opportunities: Using corporate website traffic to gauge future growth along with clients’ behavioural patterns.
  • Client outreach: Smart client outreach and demand generation via analytics, using alternative data sources such as social media data.
  • Operations intelligence: Using machine learning to automate functions.
  • Powering risk performance: AI-based algorithms and machine learning to monitor for suspicious transactions, and trigger response protocols.
  • Reporting and servicing: generating reporting for clients, portfolio and risk commentary, and marketing material using natural language processing.
  • On-demand reporting: Chat bots and machine learning used to respond to employee or investor queries, generating management reporting on-demand.
  • Employee insights: monitor employee conduct risk and employee morale

How does AI help Asset Management companies?

AI in Investment Management is reshaping distribution and enabling firms to extend their distribution models into new markets and customer segments which have been traditionally underserved. AI is also facilitating scaled distribution of customized products and tailored client interactions. AI can help firms fundamentally enhance their existing models and time-to-market. The AI helps the Asset management companies in the following ways:

  • Seamless client experience: Leverage digital technologies to deliver a personalized, consistent and efficient client experience, customized by tier and segment.

AI is changing how financial institutions attract and retain customers, and through this, offers the opportunity for firms to innovate and enhance the investor journey, from segmentation and outreach through to content distribution and reporting. What is certain now is that investment management firms can no longer rely solely on price and outperformance to attract investors. Firms that adapt their products and integrate AI, data, and analytics into their service delivery models will be better placed to optimize and execute their product and content distribution strategies.

  • Marketing and sales optimization: Empower business development and relationship management sales teams with the insights and tools they need to more efficiently engage their clients to win and/or retain flows.

AI can help firms fundamentally enhance their existing models and time-to-market. Customer relationship management (CRM) tools can provide the workflow and interaction with the sales team. AI-enabled analytics can provide differentiated insights and actions. Together, these tools can equip sales teams with easier and quicker access to relevant content.

  • Content effectiveness: Produce and distribute relevant, high-quality and timely content on demand to internal and external consumers

Conclusion

The AI analytical revolution has the potential to open up direct investing to a whole new audience, both young and old, who are able to take control with confidence and package up their own investment portfolios with a much greater understanding of risk and return, simply through their Smartphone.

Going forward the AI for Investments will move more closely to an Augmented Intelligence, harnessing the power of AI combined with human decision making. As Paul Tudor Jones famously said, “No human is better than a machine, but no machine is better than a human with a machine”.

Ranjith

Dilzer Consultants Pvt Ltd

Reference:

https://www.ifc.org/wps/wcm/connect/7898d957-69b5-4727-9226-277e8ae28711/EMCompass-Note-71-AI-Investment-Trends.pdf?MOD=AJPERES&CVID=mR5Jvd6

https://www.ipe.com/artificial-intelligence-smart-advantage-/10025005.article

https://www.cfainstitute.org/-/media/documents/survey/AI-Pioneers-in-Investment-Management.ashx