AI in investment management
Investment management, also referred to as wealth management or asset management, is a burgeoning global industry worth more than a hundred trillion USD.

AI in investment management

Investment management – an introduction

Investment management, also referred to as wealth management or asset management, is a burgeoning global industry. The combined holdings of the biggest management firms are worth more than a hundred trillion USD.

The relationship between investment management firms and their client is like that of trustee towards beneficiary; they are responsible for handling all actions related to the client’s financial portfolio such as buying, selling, and protecting his assets and investments. Apart from these duties, they plan strategies for achieving the short-term and long-term wealth goals of the client.

What is AI?

In today’s world, Artificial Intelligence (AI) is part of the human experience of day-to-day living. From science to shopping, AI has become an indispensable form of technology, aiding the efforts of mankind in every field.

What is AI? In a broad context, it is the ability of machines like computers to mimic human mental functions like thinking and learning. It consists of a range of technologies that study data and process it to predict patterns, solve problems, and arrive at solutions.

AI in investment management

Investment management firms have increased their efficiency by successfully adopting AI. From communicating with clients to building operating strategies, every aspect of a business is being constantly revolutionized with the help of AI.

The capabilities of AI in processing enormous data and anticipating future trends in different investment avenues have empowered firms in making more accurate decisions to offer the best value for clients.

10 use cases of AI in investment management

Here are ten ways in which AI finds use in this industry

  • Analyzing earnings transcripts to understand management sentiment
  • Studying the relationship between securities and their respective market, and identifying which ones work best for the client
  • Studying alternate data like weather forecasts, and SEO optimization better hedging
  • Ensuring better growth by building website traffic and studying client behavior
  • Building better client relations and generating demand through analytics
  • Automated functions through machine learning
  • Identifying and rectifying fraudulent or risky transactions
  • Generating report using natural language processing
  • Engaging with client and management and reporting through chatbots and natural language processing
  • Monitoring employee behavior for the benefit of the management

Pillars of AI transformation

The importance of AI in investment management can be understood by identifying the ways in which the industry is evolving. This makes it necessary for firms to invest in the resources that enable them to achieve their maximum potential. The four main pillars of this transformation are

Alpha generation

Alpha in investment terms is the name given to the indicator of the performance of an investment. Firms with good data analytics can boost the alpha for clients. With new data being generated by the millisecond, it is not feasible for human beings to sit and pore over the accumulated data. This is where AI-driven machines come in. These time-saving machines process data thousands of times faster than humans. They can form links between seemingly unrelated data to establish patterns. AI also enables alternative data research into potential companies for investment and eliminates risky prospects

Enhancement of efficiency

Cost management is made more efficient with firms adopting the AI game. Automation saves both time and cost of operations. Some of the big names have gone a step further and developed in-house AI services which they make commercially available, providing them not only an edge over competitors but also an added revenue source. Cloud-based operating models mean more efficient and improved service for the firm.

Improving product and content distribution

AI works to the advantage of both firms and clients. AI identifies customer base in hitherto unexplored areas, to help build better distribution models. Digital customer engagement and quality content marketing help understand, retain, and expand customer base. Maintaining customer relations in a highly competitive environment is crucial, and sales teams and customer relations teams work in tandem with AI tools to do just that.

Risk management

Risks both real and potential are more accurately identified and reported by machines with their ability to process enormous volumes of data. Machines help to keep the firm updated on investment guidelines that are constantly being changed. They also keep track of employee behavior to identify any non-conducive activity. With automation taking on the bulk of such work, employees can focus more on aspects where machine intervention is not possible, such as resolving errors and bad outcomes.

How to get started with AI in investment management

With all industries undergoing transformation, there is more and more demand to employ AI experts, who are limited in number, so there is heavy competition to employ the right talent. Investment management firms must first build a long-term model for transformation and identify and work with the right people to ensure a smooth transition. Firms must establish proper communication with the experts and explain clearly the role they expect AI to play in their business.

Full-scale transformation happens only in stages, so it is important to first test the effectiveness by studying the use cases and building a plan to achieve short-term goals before focusing on a long-term transformative process challenged by ever-evolving AI technologies.

Working in collaboration with other firms in taking the AI plunge is a great strategy to explore new ideas, solve issues, and imagine new ways of applying AI to increase the potential of the industry. Industry stakeholders and regulatory institutions who set the standards are also to be engaged in this dialogue as necessary participants for full-scale transformation.

Conclusion

The scope of AI is increasing every day, providing new kinds of opportunities to investment managers. Data analytics, cloud computing, blockchain technology, and quantum computing are some of the exciting fields of AI that are radically improving industry activities in a never-seen-before manner. By becoming early adopters of AI, firms will have a competitive edge in an industry governed by heavy competition and unpredictable risks.

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