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The use of artificial intelligence in asset and investment management

Costs and resources were often mentioned when artificial intelligence was suggested for investment strategy workflows. Not anymore. As third-party technology providers become more ubiquitous in asset management, the need for AI has become more apparent and it makes sense to learn from others and rethink current workflow processes.
The level of implementation of AI has always depended on where the business, team, or manager stands on the technology adoption curve. While companies with systematic funds and quantitative analysts are more likely to dedicate technology resources necessary to uncover signals, many discretionary fund managers have yet to embrace any form of AI technology.

The need for modern technology like AI in asset management

Asset and wealth managers face challenges like increased passive investments, reduced investment fees, and uncertainties about the future.
In the past decade, asset and wealth managers have seen tremendous change affect their business models. Fee pressure, for instance, has led to an all-out price war and the move to passive investment has put active managers on the defense. In such situations, artificial intelligence, machine learning (ML), and data analytics technologies have helped bring positive change.
AI, ML, and natural language processing (NLP) are offering efficient solutions on both fronts - the need to generate alpha and the need to contain costs.

Major use cases of AI in asset and investment management

I. Portfolio management and client enablement

  1. Automated insights

    - Evaluating management sentiment by reading earnings transcripts.
  2. Relationship

    - Finding nonintuitive relationships between market indicators and securities.
  3. Alternative datasets

    - Analyzing alternative data (e.g., weather forecasts and container ship movements) and monitoring search engines for keywords that help frame hedging strategies.
  4. Growth opportunities

    - Estimating future growth and patterns in client behavior, based on corporate website traffic.
  5. Client outreach

    - Driving smart client outreach and demand generation via analytics, using alternative data sources such as social media.

II. Front, middle, and back-office efficiency

  1. Operations intelligence

    - Automating functions using machine learning.
  2. b. Powering risk performance

    - Monitoring suspicious transactions and trigger response protocols with AI-based algorithms and machine learning.
  3. Reporting and servicing

    - Generating reports for clients, portfolio and risk commentary, and marketing material using natural language processing.
  4. On-demand reporting

    - Responding to employee or investor queries, and generating management reporting on-demand by using ML and chatbots.
  5. Employee insights

    - Monitoring employee conduct risk and employee morale.

Advantages of using AI in asset and investment management

  1. Integrate more sources into investment models, retrieving filings, financial reports, press releases, and data from news and social media.
  2. Analyze large swathes of unstructured data, i.e., alternative data from credit card data, store circulation data, satellite images, and others.
  3. Enable the automation of manual middle and back-office tasks with intelligent automation solutions; this can help reduce costs of high-volume, repetitive tasks.
  4. Improve the first line of defense supervision, with efficient real-time monitoring and surveillance of suspicious transactions. Other areas include monitoring email, chat, and other modes of communication.

How AI and technology has benefited asset management

Between April and June 2019, a survey was conducted with 42 industry participants giving inputs on their changed approach to data and technology to address challenges in the asset management industry. Nearly half of the participants held C-suite positions, including new roles such as Chief Data Scientists and Chief Technology Officers.

Here are the findings.

  1. For 50% of the respondents, performance and delivery of alpha still drove fund selection. However, the growing need for performance led to an increased use of data; 64% used both structured and unstructured datasets in their investment process.
  2. The biggest disruption to the industry came from third-party data and technology providers, according to 51% of the respondents.
  3. 60% of the respondents had started seeing positive outcomes from increased technology on their investment processes; 21% cited ML, NLP, & RPA and 30% pointed to AI.

The above survey clearly shows how stepping up technology adoption can impact the future of investment management. To do so, businesses need to -

  • Treat data as a corporate asset
  • Share data seamlessly through the enterprise
  • Leverage market insights and customer feedback faster
  • Adopt emerging technologies

Capital Market BPM Solutions

Infosys BPM enables financial institutions to optimize performance, reduce complexity, and stay ahead in the game. We help investment banks, asset and wealth managers, and financial information providers improve performance that is future-ready. Get in touch with us to learn more about business process solutions for investment management.