How AI is transforming Investment Management

Published by FirstAlign

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The use of Artificial Intelligence (AI) in Finance has always been expanding to cover a wide range of areas. Today AI is widely used in fraud detection, risk management, customer service, to name but a few.

With advancements in technology, AI is now empowering firms to explore new possibilities in investment management. This article looks at three ways AI is transforming the process of investment.

Photo by Markus Spiske on Unsplash

Generating Alpha

Alpha is a measure of the active return on an investment, the performance of that investment compared with a suitable market index. The goal of every investment manager is to generate positive alpha for their clients. An alpha generator is a type of security that can help create more significant returns with no additional risks when added to a portfolio. As technology and data evolve, companies that keep pace with new approaches outperform others.

Data is growing, and the ability to transform data for investment insights will improve alpha. To get started with alternative data adoption, we need to consider the following points.

  • Identify the right alternative data

First, identify the correct data type to be integrated with the decision- making process. Test data signals regularly to determine any alpha decay.

  • Integrated data analytics platform

An integrated platform can provide for the exchange of traditional and different financial data. It offers better market insights.

  • Robust data structure

Establish a robust data structure that can handle different technology, storage, and computing requirements.

  • Collaborative insight team

Build an insight team that consists of data scientists, engineers, and data analysts who can study and derive new insights from alternative data.

Man Group, a fund management company, uses AI and alternative data to support alpha generation. Man AHL is a quantitative investment manager that is part of Man Group. Man AHL uses an adaptive intelligent routing algorithm to pick the best route for a trade. Machine Learning is used for pattern recognition, trend following, and natural language processing.

The most effective AI application in Man AHL is developing trading strategies (algorithms that generate trades) and improving such trades’ efficiency. Man AHL’s ML model provides both alpha and diversification. It ignores existing portfolio features or nuisance effects.

Improve operational efficiency

In investment management, operational cost remains crucial; hence, many firms automate their processes and adopt AI in middle and back-office functions. The power of AI in decision-making can be a game-changer in risk management and compliance.

AI models are built on a modular and cloud-based architecture. This cloud-based architecture enables firms to integrate and as well as offer external services. An AI-enabled service model can ingest and process more data and also continuously learn and improve.

Alladin by BlackRock is an excellent example of a leading investment management firm that developed internal service and made them available commercially.

Photo by Frank Busch on Unsplash

Managing risks

With a massive increase in data volume, traditional risk management methods are not very efficient. AI-enabled risk management can identify and manage both known and unknown risks at more incredible speed. AI can automate the consumption and analysis of data and reduce administrative tasks. Employees will have more time to focus on the resolution of identified errors.

Challenges in implementing AI

The journey of AI adoption, augmentation, and implementing has its share of risks and challenges. It requires continuous understanding, identifying, and managing risks throughout the AI journey.

AI and its speed of evolution poses significant challenges. Firms struggle to find a balance between early and late adoption. Early adopters have to overcome complexity and uncertainties associated with technologies. Late adopters have the risk of being left behind. Staying current with the latest in technologies is a real test.

Firms need to mitigate implementation risks by forming an enterprise risk management framework and identify well-defined rule base processes for automation and AI implementation. Then establish a robust testing program that proactively identifies errors.

Conclusion

Even though the AI journey is challenging, AI’s transformative role in investment management is beneficial to many firms. Firms can implement AI in the generation of alpha, operations, and risk management. The successful development and implementation of AI depends on various factors such as the firm’s technological strategy, operating model, and team. Early adopters of AI are more likely to lead the financial world.

References

Published by FirstAlign

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