INSIGHTS Opinion

Initially confined to specialized applications and niche markets, we are currently witnessing the widespread adoption of Artificial Intelligence (AI) and Machine Learning (ML) affecting our lives daily and, increasingly, in financial services. The finance sector has keenly observed the rise in AI applications and interest in all areas is growing – from asset management and quant trading, to fraud detection and personal banking.

Focusing within asset management specifically, strengths of its application include the ability to analyze complex data faster, more efficiently and arguably more accurately, as a means of enhancing the decision-making process. Scale and personalization advantages are also in focus.

AI methods, whilst still at an early stage of adoption, may include the following (source: CFA Institute (2019)):

respondents using linear regression (in investment strategy and process) outnumber those using AI/ML techniques by almost five to one;
only 3% of organizations had a technology team initiated with specific AI/ML capabilities;
the number of respondents using particular ML approaches (supervised learning, unsupervised learning, reinforcement learning) and ML models (Markov models, deep learning models, support vector machines) ranged from 6% to 15%; and
even though the use of unstructured data such as e.g. news was as high as 44%, the use of AI-based techniques to process these data such as natural language processing (NLP) was low (10%).

Critical to the success of the application of AI to asset management is data. Simply, Arabesque believes that AI methods perform optimally with large amounts of data. With data increasing exponentially, being available on public clouds and processed in real-time (see Figure A), it is anticipated that AI approaches will continuously improve as a result.

Figure A: The Global Datasphere 2025. Source: Bril et al. 2020

Furthermore, AI benefits from the increasingly available access to High Performance Computing (HPC) techniques such as the use of Graphical Processing Units (GPU). With continuous improvement of hardware (including the emergence of quantum computing McKinsey & Company (2020)), AI methods will continue to evolve and improve over time.

We look forward to sharing more developments and research from our AI team and its applications to asset management.

Happy New Year and thank you for your continued support.

Arabesque Asset Management

More on Arabesque AI

Arabesque has been built on the two disruptors of finance, sustainability and Artificial Intelligence. Utilizing advancements in technology, as an organization, Arabesque seeks to deliver transparent, sustainable, innovative solutions for our clients; whether through our SRay® data services, investment solutions or, most recently, our AI research. Arabesque AI was established in late 2019, with a minority stake owned by the asset manager DWS, with the mission to build a world-leading, AI-driven, investment technology company that offers its clients high-performing, efficient and individually customizable investment strategies.

The investment philosophy underpinning the use of AI, is that the discernible structure in financial markets is highly complex and varies over time, markets, and asset classes. AI can thus be used to build systems capable of handling this complexity and of enabling scalable investment process design for a wide variety of use cases in an efficient and cost-effective way.