From Data to Decisions: Leveraging Machine Learning and Cloud Computing in Modern Wealth Management

Abstract

The wealth management industry is currently undergoing a significant transformation, driven by the convergence of machine learning, cloud computing, and the proliferation of data. Organizations can no longer ignore the potential of these new technology trends as they add complexity to the wealth management value chain. Machine learning has demonstrated potential benefits when applied appropriately: machine learning allows firms to retain or expand their competitive advantage, streamline their internal processes, and enhance their interactions with clients. It has become a priority for many organizations to accelerate the use of machine learning within their existing processes and functions. Cloud computing shifts the economics of processing data and applying machine learning algorithms, allowing firms with limited technology budgets to leverage the advantages of machine learning. It is now possible to rapidly scale up access to data and more advanced machine learning algorithms that can process it thoroughly, focusing internal capabilities on the application of machine learning rather than its operation. Data privacy and cyber risk ensure that the costs and complexities associated with huge data access are better managed by entities with expertise and experience in these fields.
Various industry sectors have actively pursued the rapid adoption of machine learning in their operations and processes. The impact of these developments has been felt across the wealth management industry. Applications cannot ignore the developments in the rest of the industry ecosystem that could add significant value to their existing processes. The paper builds on an overview of machine learning by setting the background to wealth and investment management functions. The most salient attributes of machine learning and cloud computing technologies broadly are outlined, with particular relation to wealth and investment management. A clear understanding of these capabilities enables the identification of the most pressing considerations that hold back or slow wealth and investment management applications in the mainstream adoption of these valuable technologies.
The wealth and asset management sectors have historically lagged the rest of the financial services industry in deploying advanced new technologies. Discrete processing of trades and information gathered over intimate interactions with clients is now a huge handicap as data proliferation threatens to overwhelm these processes. A wide variety of internal applications have sprung up in wealth and asset management firms, generally focusing on streamlining or automating existing processes. Some applications, automating account opening and reporting, have gained traction, but in the main firms lack a coherent understanding of how these possibilities might add value. The impact of recent external developments, such as advancements in machine learning and automated services, has begun to be felt within the industry.