In midst of the significant loss and disruption, COVID-19 may bring a silver lining as the closing of offices and workplaces create an appetite for digital transformation. This may change the tone for future business operations in every sector but in particular the Asset Management industry. The urgency for remote working continuity plans has brought asset management companies to a crossroads and seen many use Machine Learning (ML) to remove inefficiencies and produce real-time insights or divest clientele who will move to the next best performer in the industry. As face-to-face meetings, which were essential to the smooth operation of many asset management companies, are no longer an option, management grapple to find alternative ways to maintain communication and transparency.
Machine Learning is a form of predictive modelling where computers use large datasets to find patterns and apply them to infer future trends (PwC). Traditional quantitative methods in asset management can be significantly enhanced with greater accuracy in less time through the use of ML.
Using a global, well-renowned asset management company as a case study, a recent report by UCL student Maegan George and UCL School of Management’s Teaching Assistant, Marcos Fuentes, proposes the ways in which ML could be implemented within the field to achieve strategic change and business objectives.
Project 1: Customer Insights
Project 2: Investment Insights
Project 3: Smart Portfolio Tools
Project 4: Back Office Operational Optimisation
Project 5: On-Demand Reporting with Chatbots