In a recent white paper entitled Evolution in Asset Management, SEI pointed out that 70 per cent of US fund managers are currently looking to deploy advanced analytics in the front-office. The field of data science and machine learning-based data analysis is helping to transform how fund managers think about data to gain a competitive edge.
Maintain influence in a more competitive DCIO environment - An industry-shifting approach to gathering DCIO assets
At nearly USD8 trillion, the US defined contribution (DC) retirement market remains one of the largest – and growing – opportunities for asset managers. From 2007 to 2017, net assets grew by 67 per cent, increasing by USD3.1 trillion.
To gain an advantage in today’s hyper-competitive world, asset managers must not only evolve their operations, but they must also discover new ways to leverage them. Managers must be operationally adept; particularly in a business that grows more costly and complex, they need an infrastructure that will equip them to better meet client needs, satisfy regulators and compete more effectively.
When reviewing middle-office outsourcing proposals, this comment is not uncommon. However, the inclination to evaluate outsourcing by extrapolating in-house operating budgets and comparing them to outsourcing proposals is an insufficient method for determining the total value of a proposed outsourcing relationship.
The first wave of middle-office outsourcing deals came on the heels of the the credit crisis in 2008. Given this timing, it should come as no surprise that the origin of these first generation deals was driven by cost savings. At the time, middle-office operations was viewed as a necessity but also a cost centre.
An asset manager’s decision to change their operating model and outsource their middle office to a service provider should not be made in haste. From building internal consensus to carefully designing an evaluation process, selecting the best fit for an outsourcing partner is a significant investment.
Digital transformation: Science, not fiction - Science fiction is a beloved and enduring genre largely because of technology’s transformative potential. The best examples leverage this to the hilt and extract as much dramatic tension as possible from the situation.
In 2017, investors allocated almost USD120 billion into private debt funds, on the back of approximately USD100 billion being raised in 2015 and 2016. To better understand the context into which investors are deploying such large amounts of capital, SEI and Preqin conducted a survey of private debt fund managers in mid-2018.
As discussed in the first part of our discussion with SEI on the potential of natural language processing (NLP) – click here to read – the company is currently in the early stages of exploring how NLP might enhance the way staff and clients interact with data. The aim of this initiative is to increase the overall level of engagement and grant users the flexibility to choose their preferred method of navigating information.
Natural language processing (NLP) is changing the way humans interact with machines in ways that were unthinkable a decade ago. Thanks to huge advances in machine learning, driven and supported by ever-faster computer processing, we are increasingly using NLP tools such as Amazon Alexa, Siri, Google assistant, Cortana, Bixby, and interacting with chatbots from our service providers.