Schroders Investment Management is a 215 year-old company formed in London and now in 32 markets globally, including 8 in Asia. They are primarily an asset manager, with institutional clients as well as intermediaries who distribute their investment products.

Given the number of markets it operates in, Schroders has invested in its ability to manage business change across its business units, such as changes to shared services or regulatory driven change in processes. Schroders values delivering a great experience to its clients and employees and so has developed digital, cognitive science and data science capabilities.

Schroders collects a lot of internal data from various operational transactions, processes as well as activities monitoring. However, there is a desire for automated and intelligent process or technology to further mine the value of such internal data to the organisation in Singapore or any market.

Currently, the process to answer key Operational, Investment Management or Distribution related questions still requires a lot of manual intervention - searching for data, sense making, and communicating insights or recommendations. This may involve one or more of investment managers, traders, data insights team and relationship managers (RM), which is time consuming and not necessarily accurate, or complete. This impacts the organisation’s ability to understand if it is operating at its best capacity, effectively or efficiently, and where the possibilities of opportunities to improve are, on its journey to becoming the leading digital asset management firm globally.

The Problem Statement

How might we improve our internal processes to derive relevant insights, particularly through utilising our data, in order to operate more effectively and efficiently?

What They Are Looking For

• A prototype solution(s) that will enable us to make faster and more informed decisions, to improve our business across Operations, Distribution and Investment Management teams.
• For Operations:
  • Better resource planning.
  • Identifying which processes are sub optimal.
  • Enabling us to prioritise which processes to automate and/or digitise.
  • Identifying opportunities for innovation.
• For Distribution (Sales and Marketing):
  • Understanding cost to acquire, serve and retain our clients.
  • Identifying indicators of customer churn and actions we could take to reduce it.
  • Predicting future buying or selling patterns.
  • Calculating optimal timing/ mechanisms for client contact.
• For Investment Management:
  • Improving regulatory compliance processes.
  • Enhancing identified processes for better efficiencies.

For example:

• Digitising and automating identified manual processes.
• Incorporating new technology solution to improve identified processes.
• Ideally, these solution(s) exist in some form and can be applied to our data sets to demonstrate capabilities relatively easily.

There are no restrictions on the geographic location of the problem solvers who may choose to apply to this challenge. However, the prototype needs to be demonstrated in Singapore.