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Still Using Legacy ‘Risk’ Systems For Portfolio Analytics?

Legacy systems in mid to large investment firms still form the main backbone for core operational systems. Stability of these systems, coupled with maturity of the required functionality, are the key strengths to continue to use them.

Some cons of such systems include lack of flexibility necessary for fast changing investment trends(handle new asset classes, investment products), scalability and performance to handle increased volumes, to name a few.

Although these systems help to keep the ‘lights on’ for the business, the increasing costs for maintaining such systems is a concern, as firms focus on reducing operational expenses with the goal to improve ‘operational alpha’.

Replacing/rebuilding core operational systems such as accounting and data management will need more than a few months, even for mid-size firms. However, satellite legacy systems such as portfolio analytics (risk and performance) need to be reviewed and put into a roadmap for migration.

If using vendor systems, then reviewing their roadmap will be necessary before finalizing firm’s overall technology roadmap.

So what are some of the key points that investment firms need to consider when migrating or building new ‘RISK’ system for portfolio analytics?

1. Are risk calculations run in batch mode or in real-time?

2. Is integration with the source system for holdings enabled through the use of API?
— Or do users need to upload the data manually or drop CSV files?

3. Can the risk system use prices from the client’s data management system for necessary asset classes such as for fixed income, otc derivatives?

4. Do you need to push data (holdings) or can the system pull this data from client’s source systems?
  — Pull is preferable as it reduces reconciliation of results if timing of data can potential cause discrepancies across say performance and risk results

5. Can the system support both ex-post and ex-ante risk analytics?

6. Can the system provide ‘What if’ scenario analysis in real-time?

7. How transparent is the logic used for risk calculations?
— Can users review the result to understand/tune the model?

8. Does the system support custom risk analytics that users can model using their preferred analytics tools such as Python, R and/or MATLAB?

9. Does it use modern architecture (‘microservices’) to help with scale and performance as volumes grow? 

9. Finally, does system enable the firm to a ‘Pay per Use’ business model?

In the next post, we will look at features necessary from the user’s perspective.

 

 

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