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Predicting NAV delays with AI

August 12, 2021

Nekele McCall, OnCorps

Why We Built It

Delivering accurate NAVs, and delivering them on time, is the perhaps the most important responsibility of fund operations teams. Yet in today's outsourced-dominant world, asset managers
often have little visibility into this critical activity.


We developed the OnCorps AI NAV Timeliness Algorithm to help operations teams better predict delays in their NAV production process. By analyzing multiple data sources, our AI-powered algorithm learns which factors, or combination of factors, commonly lead to delays. The algorithm alerts teams when a NAV is expected to be delivered late, allowing them to proactively intervene earlier in order to more effectively mitigate risks.

How it Works

The algorithm uses three types of data to fuel its predictions and works seamlessly within the OnCorps AI NAV Oversight platform. It runs continuously and as soon as the NAV file is predicted to be late, sends an alert to the team, allowing them to re-allocate internal resources, communicate with the outsourced team, or notify relevant parties earlier in the day.

  1. Intra-Day: Was the pricing file delivered late today? Is the Corporate Actions team behind?
  2. Historical: How often has the NAV file been late recently? What factors have historically caused delays?
  3. Market: How volatile was the market today? How many Corporate Actions were initiated?
The algorithm maps all related files and identifies which are most consequential to producing the NAV on time.

Results

OnCorps developed this algorithm for the operations team at a Top 5 global asset manager. In just 12 weeks of training, the algorithm has achieved a 93% mean accuracy rate in correctly predicting late NAV files, which should continue to improve over time as our models continually learn and optimize.

Over a 3 month period, the algorithm achieved a daily accuracy rate of 95% or higher roughly two thirds of the time.

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