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Every institutional investor needs shadow accounting for a reliable investment book of records. But data received from managers and custodians may not be perfect, leading to unreliable outputs.

Are your outdated processes bringing unwanted risk?

Historically, recorded in Excel and distributed via PDF, data is subject to error. Organize and structure the influx of investment data flowing in, and then layer on a robust analytics engine to glean key insights for more accurate, sound portfolio strategy decision-making. 

How accurate is your data?

The latest enhancement to shadow accounting capabilities allows your team to view data with greater transparency, accuracy, and control. Regain confidence in your decision-making and get everything you need to build an investment book of records that you can rely on. 

Video: Complexities of data

Learn ways to reduce risk and help improve accuracy in sophisticated investment portfolios.

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With the complexity of today’s investment landscape, many large institutional investors are in need of robust portfolio analytics so they can glean important, timely insights on their portfolio. With the right data, analytics is the easy part, and the questions investors are trying to answer aren’t that hard to solve. However, the problem IS the data. We have seen 2 big trends:
1.    Asset managers are sharing data with greater transparency, which is a double-edged sword.
2.    Investors have transitioned from 60/40 portfolios to more sophisticated asset allocations significantly weighted toward alternatives, mostly in the form of drawdown vehicles.

So what’s the data dilemma within these trends?

The process for exchanging information between asset managers and asset owners relies on PDFs, a technology invented in 1991. The manager enters information into a table or database, puts together the communication, and generates the PDF. The investor receives the PDF, then has to type the key information into Excel or a SQL database to analyze the data. This process is far too manual for a sophisticated institutional investor. In addition, managers are sharing disparate levels of transparency at varying frequencies with inconsistent taxonomies.

Lastly, check and commitment sizes for alternatives are smaller than for an S&P 500 Index Fund. And for a private investment program, the mechanism is a drawdown vehicle, which means the investor commits capital. Instead of wiring the full commitment amount on Day 1, the manager calls capital intermittently during the investment period and distributes capital intermittently during the harvest period. This creates an exponential increase in the number of PDFs an investor needs to process.

Investment teams can often struggle to handle the disparate levels of transparency and varying frequencies to make sense of the data. Plus, it takes a lot of time and effort to harmonize inconsistent taxonomies to draw meaningful conclusions from the data.

That’s where SEI can help. We work with investors to organize and structure the influx of investment data flowing in, and then layer on a robust analytics engine to glean key insights for more accurate, sound portfolio strategy decision-making.

Visit our website <<Shown on screen:>> for more information about our streamline data and analytics solution, and look for an invite to our upcoming webinar, where you can learn firsthand from a large university about their need for greater portfolio visibility that SEI’s data and analytics solution has helped provide. 

Information provided by SEI® Investments Management Corporation (SIMC), a registered investment adviser and wholly owned subsidiary of SEI Investments Company. Technology services provided by SEI through its affiliates and subsidiaries.

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