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The Undesirable Consequences of Flawed Financial Assumptions

Industry Trends and Research

We are all familiar with GIGO, “Garbage in, Garbage out”, the fundamental concept in computer science and information technology that highlights the importance of data integrity. Incorrect, incomplete, or nonsensical data as input inevitably results in equally flawed output. Said another way, the quality of the output is directly determined by the quality of the input.

The flaws are not always immediately obvious, as anyone who forgets to take the garbage out before leaving for vacation can attest. It often requires the passage of time to fully appreciate the impact of missing, incomplete, or manual interventions in a process, particularly processes as complicated and intricate as those in securities processing and participant accounting.

Considering all the advances made by data scientists and technologists over the past several decades, it’s surprising to realize how much manual platform process intervention is still required in the retirement plan industry. And the consequences show up down the road in many unexpected ways.

Trust is Fundamental 

Human beings tend to trust output from electronic sources. Financial professionals frequently lose sight of just how much manual “intervention” occurs in the pricing of collective investment trusts, stable value funds, or other assets that lack (or are processed without a link to) CUSIPs or ticker symbols. Sometimes those assets are priced with an old price, sometimes a price that is “off” by a decimal place or two, and sometimes they’re priced—with no price at all. And when that bad pricing is “buried” in the valuation of a target-date fund or managed account, decisions are made that impact people’s future that cannot be reversed or reprocessed.

Asset pricing is merely one potential data flaw. Also, consider the timely and accurate crediting of income. Be it dividends or interest income, whether posted as income or reinvested, it generally depends on the same type of identifiers described above. And what, one may ask, happens to the timely crediting of income when those identifiers are missing, or have been manually manipulated?  

These things can be corrected with time, but by then, some participants will have transferred from those funds and perhaps even taken a distribution.

This means, of course, that the valuations upon which those balances were based were inaccurate, perhaps significantly so, depending on the error amount and how much time had elapsed before it was recognized and adjusted. This is often material and meaningful and requires action.

The sad reality is that we take a lot for granted. Data integrity is essential for reliable reporting and maintaining trust. The best valuation platforms have mechanisms to check for missing or wildly erroneous pricing, as well as the timely posting of income and the missing identifiers that are frequently the root cause of the problems.

From the outside, it is difficult to know which organizations implement sound data governance policies, quality data frameworks, and robust data management practices to ensure data integrity throughout its lifecycle. It is, however, important—critical, in fact—to know the difference.

Your clients, and their comfortable retirement, depend on it.

Kassandra Hendrix is Chief Marketing Officer with Austin, Texas-based LeafHouse Financial.