Taking the risk out of mutual fund compliance: a seven-step check list for safer, more cost-efficient mutual fund sales

The Authors

Jeff Levering, Vice President of Corporate Development and Business Strategies, NewRiver, Inc., Andover, Massachusetts, USA.

Acknowledgements

Editor's note: founded in 1987, and headquartered in Andover, Massachusetts, NewRiver has a long history of successfully partnering with retirement plan providers, variable annuity providers, retirement plan record-keepers, third-party administrators, and financial brokerage firms (including their registered representatives, broker assistants, operations staff, and compliance professionals) with its industry-leading electronic compliance and intelligent document fulfilment solutions. For more information, visit www.newriver.com

Abstract

Purpose – The purpose of this paper is to explain the risk of bad mutual fund data for a financial services firm that sells mutual funds and to recommend steps a firm can take to ensure the reliability of its mutual fund data.

Design/methodology/approach – Explains problems caused by missing data, including the “breakpoint issue,” the best sources of mutual fund information, problems firms have retrieving and compiling that information, and weaknesses in a free service (the “Service”) provided by a well known industry utility and securities depository that related to coverage, completeness, and cost. Recommends seven actionable steps a firm can take to ensure the accuracy of its mutual fund data.

Findings – Firms offering funds may sell billions of dollars of funds every week, and maintain considerably more on their books, but frequently they pay scant attention to the accuracy of their mutual fund reference data. The breakpoint issue is alive and well today, and would seriously erode investor and regulatory confidence if this fact became better known. For many firms, discrepancies in data result from funds having one policy disclosed to the SEC while their distributors use entirely different ones. Firms now realize that ensuring accurate reference data is no longer an issue that can be avoided.

Practical implications – Rather than operating under the false assumption that critical data is accurate, make sure you dedicate the time to examine your organization's use of mutual fund data to ensure efficient, ongoing investor relations and to ensure the overall reputation of your firm.

Originality/value – Professional advice from an experienced vendor of compliance systems and software.

Article Type:

Technical paper

Keyword(s):

Financial markets; Financial services; Funds statements; Data management; Risk management; United States of America.

Journal:

Journal of Investment Compliance

Volume:

Number:

Year:

2008

pp:

40-44

Copyright ©

Emerald Group Publishing Limited

ISSN:

1528-5812

Today, the majority of financial services firms are involved in selling mutual funds, frequently in great quantity, yet very few give any thought to the validity of their data or the technology that is responsible for aiding in the sale or disclosure of these funds. Selling a mutual fund relies on complex mutual fund information to be summarized down into small parts or “data” that sellers can understand – which is most often driven by a technology or data warehouse infrastructure. After all, if a financial services firm unknowingly relies on bad data it can get in the way of good, profitable, and reputable investor interactions.

Let's face it, missing and/or bad mutual fund data is nothing new but it has recently garnered the attention of regulators as it was directly responsible for creating the renowned mutual fund “breakpoint” issue. As a result, sellers of mutual funds now find themselves asking what they can do to better understand the fundamentals of mutual fund data, to understand why it's important to their firm, and to how to identify possible risks if these issues were to occur again.

Missing data causes big problems: enter the 2002 mutual fund calamity

In 2002 a blemish appeared on the generally spotless record of mutual funds; which is referred to as the “breakpoint issue.” Discovered by examiners from the Financial Industry Regulatory Authority (FINRA) – formerly the National Association of Securities Dealers (NASD) – the breakpoint issue relates to investors being overcharged commissions because some brokerage and other firms have not disclosed or used detailed fund policies, such as “rights of accumulation” or “letter of intent”, when selling a fund.

While committees were formed and industry groups opined, the net result is that very little has changed. The breakpoint issue is alive and well today, and would seriously erode investor and regulatory confidence if this fact became better known. The problem is at one level simple. Mutual fund sales are driven by technology that relies on mutual fund policies, such as “rights of accumulation” rules, being summarized as data points for the technology to work. Firms offering funds may sell billions of dollars of funds every week, and maintain considerably more on their books, but frequently they pay scant attention to the accuracy of their mutual fund reference data. Reference data is the critical link as it electronically represents a number of key data attributes including investors, intermediaries, issuers, products and prices. With reference data comprising the majority of the data content in trades, firms now realize that ensuring accurate reference data is no longer an issue that can be avoided.

Worse, even when firms believe they have the reference data issue under control, random data audits have found that when comparing the mutual fund data held by distributors to the mutual funds' own rules, a disturbing accuracy problem still exists. For instance, comparisons have shown that for many firms, discrepancies in data results because funds have one policy as disclosed to the SEC while their distributors use entirely different ones.

Database streamlines disclosures and reduces risks

The best source of mutual fund information – its pricing and other policies – is contained in each mutual fund's prospectus and more detailed statement of additional information (SAI). From a legal point of view the prospectus is key; it is defined in US Federal Law by the Securities Act of 1933, and again in the Investment Company Act of 1940, as the definitive source of information about a fund. Policies within the SAI are equally binding to a distributor, but rarely are these documents provided to investors.

For example, every single fund is required to provide its prospectus and SAIs to only one party, the Securities and Exchange Commission. While these documents were initially required in a paper-based format, beginning in the late 1990s mutual fund companies were required by the SEC to file them in an electronic format to the Electronic Data Gathering, Analysis, and Retrieval or EDGAR system.

However, lacking an easy way to retrieve the information from these electronic prospectuses at the SEC, brokerage firms, industry associations, and vendors have been devising ways to develop data repositories with prospectus-like information for their trading and information technologies. Each solution, which runs the gamut from teams of people in back-offices, to industry-led repositories, has touted its data quality as the most reliable. Yet because each ignores the inherent power of the EDGAR system as the base resource, many initiatives begin based on a flawed model.

In order for firms to build and manage this mutual fund data themselves they need to gather information manually from fund companies via phone, e-mail and faxes; buy and piece together data feeds from vendors such as Morningstar; and then run separate processes to figure out which source seems best. Frequently, and especially with the larger firms, there may be multiple groups creating their own versions of this data, which as we all know increases the cost and likelihood that the information will not match internally.

Firms absorb incremental expense to do this work, and in larger firms the costs can easily exceed $1,000,000. Also, these same firms privately suspect the quality of this effort due to its patchwork approach. This leaves the firm with an uncomfortable truth: high quality, reliable mutual fund data is absolutely essential, but it is not available for free; and alternatively it is expensive and risky to self-manufacture. At the end of the day the people who are most at risk at the end of this whole process are the mutual fund investor, and the advisor they use.

There's a reason that the existing service is free

The key industry solution for the mutual fund data is a free service (the “Service”) provided by a well known industry utility and securities depository. The concept is fairly straightforward: fund companies, if they choose, may provide information to the Service which will then be provided at low cost to participant firms, primarily the brokerage firms who need it.

The free Service was designed in 1999 and remained idle until 2002 when the breakpoint issue was becoming more clearly understood. This first version of the Service was revamped and re-launched in the fall of 2007. While the effort was a start in the right direction, the reason the Service won't work revolves around three key issues: coverage, completeness, and cost. Let's look at each of these issues separately:

Coverage is the first thing a firm must question when it comes to the Service. And ask the tough questions staring with: does the Service have the participation of all the funds the firm needs? If not, every time a fund is missing, then the information must be manually found and managed, which once again opens the firm up to inaccurate reference data.

More specifically, we compared Service data from October 2007 with fund data on the SEC's EDGAR system and found that there are still considerable gaps in the offering, with more than 40 percent of funds missing from the Service.

What firms needs to ask themselves is why 40 percent of the funds are missing from the Service, while 100 percent are providing information to the SEC as required? The reason is that not all fund companies participate in the services of the industry utility, and of those that do participate, not all elect to provide data to the Service.

Completeness is the next issue that needs consideration. Completeness refers to the accuracy of the information, including the degree to which fund policies listed on the Service match the same policies in the SEC's EDGAR system. There are two types of discrepancies: the first being where the data is simply different, and the second where the data is missing – i.e. a “blank” when the EDGAR system shows the fund has a policy. In the case of the breakpoint tables, the core commission pricing table for front-end loaded mutual funds, more than three out of five (or fully 68 percent) of the funds in the Service either have a discrepancy or are missing.

Even more detailed mutual fund policies show discrepancies between the Service and the SEC EDGAR system. And it is important to understand that the compound effect of multiple discrepancies across various data elements leads to an exponential increase in disclosure and transaction errors. For example, if 15 data elements are required to accurately calculate a front load breakpoint, and each is 95 percent accurate, the cumulative effect is to miscalculate the fee more than half the time.

So that leaves us with Cost. Firms hoping to rely on the Service are left trying to cobble together data from the missing funds – a plight that seems even worse because of their overall lack of confidence. This added cost, which is often buried deep in operations, tends to be hidden from most organizations. Fund companies, and their investors, are similarly left with the expense of manually managing this data. For example, if just two people across the 550 fund groups spend 15 percent of their time monitoring and making changes to the Service, you have investors spending over $16 million a year maintaining this free service.

Seven critical questions for mutual fund data success

Avoiding the risks and costs of unreliable data is not as difficult as it seems provided you follow seven, actionable steps to assess the accuracy of the mutual fund data your firm currently uses. More specifically, firms should be careful to ask themselves the following seven key questions:

What mutual fund data does the firm use now? How important is it to our enterprise value? Since mutual fund trading is automated to a point, find out what data your firm is currently using to support trading. Do a “back-of-the-napkin” assessment of the risk level to your company if this data is wrong – including but not limited to regulatory, financial, reputation, etc.
How is mutual fund data collected and managed now, and what cost/risk assessments have been done? Mutual fund data can come from multiple sources, and take significant resources to manage. Firms need to ask how much money they are spending to collect this data now.
Does our firm have, or is it considering, a data governance initiative to reduce risk? As securities and financial services firms recognize that their automated systems provide added efficiency and accuracy when leveraging high-quality data, are they creating organizations and procedures for overseeing data management? At one end of the spectrum firms are forming complete data-governance organizations, including a chief data officer, to manage both data acquisition, management, and risk assessment. On the other end of the spectrum firms are opting to hire outside vendor capabilities to support, create and manage quality data sourcing and oversight. The goal is the “golden copy” of the data from which an entire business can run. Be sure to understand what your firm is doing, and how is the organization changing to better manage mutual fund data.
Should we consider doing a mutual fund data audit? You should test mutual fund data via a comprehensive audit, whereby data from your firm is compared to current available policies sourced from existing SEC documents. The goal is twofold: one, identify discrepancies, and two, identify mutual fund policies that are available, but currently missing.
To what degree do our employees rely on data that our company does not manage? When the breakpoint issue first occurred, many firms worked to mitigate future risk by creating paper forms and spreadsheets. Chances are this data, which is outside of most organization's core collection processes, is old and under-managed. It is critical to understand whether your employees are using data from vendors or analysis firms and are disclosing this information to investors, and whether they are taking into account all the requirements for breakpoint disclosure?[1].
What level of validation is sufficient? Mutual fund data, because it simplifies accuracy checking for each trade through automation, needs to be reliable – i.e. consistent, correct, and complete. But data quality standards will vary depending upon the type of data, its source, and resources applied. Take the time to understand the validation processes that are in place today and determine what, if anything, needs to be done in the future.
Is liability and risk part of the assessment? This kind of assessment frequently happens after a problem has been identified. For instance, the “breakpoint issue” most likely has already been found and is now assumed fixed at your firm. However, what would be the cost to your firm if the issue is rediscovered by regulators? What protections do you have if you are self-sourcing mutual fund data internally?

Better mutual fund data leads to better profits

Investing in exemplary mutual fund data makes sound business sense, especially if it is sourced from the SEC's EDGAR system. Because of the risk and complexity, this is one of those cases where “buying” a solution makes so much more sense than “building” one, both from a cost as well as a risk reduction perspective.

Before your company spends another day operating under the false assumption that critical data is accurate, make sure you dedicate the time to examine your organization's use of mutual fund data to ensure efficient, ongoing investor relations and to ensure the overall reputation of your firm. By doing so, you may become your firm's biggest asset, having protected them by making some sound and basic changes in this important yet often hidden and under-valued area.