From Boots to Data Integrity: Tackling Challenges in the Retirement Industry

Enterprise Iron Financial Industry Solutions, Inc.
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An Introduction

My career began with boots, a badge, and a patch – specifically a 1st Cav patch while serving in the U.S. Army in Ft. Hood Texas. Granted, working accident investigation, drunk driving suppression, and radar enforcement is a lifetime away from financial services and Defined Contribution plans. However, my journey also included time as a high school math and computer software teacher, experiences that provided a unique foundation for my current role as a consultant in the retirement plan industry. Being a cop and a teacher honed my ability to tackle challenges head-on, skills that are invaluable in this ever-changing, yet unchanging field like ours.

Defined Contribution plans may seem simple at first glance. A plan document, a payroll file, a recordkeeping database, a custodial account, and some reporting capabilities appear to be all that is needed. Once the plan document is signed and the custodial account set up, everything else seems straightforward: sponsors send payroll files, recordkeepers process them, custodians complete trades, and participants receive quarterly statements and year-end reports. Simple right?

The Challenges
Not quite. What complicates the process (that I didn’t mention above and in no particular order), are the numerous variables and unforeseen issues: distributions, loans, beneficiaries, transfers, QDROs, payroll inconsistencies, and a host of interface challenges—to name a few. Add to this the ever-changing regulatory requirements that create more complexity with each act that’s passed, and the simple isn’t so simple after all, and we aren’t finished yet – there is this not so little thing often referred to as “data”. No, not the android from Star Trek: The Next Generation (and yes, Kirk is the superior captain every day of the week), but the data provided by plan sponsors, vendors, custodians, and others.

Jeff Evers
Senior Principal Consultant
Jeff has 25+ years of experience in the Retirement Plan sector. On the business side, his areas of expertise include plan consulting, operational consulting, plan implementation, platform conversions as well as the ongoing administration of retirement programs including compliance, government form filing, risk management and process improvement. Jeff is a problem resolution resource for clients, auditors and employees. On the IT side, his areas of expertise include software design, requirements gathering and data analysis.
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The backbone of the defined contribution industry is data, and it’s amazing that in the midst of ever-changing regulations and technology, data is the never-changing instrument of frustration that makes all of the above more complicated than one could imagine. Combine this with the constantly increasing need for more and more data, and this never-changing “instrument of frustration” becomes overwhelming. You may be thinking “If we need more and more data, how can it be ‘never-changing’? That’s a great question.

Data needs change based on systems, experiences, regulations, and other factors. What doesn’t change is the quality of the data. Plan sponsors still send census and payroll data that isn’t correct, participants provide incorrect information, advisors provide incorrect information, fund companies provide incorrect prices, and even prior record keepers provide insufficient and incorrect information. These are just a few of the means by which the data integrity of most recordkeeping systems is compromised. The struggle to maintain data integrity is real and complex and, based on my experience, is the root problem of most issues we see in the recordkeeping industry today. And if I were to venture a guess, incorrect data is the cause of most errors experienced in any recordkeeping environment, whether daily or balance forward (yes, some of these plans still exist). Data gathering, analysis, and validation are where most time is spent by those in the recordkeeping business (aside from keeping up with the never-ending regulatory changes).

The Impacts
The risks created by bad data are substantial and the associated costs enormous. A 2022 whitepaper, authored by DCI out of London, indicates that poor data costs organizations $12.9 million per year, and with our ravenous need for more data, this number is growing. However, cost is not the only factor associated with bad data, nor the most important. Risk, in all of its forms, is the most important factor, whether it is a Fiduciary risk, Staff Maintenance risk, or that most important risk of all – Reputational risk. The risks associated with managing poor data are significant so let’s look at how these risks are realized, their impacts, and some associated costs. The following are some real-life examples from my career as an Implementation Manager, Director of Operations, Pension Administrator, and Consultant.

Example 1: Plan Sponsor – About Your Payroll File

Background: A new client with 26,000 employees and a high turnover rate faced significant challenges due to incorrect and inconsistent payroll files. The plan sponsor struggled to provide accurate data, with issues such as bad dates of birth (DOBs), incorrect dates of hire (DOHs) and termination (DOTs), incorrect salaries, and incorrect Social Security numbers (SSNs). This steady stream of bad data led to ongoing problems, including DOP issues, vesting issues, testing complications, processing delays, and reporting inaccuracies.
Risks & Costs: The financial costs to the plan sponsor were substantial. The service agreement included time and expense (T&E) charges for addressing these consistent errors, resulting in an annual bill six times higher than what it would have been with clean data.
Solution: Collaborating with the sponsor and their payroll team, over two years, including onsite support, resolved the data inconsistencies. Once accurate data was provided, T&E costs dropped significantly, and the plan began operating smoothly with minimal issues.

Example 2: Vendor – We’ve Always Done It That Way

Background: An onshore vendor assumed processing duties for the recordkeeping company. During this time, the vendor incorrectly processed forfeitures for approximately 1,500 plans, erroneously forfeiting the non-vested account balances of all terminated participants in the system. This error led to incorrect trades totaling ~$670m.
Risks & Costs: The incident caused severe financial and reputational damage. The vendor was financially responsible for funding losses to plans that experienced them and lost significant revenue as client fees were impacted. The recordkeeper bore high staffing costs to resolve the issue and suffered a 15-20% loss if business due to reputational damage. It took two years to correct all impacted plans, further straining resources.
Solution: A comprehensive recovery effort. This was a boots-on-the-ground scenario where we worked to identify and restore impacted participants who were erroneously forfeited immediately. Once that was done, we had to calculate the financial impact on 1,500 plans. This was done by reconciling the cash for each plan before and after the correction and allocating gains as earnings and funding losses.

Example 3: Fund Company – The Price Isn’t Right

Background: A fund company transmitted incorrect pricing for their underlying assets unitized into various investments. This error affected all trades involving the unitized funds, including distributions, contributions, loans, and transfers. The resulting errors required extensive manual corrections and plan reconciliations to identify gains or losses.
Risks & Costs: The financial burden fell entirely on the recordkeeper, including increased staffing costs and responsibility for funding losses caused by pricing errors. Reputational risk also loomed, as such errors undermine trust in operational reliability.
Solution: All transactions involving the impacted investments were reviewed and corrected. System-wide price and balance corrections were implemented, and participant records were updated through trading and non-trading transactions for distributions, transfers, and contributions.

PMCS: Preventative Measures Checks and Services
I began my career with boots, a badge, and a patch, tackling challenges head-on sometimes literally – in the line of duty. Transitioning from those experiences to teaching math and ultimately consulting in the retirement industry has been a transformative journey. Each step has built a depth of expertise that allows me to address one of the industry’s most understated and critical issues: data integrity—the elephant in the room.

In the Army, we used PMCS: Preventative Measures Checks and Services, to mitigate equipment failure. This process includes routinely checking equipment to identify and resolve issues on a regular basis before it fails. Similarly, in the retirement industry, bad data originates from numerous sources – not just plan sponsors. In a field driven by an insatiable appetite for data, an ounce of prevention is worth tens of thousands of dollars in cures. As highlighted in the examples above, poor data quality leads to financial, operational, and reputational risks. However, there are proven solutions to mitigate these risks, and none are more essential than preserving reputational risk.

At Enterprise Iron, we are dedicated to helping our clients tackle data challenges head-on. By offering tailored solutions for Plan Sponsors, Recordkeepers, and Vendors, we enable businesses to achieve the data integrity needed to optimize operations and drive success.