One Governance Gap, Two Fraud Channels: The MI5 Denny’s Case

In 2022, a Minnesota woman was sentenced to more than nine years in federal prison for embezzling over $881,000 from a Denny’s franchisee and a family-owned construction company. What makes this case worth a second look, beyond the last article’s Randstad payroll scheme, is that the same person ran two separate fraud channels through the same underlying weakness, and neither channel needed to be sophisticated to work for five years.

What happened

As Director of Operations for MI5, Inc., Kimberly Sue Peterson-Janovec had oversight of payroll, vendor billing, and cash deposits across eight restaurant locations. She used that access two ways. First, she submitted false requests for vendor payments, creating fake email accounts to impersonate vendor employees and generate fake correspondence supporting the payments, netting roughly $336,000. Second, and separately, she manipulated the payroll system to issue herself unauthorized pay using the names of employees who no longer worked for the company, netting another $20,000. On top of that, she was held responsible for an additional $181,000 in stolen cash deposits.

Two different fraud mechanisms, one root cause. Both vendor identity and employee identity were things the system trusted once established and never re-verified.

Why one control gap produced two exploits

Most ERP fraud writeups treat vendor fraud and payroll fraud as separate problems needing separate controls. They usually are separate controls in practice, but they share the same underlying assumption: once a master record exists, whether it is a vendor or an employee, the system treats it as valid until someone actively flags it. Nobody was asking the system to continuously re-verify “is this vendor real” or “is this employee still employed” on every transaction. Both checks happened, if at all, as periodic manual review rather than a standing rule enforced on every payment run.

That is the pattern worth generalizing. A fraud scheme doesn’t need two different weaknesses to run two different exploits. It needs one weak assumption that both processes happen to share.

Controls that would have caught it

Vendor side. A governed vendor master process should treat any new vendor contact channel, especially email domains that don’t match a registered business domain, as a flag requiring secondary approval before the first payment goes out. Cross-referencing vendor contact emails against internal employee email patterns is a cheap, high-value check most ERP implementations never configure, because it isn’t a default workflow. It has to be built.

Payroll side. Every pay run should cross-check active employee status at the moment of payment, not rely on a termination flag set once in HR and assumed to propagate. In practice, this means the HR and payroll integration needs to be a hard gate, not a soft sync, so a terminated worker record cannot appear as a valid payee in any pay run regardless of when the termination was recorded relative to payroll cutoff.

Both sides. The five-year duration of this scheme is the real tell. Neither vendor payments nor payroll runs were being reviewed for anomalies as a routine, systemic process. They were being trusted because they had always been trusted.

An AI prompt example for ERP fraud detection

This is where AI-assisted review earns its place, not replacing the controls above but catching what static rules miss because nobody thought to write the rule. Using a natural-language query layer against the ERP’s vendor and payroll data, a controller could run something like:

“Compare vendor contact email domains against our employee email domain. Flag any vendor created in the last 24 months where the contact email domain is unregistered, uses a free email provider, or closely resembles an employee’s name.”

And separately:

“List all payroll disbursements in the last fiscal year paid to employee IDs with a termination date recorded in HR prior to the pay period start date.”

Neither query requires new functionality. Both require someone to think to ask the question, which is exactly what a five-year undetected scheme tells you nobody was doing. The value of an AI layer here isn’t that it catches something a human couldn’t. It’s that it makes asking the question cheap enough to do routinely instead of only after something else triggers an audit.

The pattern for this series

Every case in this series will follow the same shape: what happened, what shared assumption let it run, what a properly governed ERP control looks like, and one or two concrete AI prompts that turn a periodic audit question into something that can run continuously. The goal isn’t to suggest AI replaces the underlying data governance. It’s to show what becomes possible once that governance exists and someone actually asks it the right question.

Source disclaimer

The case details in this article are drawn from a press release published by the U.S. Attorney’s Office for the District of Minnesota, a public government source, along with contemporaneous news coverage of the same case. All facts, figures, and quotations describing the case are sourced from those releases and reports. The analysis of the shared control gap, the proposed detection controls, and the AI prompt examples are original commentary and are not part of the source material.

References

United States Attorney’s Office, District of Minnesota. “Kenyon Bookkeeper Sentenced to More Than 9 Years Prison for $881,000 Employer Embezzlement and Tax Fraud Scheme.” Press release. https://www.justice.gov/usao-mn/pr/kenyon-bookkeeper-sentenced-more-9-years-prison-881000-employer-embezzlement-and-tax

Walsh, Paul. “Woman who embezzled $880,000 from Denny’s franchisee, Rochester company gets 9 years.” Minnesota Star Tribune, June 2022. https://www.startribune.com/9-1-4-years-in-prison-for-woman-who-embezzled-880k-from-dennys-franchisee-rochester-company/600184577