Looks Right Isn't a Control Does Your Payroll System Sanity-Check Itself
Published on: Fri Jan 10 2025 by Ivar Strand
“Looks Right” Isn’t a Control: Does Your Payroll System Sanity-Check Itself?
A common ritual in many organizations is the final review of the payroll register. A manager scans a list of names and figures, notes that the total seems plausible, and provides the final approval. This reliance on a subjective, human “looks right” assessment is one of the weakest links in the financial control chain.
A system that blindly accepts and processes data, trusting a cursory human review to catch every error, is not a robust system. It is a brittle one. The misplaced decimal point that results in a 100x overpayment is not a failure of the person who made the typo, but a failure of a system that did not have the basic intelligence to question it.
The Brittleness of Blind Data Processing
Most financial systems are exceptionally good at executing calculations. They will dutifully and precisely process a payment for $200,000 based on an input file, even if the intended amount was $2,000.00. The classic principle of “garbage in, garbage out” holds true.
The vulnerability lies in the fact that these systems are often passive recipients of data. They are designed to process, not to question. This design assumes that all data entry and upstream processes are perfect, and that a final human review is a sufficient control. In any complex organization, these are flawed assumptions.
Building Resilience with Automated Sanity Checks
A more resilient and mature approach to system design is to build in automated “sanity checks.” This is a set of pre-processing rules that the system uses to scrutinize its own data, automatically flagging anomalies and outliers for mandatory human review before a transaction is finalized.
This transforms the system from a passive calculator into an active participant in the control environment. For a payroll system, such checks are a non-negotiable feature for sound governance. At Abyrint, our view is that a robust payroll system must, at a minimum, perform the following checks:
- Automated Variance Analysis. The system should automatically compare each employee’s net pay in the current payroll run against their net pay in the previous period. Any variance exceeding a pre-defined threshold (e.g., +/- 15%) must be automatically flagged and held for review.
- New and Terminated Employee Validation. The system should generate a specific, mandatory review queue for all employees appearing on the payroll for the first time, as well as for all employees receiving a final termination payment. This ensures these high-risk changes are intentional.
- Authorization Cross-Referencing. If an employee’s salary or base pay has changed since the last period, the system should automatically query the HR module to confirm that a corresponding, approved change authorization was processed. A change in pay without a corresponding authorization is a critical control breach that the system should flag immediately.
- Absolute Value Thresholds. The system should have configurable absolute limits. For example, any single net payment above a certain amount (e.g., $20,000) or any total payroll run that is a certain percentage higher than the previous one should trigger an alert and require a higher level of approval.
Shifting the Burden of Review
This automated approach does not remove the need for human oversight. It makes that oversight far more effective. It shifts the burden from asking a manager to find a single needle in a haystack of 500 seemingly correct payroll entries, to presenting them with a pre-filtered list of five high-risk anomalies that require their direct attention and judgment.
A system that can sanity-check itself is inherently more trustworthy. Embedding these automated controls is a form of real-time, continuous monitoring. It is a core principle of the “Verifiable Systems” that the development sector needs, and a fundamental step in building technology that actively earns the confidence of its stakeholders.
