Every HRMS with a regularisation workflow eventually accumulates a queue of correction requests that HR spends real hours each month approving, following up on, or chasing documentation for. The instinct is usually to streamline the approval workflow — faster forms, fewer clicks. That helps at the margins, but it doesn't touch the actual volume, because the workflow isn't the problem. The requests are a symptom of something upstream.

Regularisation requests are a diagnostic, not just an admin cost

Before optimising the approval process, it's worth pulling three months of regularisation data and sorting it by cause, not just by employee or department. In most organisations, requests cluster around a small number of root causes rather than being evenly spread across a hundred different reasons. Once you can see the clusters, you're looking at operational problems, not attendance-policy violations.

Common clusters worth checking first

  • A specific site or shift with a geofence set too tight. If one location generates a disproportionate share of "outside boundary" corrections, the fix is a radius adjustment, not employee counselling.
  • A recurring commute or connectivity issue tied to one route or time slot. Late check-ins clustering around a specific transit disruption window point to a real-world cause the policy should account for, not ignore.
  • A device or app issue affecting a subset of employees. Older phones with weak GPS or camera hardware produce more failed check-ins — this shows up as a spike in one employee segment, not a random spread.
  • A policy ambiguity that different managers are interpreting differently. If regularisation approval rates vary widely by manager for what looks like the same underlying situation, the policy language — not the employees — needs fixing.

What actually reduces volume, in order of impact

  1. Fix the geofence and shift-timing edge cases first. These are usually the single largest cluster and the easiest to correct — a radius change or a five-minute grace window removes a request category entirely rather than making it faster to approve.
  2. Give employees a way to self-correct minor issues before submission. A short in-app prompt at the moment of a failed check-in ("outside geofence — request review now?") resolves the situation in the same interaction instead of generating a support ticket days later.
  3. Set clear, published thresholds for what needs regularisation at all. Not every five-minute discrepancy needs a formal workflow. A defined grace window, applied consistently, removes a category of requests that never should have existed.
  4. Route routine, low-risk corrections to auto-approval. Once you know the common, low-risk patterns (site-radius edge cases, known connectivity dead zones), those can bypass manual review entirely, freeing HR time for the requests that actually need judgment.
Most attendance regularisation queues shrink the most not from a faster approval button, but from removing the two or three recurring causes that shouldn't have generated a request in the first place.

The compliance angle Indian HR teams can't skip

For organisations operating under India's Shops and Establishments Acts or standing orders, attendance records feed directly into wage and leave calculations, which makes an unresolved regularisation queue a compliance risk, not just an admin backlog. A clean audit trail — the original check-in, the reason for correction, and the approver — matters as much as resolving the request quickly. Any regularisation workflow worth using should preserve that trail automatically rather than relying on someone remembering to note it in a spreadsheet.

See the regularisation workflow

LaaynHR logs the original check-in, the correction reason, and the approver automatically — no separate audit spreadsheet.

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Measuring whether it's actually working

Track regularisation requests as a rate — requests per 100 employees per month — rather than a raw count, so the trend is visible independent of headcount changes. A steady decline over two to three cycles after a specific fix (a radius change, a grace-window policy, an auto-approval rule) confirms the fix worked. If the number doesn't move after a change, the cluster you fixed wasn't the one driving the volume, and it's worth re-running the diagnostic rather than tightening the approval process further.

HR operations reduce regularization requests HR attendance correction workflow HRMS regularisation policy