BetterFlow Team

ARTICLE

How AI Detects Timesheet Padding Before It Becomes a Billing Problem

Q: How AI Detects Timesheet Padding Before It Becomes a Billing Problem?

Timesheet padding isn't usually fraud - it's vague entries, generous rounding, and task bundling that inflates project costs. BetterFlow's AI quality scoring catches these patterns before they reach client invoices.

January 20, 2026

7 min read

BetterFlow Team

How AI Detects Timesheet Padding Before It Becomes a Billing Problem

Sarah manages a team of 12 at a digital agency billing $180/hour. Last month, she approved a timesheet entry from a junior developer: "Bug fixes and testing - 7 hours, Project Meridian." The entry looked normal. The hours were within range. The project was active. She approved it along with 80 other entries that week.

Three weeks later, the client questioned the invoice. Project Meridian had only two minor bugs reported that sprint, neither requiring more than an hour of work. The 7-hour entry wasn't malicious - the developer had lumped together exploratory testing, environment setup, and some unrelated research. But the vague description masked hours that should have been split across three different billing codes.

This is timesheet padding in its most common form: not deliberate fraud, but imprecise entries that inflate project costs and erode billing accuracy.

What padding actually looks like

The word "padding" conjures images of employees deliberately inflating hours. That happens, but it's the minority case. The far more common patterns are:

Task bundling: Combining multiple small tasks into a single large entry. "Development work - 6 hours" might actually be 2 hours of coding, 1 hour of meetings, 2 hours of code review, and 1 hour of email. Each activity might belong to a different project or billing category.

Generous rounding: A task that took 35 minutes becomes "1 hour." A 2-hour-and-15-minute meeting becomes "3 hours." Individually, each rounding seems trivial. Across a team of 20 people logging 40 entries per week, generous rounding can add up to 50-80 phantom hours monthly.

Context switching overhead: When developers switch between projects, there's legitimate overhead - reloading context, reviewing where they left off, checking messages. But some employees log this transition time to whichever project they're switching to, inflating that project's hours. Others log it to the project they're switching from. Neither approach is inherently wrong, but inconsistency creates billing inaccuracy.

Vague descriptions: Entries like "Research," "Planning," "Miscellaneous," or "Admin" without further detail are impossible to verify and easy to inflate. They're the timesheet equivalent of a receipt that just says "expenses."

How AI quality scoring catches these patterns

BetterFlow's AI verification system uses a GREEN/YELLOW/RED quality scoring framework that analyzes each timesheet entry across multiple dimensions. Unlike simple rule-based checks (e.g., "flag anything over 8 hours"), the AI understands context and evaluates entries holistically.

GREEN entries have specific descriptions, reasonable durations for the described work, correct project assignment, and consistency with the employee's historical patterns. Example: "Implemented user authentication flow for Project Meridian - integrated OAuth2 with existing session management. PR #247 submitted for review." This entry is verifiable, specific, and appropriately scoped.

YELLOW entries have potential issues that warrant human review. The description might be vague, the duration might seem high for the described task, or the pattern might be unusual for this employee. Example: "Bug fixes - 5 hours, Project Meridian." The system notes that this employee typically provides more detail and that 5 hours of bug fixes without specific bug references is unusual.

RED entries have clear problems. The described work doesn't match available evidence, the hours are inconsistent with deliverables, or required details are missing entirely. Example: "Development - 8 hours, Project Meridian" when GitHub shows zero commits and Jira shows no ticket transitions for that day.

Catching errors before the invoice

The critical difference between traditional timesheet review and AI-powered verification is when errors get caught. In traditional workflows, a manager reviews timesheets at the end of the week (or month), often rubber-stamping entries because they lack the context to evaluate each one. Problems surface only when clients question invoices - weeks or months after the work was done.

BetterFlow's verification runs as entries are submitted, providing immediate feedback to employees. When a developer submits a vague or potentially inflated entry, the system flags it in real-time. The developer can correct it while the work is still fresh in their memory - a much more accurate correction than trying to reconstruct what happened three weeks ago during an invoice dispute.

This shift from reactive to proactive verification changes the entire dynamic. Employees learn what good entries look like because they get immediate, consistent feedback. Managers spend less time on manual review because the AI has already identified the entries that need attention. Clients receive invoices backed by verified, detailed time data.

92% accuracy and confidence scoring

No automated system is perfect, and false accusations are worse than missed detections. BetterFlow addresses this with confidence scoring: each flag includes a confidence percentage indicating how certain the system is that an issue exists.

High-confidence flags (above 70%) are surfaced to the employee immediately. These are cases where the evidence strongly suggests an issue - like 8 hours logged to a project with zero corresponding activity in connected systems.

Medium-confidence flags (40-70%) are included in the manager's review queue but don't interrupt the employee. These might be entries that are unusual for this employee but not necessarily wrong - like a designer logging 4 hours to a backend project.

Low-confidence flags (below 40%) are logged for pattern analysis but not surfaced individually. Over time, if a pattern of low-confidence flags accumulates for a specific employee or project, the system escalates the pattern rather than individual entries.

With an overall accuracy rate of 92%, the system catches the vast majority of genuine issues while keeping false positives manageable. The 8% false positive rate means that roughly 1 in 12 flagged entries turns out to be legitimate - an acceptable trade-off that most managers prefer to the alternative of catching nothing.

Real impact on agency billing

For services companies, the financial impact of catching padding before invoicing is substantial. Consider a 30-person agency billing an average of $150/hour:

If 5% of logged hours contain some form of inaccuracy (conservative estimate), that's approximately 300 hours per month
If half of those inaccurate hours would result in over-billing (leading to client disputes) and half in under-billing (lost revenue), the exposure is significant in both directions
Catching and correcting even 60% of these issues (the realistic impact of AI verification) prevents $27,000/month in billing errors
Annualized, that's $324,000 in billing accuracy improvement - far exceeding the cost of any verification tool

Beyond the direct financial impact, agencies report that verified timesheets fundamentally change client conversations. When you can show a client exactly what was done, cross-referenced with commit histories and ticket progress, billing disputes effectively disappear. BetterQA experienced this firsthand - it's one of the reasons we built BetterFlow in the first place.

Getting started

Implementing AI-powered padding detection doesn't require overhauling your current timesheet process. BetterFlow layers verification on top of existing time entry workflows:

Connect your tools: Link GitHub, Jira, or other project management systems to provide the objective data the AI uses for cross-referencing
Establish baselines: The system needs 2-3 weeks of historical data to learn your team's normal patterns before it can effectively flag anomalies
Start with YELLOW: Begin by reviewing YELLOW-flagged entries rather than RED ones. This builds team familiarity with the system without feeling punitive
Iterate on feedback: When the system flags something incorrectly, mark it as a false positive. The AI uses this feedback to improve its accuracy for your specific team's patterns

The goal isn't to create a surveillance system - it's to create a quality system. Just as improving timesheet accuracy benefits everyone involved, catching padding before it reaches invoices protects both your revenue and your client relationships.

Conclusion

Timesheet padding - whether intentional or accidental - is a billing accuracy problem that traditional review processes consistently fail to catch. AI-powered quality scoring provides the objective, consistent verification that human reviewers can't maintain across hundreds of weekly entries. By catching vague descriptions, inflated hours, and categorization errors before they reach invoices, agencies protect their revenue and their client relationships simultaneously.

The shift from manual review to AI-assisted verification is less about catching bad actors and more about helping good teams produce accurate data. When every entry gets consistent, objective scrutiny, the result isn't a surveillance culture - it's a quality culture.

Sources & References

Published by BetterQA, an ISO 27001 and ISO 9001 certified company with 8+ years of experience in software quality assurance. According to research by McKinsey, data-driven project management improves team productivity by up to 25%. Last updated on February 22, 2026.