Uncover overtime fraud, effectively, every time.
Protect your organisation from financial losses with our advanced detection system, swiftly separating legitimate claims from costly fraud.
- Increase Profitability: Stop fraudsters draining your profits. We uncover $100,000+ fraud per case.
- Rapid Results: Upload data, then get a comprehensive, detailed, and actionable report within 72 hours.
- Sector-Specific Expertise: Our algorithms detect fraud patterns in your sector; business, policing, healthcare, emergency services and more.
- Prices start at $7,500
- Dedicated account manager
- 72 hour report turnaround time
- Trusted by government agencies
Advanced fraud detection technology
At a fraction of consultancy costs.
Our technology identifies individuals misusing overtime, typically saving companies over $100,000 per case discovered. High-priced consultancy expertise is now available as an affordable solution. No other product offers this level of protection.
Proven success in Western Australian Public Sector
Plus many other commercial businesses, not-for-profit organisations and government agencies…
Detect overtime fraud in 3 simple steps.
Our process is simple, smooth, and efficient.
Get Your Free Demo
Schedule a no-cost consultation and report walkthrough. Our team will show you how we detect fraud.
Upload Your Dataset
Send us your overtime transaction data via email or secure Onedrive link. Our process ensures complete confidentiality.
Receive Your Report
Within 72 hours, we deliver a clear, comprehensive report containing actionable information, detailed insights and revealing potential fraud cases
Understanding overtime & organisational risk
Overtime transforms employee hours into higher-rate compensation, creating the potential for fraud.
Overtime transforms employee hours into higher-rate compensation, creating the potential for fraud.
It’s often the simplest path for financial theft within organisations, paying out at a high rate with minimal effort from the fraudsters.
And while managers are often trusted to identify false claims, they can become overwhelmed with notifications or overlook suspect claims from ‘trusted’ staff.
By examining key factors, we identify patterns and anomalies that may indicate fraudulent activity.
Employee Reference
Unique identifier for each worker, enabling tracking across multiple records.
Date Paid/Claimed
Timestamps of compensation, revealing patterns in claim frequency and timing
Hours Claimed
Quantity of extra time worked, highlighting potential discrepancies or excesses.
Amounts Paid (Gross or Net)
Quantity of extra time worked, highlighting potential discrepancies or excesses.
Overtime fraud: Common methodologies
Understanding the strategies fraudsters use to exploit your overtime budget is crucial. These four tactics can drain your resources if left unchecked.
Ready to tackle overtime fraud in your organisation?
Contact us today to arrange a free consultation call
Why choose overtimefraud.com?
Our process efficiently detects fraud and generates substantial savings. Here's what distinguishes our service:
Significant Savings
Our advanced technology typically uncovers over $100,000 in fraud per case, often much more. By preventing ongoing losses, we help protect your bottom line and boost profitability.
The return on investment is substantial and swift, often paying for itself within weeks. Many clients see savings that transform their entire overtime budget.
Rapid Results
No need to wait months for consultant reports or complex audits. Simply provide us with your data via our secure link, and receive comprehensive analysis results within 72 hours.
This speed allows you to identify and address potential fraud promptly, minimising further losses. Quick action means you can stop fraud in its tracks, potentially saving millions over time.
Sector Specific Expertise
We understand the unique overtime challenges across multiple sectors – business, policing, healthcare, and more. Our algorithms are fine-tuned to detect fraud patterns specific to your industry, ensuring highly accurate results and reducing false positives.
This means you're getting insights specifically relevant to your organisation's structure and operations.
Stop overtime fraud eating into your budget
Contact us today to arrange a free consultation call and product demonstration.
Frequently Asked Questions
Do you need more information? Find the answers to your questions here.
Any business that spends money on overtime expenses. Typically our clients spend anywhere from $500k to $200m per annum on overtime expenses.
We have turned our expertise into an app which uses a range of complex algorithms to detect fraud. This allows us to provide the highest level of service, rapidly for our clients.
Both fraud types occur when the staff member realises overtime governance is overly trusting. In snowballing fraud losses increase in magnitude over time as the staff member has no limit or concern about detection. In blended fraud the staff member combines real claims with false claims to try and stay undetected. Our algorithms identify both kinds of overtime fraud.
With drip fraud the staff member claims small fraudulent claims on overtime. The majority of the claims may be fraudulent, but manages the problem of detection by keeping the total amount stolen low, even if the majority of claims are fraudulent. Our algorithms identify this kind of overtime fraud.
Collusion fraud is the type of fraud with the highest losses to the business and has been impossible to detect – before we developed our solution. Collusion fraud is where the manager and staff member work together to steal from the business, with the staff member submitting a large volume of false claims, and the manager agreeing to support the claims internally, with both parties dividing the stolen resources between themselves. The unique feature of this fraud type is that if questioned by human resource or an internal audit team, the manager will defend the overtime claims publicly in many cases insisting that this is a good spend of money and is a good return on investment for the business, making it hard to detect. Our algorithms identify the instances which reflect this type of risk.
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