How to Catch Fraud in Your Incentive Program
Companies lose out on billions of dollars every year through their incentive programs as a result of non-compliance and fraud (i.e., duplicate claims, inconsistent invoices, misinterpreted program requirements, and much more).
Without a proper process for review, fraud or misinterpreted claims in your rebate program can detrimentally impact your bottom line and damage your credibility.
In order to reduce and prevent fraud payouts here are a few best practices your organization should be taking.
Point Of Sale (POS) Exports
Receiving transactional data as a direct upload from the point of sales terminals out in your channel removes a great deal of opportunity for sales associates (SAs) or consumers to enter mistakes or flat-out false claim data. Starting with claim data pulled directly from the terminal which transacted the sale will, in many cases, provide the cleanest dataset for processing.
84% of organizations rely on spreadsheet software such as excel to support sales compensation administration, making their program vulnerable to fraudulent behavior and common manual errors.
Implementing a digital platform will be a time commitment and investment for your organization, however, going digital will eliminate common manual errors and save your channel management, in the long term, time and resources.
Require a Copy of Their Invoice and be Weary of Invoice Sequence
Are you asking your SAs to submit a copy of the store invoice along with sales incentive claims? Does your rebate program require that consumers submit a copy of the store invoice or delivery slip?
If your organization is paying out to an individual or business based on a transaction that they’ve engaged in, they should be providing proof of that transaction.
Provided that you have a scalable system for capturing and sorting all of your invoice data, this is an easy thing to keep an eye out for. By receiving store invoices with claim submissions, you start to build a baseline of what the logical flow of invoice numbers looks like. This can generate some probing questions such as: “does this claim fit into the logical flow or does it look like it’s from two years ago?”. Out of place invoice numbers are a great indicator that the claim needs a closer look.
A digital platform will look at invoice numbers out of sequence or with incongruent formatting from that channel and flag it for a closer look.
Track ZIP Codes to Combat Fraud
Let’s say that you have a rebate claim that shows a customer purchased a suite of kitchen appliances from a retailer in Knoxville, TN. The same claim shows the customer address as being in Aberdeen, WA. While this scenario is indeed possible, it is statistically unlikely that this transaction is legitimate and worthy of a closer look.
Sorting through ZIP codes provides the retailer with a clear view of vast gaps in the code syntax, flagging claims like the one mentioned above for future audit.
Start Tracking Serial Numbers to Combat Fraud
Ideally, manufactured products of a certain value are stamped with a unique identifier such as a serial number or perhaps a coding system unique to your brand. You can use the unique identifier of the serial number from your physical goods to associate with each dealer, each customer and subsequently, each rebate you process.
Capturing this number at the time of your claim input is by far the simplest and best way to prevent paying out on the wrong claims. Any claims containing duplicate serial numbers must be flagged for audit.
Provide Human Oversight
Lastly, having human oversight is an effective way to deter any fraudulent behaviour or misinterpreted claims.
When segmenting the reporting of your claims processing by territory, it is highly valuable to run those reports by your field sales managers. They might be able to recognize some of the top performers overtime. For those that are unfamiliar, it’s an opportunity to get to know who that top sales person is or potentially stop the top fraudster.
By following these best practices, it has never been easier to point out data anomalies, saving you time, resources, and most importantly capital.