As per a recent post, fraud is permeating your supply chain and your current iZombie platform needs to take a lot of the blame as it lulls you into a false sense of security when it should be sounding all the warning bells and sirens at its disposal.

So what kind of platform do you need?

As per our last post, simply put, a platform with good market intelligence, encoded expert intelligence, (hybrid) AI algorithms, and other modern features that can detect common types of fraud and stop it dead in its tracks. To give you a better idea of what these platforms look like, we’re going to address more types of fraud an organization may encounter and what a platform would need to detect it.

Abnormal Vendor Selection

In our last post we talked about how a good platform can detect unacceptable cost inflation via metric inflation designed to target a certain supplier. This could be done for many reasons — direct or indirect kickbacks to the buyer, financial gain to the immediate or extended family of the buyer, a tit-for-tat arrangement (where the supplier agrees to select a vendor chosen by the buyer that will directly or indirectly benefit the buyer).

But not all abnormal vendor selection is done by way of metric inflation. Some is done by way of weighting a particular geography, a particular type of responsibility or compliance program, a particular association, or something else unusual that will choose a particular vendor that would not normally be used.

A good platform with good analytics and machine learning can detect when unusual characteristics are applied to vendor selection.

Unusual Payment Patterns

Just because there is an invoice that is accepted against a (blanket) PO or for a category / amount that does not require a PO, that is approved by a senior manager or direct, that doesn’t mean that the payment is okay. But a single payment is hard to detect. However, if similar payments show up over and over again and they are not for regular recurring payments like rent, utilities, predictable support services, it might be an indicator of fraud. A good platform will be able to classify and detect repeating payments of this type that are not expected.

This requires good trend analysis applied to non-PO categories not identified as having regular payments of a specific type.

Too Frequent (Automatic) Order Triggers

When a contract for a category is cut, there is an expected demand against an expected order schedule. As a result, there are expected (re) order schedules that shouldn’t vary too much. If they do, either someone is adjusting minimum stock on hand levels or a POS is submitting sales numbers that are higher than actuals to cause too frequent re-orders. But since a good system can compare planned schedules to expected schedules based on market conditions to actuals, this can be detected.

Again, good analytics with dynamic trend analysis against plans and modified plans based on market conditions derived from market data.

Lost Returns

If a higher than usual number of products get marked as defective but a considerable percentage of these don’t make it back to the supplier for credit, that’s typically indicative of fraud. Typically, someone, somewhere is marking good products bad, marking them to be returned, but then insuring they go missing somewhere along the line. Usually a case of high-value product at a time.

But a platform that maintains a record of average defect rates by category (and supplier), average return success by category (and supplier), and average return success for the organization can compute when theft is very likely.

Analysis of rates against expected rates and identification of unusual deviations.

Fixed Asset Fraud

If the platform contains complete service history, industry metrics for average service requirements for the platform by hour of use, and average upkeep and overhead costs, and all of a sudden the service requirements and upkeep costs double for recorded hours of use, then there is a good chance that the asset is being used for non-sanctioned purposes. This is still fraud and theft from the company.

Analysis of costs and life-spans against expected costs and life-spans and identifications of costly deviations.

And again, while platforms aren’t the entire answer, as they might not be able to pinpoint whether it is a warehouse worker, a carrier (driver), or collusion between the two in “lost” return theft, they can certainly detect quickly when the fraud is happening, and then the organization can take steps to identify the perpetuator(s).