RETEL TECHNOLOGIES

Without a doubt, cash and product theft is one of the most damaging sources of shrink faced by retailers and restaurant chains. As such, many now employ POS systems that offer robust exceptions-based reporting (XBR) to flag events that might suggest theft or shrink. And, while these systems can often capture a decent number of events that are actual theft, there are a few critical capability gaps that prevent them from truly being a comprehensive theft prevention system. These critical capability gaps are our exceptions to XBR.

XBR Exception #1: Theft that occurs outside the POS. Smarter dishonest employees have come to recognize that certain transaction keys – voids, cancellations and refunds, for instance – are now used by XBR to identify events that should be flagged and reviewed for shrink. Therefore, if they can perform transactions entirely outside of the POS, there is no opportunity for an event to be flagged in the POS. Examples of this include sweethearting (giving product away for free to friends and family), leaving the cash drawer open between transactions, and selling hard-to-inventory items (such as drinks) without using the POS.

XBR Exception #2: Too many false positives. Much like that car alarm down the block, or the boy who cried wolf, too many false positives in any XBR system renders it useless as the personnel assigned to monitor the system either lose the capability to effectively find actual events, or simply lose interest in using the system altogether. While thresholds and triggers can be adjusted to account for false positives, this often comes at the expense of reducing the system’s capability to find actual events – a classic Catch 22 situation. This problem can be exacerbated significantly if multiple locations are using XBR simultaneously, thereby flooding the loss prevention or asset protection department with volumes of events that are too numerous to review.

While these problems are serious, they aren’t impossible to solve. ReTel’s new CashAudit service is designed specifically to both discover shrink events that occur outside of the POS, and also work directly with XBR systems to act as a second filter on captured events to deliver only those that need added LP review to internal auditors. The result? XBR as it should be, and true deterrents to employee theft no matter how many locations need to be monitored. To learn more about CashAudit, please click here.

QUICK LINK: Download the whitepaper here – no registration is required.

Within the retail industry, it’s commonly known that internal fraud – that is, losses that occur because of employees – account for the majority of thefts and losses suffered by retailers. With the one exception of organized retail crime, these internal losses are typically the biggest concern for retailers’ loss prevention (LP) and asset protection (AP) departments.

Internal loss comes in a variety of forms. At the simplest level, asset misappropriation activities such as skimming (taking cash before it hits the books) and larceny (stealing cash and product that is already on the books) can be pervasive throughout the organization, from the stores to the warehouse to corporate HQ. At a more complicated level, corruption activities such as embezzlement are often more isolated to senior management levels, and are often very difficult to detect.

In their latest Report To The Nations, the Association of Certified Fraud Examiners (ACFE) surveyed corporations and independent CFEs worldwide to discover three key sets of data:

1. The types of fraud events that organizations typically experience
2. The total dollar amount associated with each type of fraud event
3. The average amount of time it takes to detect a fraud event

The report was both fascinating and sobering. Overall, most organizations lose five percent of their annual revenues to fraud. Factor in the low margins of retailers, and this becomes an extremely significant hit to the bottom line. Even more troubling, however, is that fraud events often go undetected for as long as two years. Early detection, and technology that enables early detection, therefore becomes paramount to organizations that suffer regularly from these losses. It can make the difference between profitability and significant losses.

We have taken the 2010 Report To The Nations and analyzed the key points that are relevant to retail LP and AP professionals. In particular, we look at the impact that early detection has on reducing losses suffered as a result of fraud. The white paper is available for download by clicking on this link, and no registration is required. Please feel free to distribute this white paper, and your feedback is appreciated.

You may be familiar with the classic tale of the boy who cried wolf. As a shepherd, he spent all day watching his sheep graze peacefully, yet on occasion, would cry “Wolf!” to get a villager or two to run down to help. When they arrived, he’d yell “Gotcha!”, laugh, and the poor villager would sulk back to the village, angry that they wasted their time on his joke. Of course, we all know what happened next. By the time an actual wolf came around, and the boy cried “Wolf!”, no one came to his aid. The result? No more sheep, one very satisfied wolf, and an out-of-work shepherd.

If this boy was a video analytics system, he would have what we call a poor signal-to-noise ratio.

Signal-to-noise is one of the general terms used to describe how often an automated alerting system returns a true event versus a false event. It’s also what typically fails these systems in the real world, as system operators learn to ignore alerts because the majority of those received tend to be false. In the surveillance world, automated alerting has primarily been used for video analytics and surveillance system “health check” programs that check assets for operation and uptime. Here’s an example of how they fail because of signal-to-noise issues.

Let’s assume I manage an installation of 100 cameras in a medium-sized corporate facility. I assign one guard per shift to sit in an on-site central monitoring station to watch screens, get alerts, and be prepared as a first responder in case of an event.

First and foremost, I have a health check running on my surveillance system. If a camera goes down, the guard protocol is to receive the alert, check the monitor, verify the problem, and fix the problem (or call the surveillance installer to fix it). I have one of the best in breed health check systems. That means that each camera only generates 2 false positives a day. Over 100 cameras, that translates to 200 false positives a day. Let’s also assume that there are 5 real problems mixed in there. This translates to a signal to noise ratio of 1:40. For every 40 false events, there is 1 real event. The result? When the health check cries “Wolf!”, no one responds.

Next, I have video analytics on 20 cameras outside monitoring a virtual tripwire to indicate a perimeter violation. Here’s where it gets even more challenging. Next to and around the areas that these cameras monitor, I have branches swaying in the wind, animals (maybe even an occasional wolf) going to and fro, and the occasional early morning jogger who seems to like jogging along our fence. Therefore, on a daily basis, each camera is sending 50 alerts of a tripwire violation – that’s 1000 a day! And, on a daily basis, the perimeter is never violated. Not even on a weekly basis. Or monthly. Actually, last I remember, there was a break-in…two years ago? You get the picture.

So what is the solution to the problem of “The Camera That Cried ‘Wolf!’”? Unfortunately, many industry experts will tell you that even the best analytics and health check systems are still a ways off from effectively lowering the rate of false positives that they generate. That’s why ReTel is developing applications that will work right now with our proprietary two-layer auditing system to solve these problems and more. The broadest description of what we are developing would be a video noise filter that separates bad from good, returning only signal to the end user.

And our goal is not to replace analytics or health checks, but to make them better and more usable, so that they become acceptable features of an organization’s security and surveillance system. Bias is already creeping into end users’ opinions on analytics and health checks, which can make future adoption difficult – even after all the wrinkles are ironed out. That is unfortunate, because they can be truly useful tools to help manage an organization’s security and surveillance.

After all, when that real wolf comes, you want to make sure that there is someone there to hear the warning!