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Red Amber is a complete suite of six modules, all targeted at various problems that retailers face with in-house rewards programs. In addition to detecting unusual patterns of points earning, available modules also detect suspicious patterns of points adjustment or points transfer, nonconforming gift redemption activity, non-qualifying member status upgrades, and unexpected changes in individual points balances.
This module identifies and reports unusual transaction activity or points earning within a loyalty program or discount scheme. The module applies a risk score to each reported case, indicating the severity of risk that the pattern observed is not that of a real customer. The patent-pending approach for doing this goes beyond mere counts of transactions, incorporating other risk factors which further decrease the probability that the pattern observed is a real customer, or which, on the other hand, mitigate that risk.
The rules engine can be configured by a designated Admin user, and decision rules can be customized based on various criteria, such as per line of business, for example.
The system also has features that allow it to learn over time, based on user feedback, as cases are investigated and closed. As a result, the methods employed by this module significantly reduce both Type I and Type II error versus simpler methods for identifying cashier-type fraud within a loyalty program.
As with all six modules, all alerts are delivered to the dashboard of the designated person(s) responsible, and the system tracks whether the designated recipient opened the report and, if so, whether they closed the case within the amount of time designated. Cases that are not closed within the allotted SLA are automatically escalated to the designated second-level resource, such as Loss Prevention, for example.
Points adjustment is an accommodation that is allowed by many programs for situations where a loyalty card was defective, or the terminal was offline or other reasons, defined within the rules of that program. Although such adjustments are justified and fair, it is easy to see how a points adjustment process may be vulnerable to fraud, even if multiple approvals are required for such adjustment.
For example, designated approvers might lose track of the fact that certain account numbers appear frequently over time in requests for points adjustment, or an approver could be an accomplice.
Therefore, the Points Adjustment Module is designed to detect if there is systematic tendency to issue points adjustments for particular cards. Points adjustments for old transactions that exceed a user-defined aging threshold can also be flagged, and a lifetime total points adjustment threshold can be defined by users as well.
Points transfer between cards is allowed by some programs under certain conditions, such as death of cardholder, or for consolidation of points by different family members, to claim a larger reward. As with points adjustment, this is another activity that creates opportunities for abuse.
For example, employees may search customer records for inactive cards with unused points, and find ways to transfer points from such cards to a target account. For this reason, the Points Transfer Module reports if there is a systematic tendency to transfer points to particular cards.
In addition, a multiple donor alert will flag cases where points from two or more cards have been transferred to the same target card. There is also a repetitive transfer alert. Designated users can also be alerted if points were transferred in an amount that exceeds a pre-defined value threshold.
Depending on your program rules, redemption might be allowed only for certain cards and not for others, or only for certain items and not for others. If non-qualified cards are given a redemption privilege, or if non-designated SKUs are distributed as gift rewards, then those activities will be flagged.
For example, some programs have a temporary rewards card, and many such programs do not allow awards redemption by such cards until the account has been converted to permanent status. Also, some programs have wholesaler cards, or other classes of accounts that are not intended to be used to earn points or to redeem gifts.
Although loyalty systems were probably designed so that it is theoretically impossible to redeem gifts using such nonqualified cards, this alert acts as a second layer of defense to report such redemptions if somehow they do occur.
In a similar way, many programs do not allow redemption for cash equivalents such as gift cards. In other cases, certain SKUs are allowed as gifts, but not others. Either way, this module will identify and flag all non-qualified items that were redeemed as gifts.
After cards have expired past a user-defined grace-period, many program rules state that all points would be reset to zero for such accounts. However for various reasons, this might not happen as expected in every case, leading to issues with liability accounting, and other problems. Therefore, this module will report and list any expired cards that have a non-zero points balance, showing the points value of each such card.
Also, for most loyalty programs, it should theoretically be “impossible” for a card to have a negative points balance, but in practice, certain types of rare events can cause cards to carry negative points balances. Therefore, this module will flag such cases, so they can be quickly investigated to uncover and correct the problem. In addition, this module flags accounts that suddenly increase in value by more than a user-defined threshold, so that an authorized user can review the situation.
Elite cards should only be given to customers who have achieved and maintain the required spending levels – not as a favor or accommodation. However, this is an area where operational discipline can sometimes be weak. Therefore, this module produces an exception report that flags issuance of elite cards whose originating card is not included among the accounts that are qualified for upgrade to elite status.
In some cases, store managers may be authorized to upgrade certain types of members to elite status, such as bloggers, press and VIPs, so the system also has the ability to filter non-qualified elite card upgrades by reason type.
If you are a retailer with a points-based rewards program, then you have a problem.
Your cashiers and other employees are diverting points to create rewards for themselves.
That’s for sure. No rewards program escapes.
Of course, your program can generate a lot of insight and value for you and for your best customers, but why not make the program even more profitable, by preventing internal fraud and abuse of the program?
As you know, cases of rewards fraud or abuse tend to escalate internally, even all the way to the Board level – creating undue distractions and stress for leadership in Marketing and Loss Prevention. You don’t need that – especially since these cases can be detected early and managed effectively
Download the report to find out how.
In the classic abuse scenario, cashiers retain a rewards card that belongs to them or to a family member or friend. After they verify that the customer is not a rewards program member, they swipe their own card, earning points, based on the purchases of others.
One of our clients is a $4 billion industry-leading retailer, with 50 malls and more than 200 supermarkets, hypermarts, grocery stores, and department stores. They generate more than 500 million transactions per year. Despite excellent operational discipline and good internal IT support, they uncovered significant cases of fraud and abuse on the very first day of implementation.
Suspicious activity is detected, and accounts are flagged as High Risk, Very High Risk or Severe Risk.
Authorized users can drill down into transaction details, to review transaction amount, time of transaction, POS terminal number, cashier ID, tender type and other details.
There are permission-based data views, such as store level, district level, regional manager level, line-of-business view, Loss Prevention view, and Admin view.
And it’s not just reporting. Red AmberTM uses a case management approach that helps you to ensure that actions are taken, findings are documented, and cases are closed.
Accumulated losses from loyalty fraud can reach many hundreds of thousands of dollars, but this might not even be the most harmful part of the problem. Perhaps an even bigger concern is the effect of this activity on your data models.
If demographics of cashiers, or their family and friends become part of a model for “high-value customers” and if shopping carts of completely random nonmembers are used to describe the purchase habits of this “high value” group, then your data models could suffer a lot.
What this means is that Red AmberTM can be an impact player for your targeted marketing, by helping to improve the accuracy and precision of your data models.
Existing fraud-detection solutions are directed at spending. Points earning is a completely different problem, and one that requires very different methods to detect and manage.
Some retailers deal with cashier rewards card abuse in a reactive manner. In other cases, in-house IT teams run exception reports that flag cards with high transactions. This is not effective, because there’s a long tail of high velocity card users in most programs, so exception reports based just on transactions produce many false alerts.
Red Amber uses stacked algorithms that are very effective at identifying improbable patterns of points earning activity, based on many risk factors. This greatly reduces false-positive alerts.
A fraud prevention suite designed for points earning – which is a very different type of problem from spending.
The user interface allows customized rules by line of business or other factors.
8% of sales credited to your loyalty cards may be for transactions where the card is not from the actual customer. That’s a waste of money.
If cashiers become part of a data model for “high-value customers” and if shopping carts of completely random non-members are used to describe the purchases of this “high-value” group, then your data models will be compromised.
The patent-pending approach goes beyond transaction counts, and is highly effective at reducing both Type I and Type II Error.
Red Amber creates cases that must be closed. There are also second-level alerts to Loss Prevention, for cases that are aging without being closed within the time allotted to do that.
A suite of six related modules, covering the complete loyalty life-cycle.
Can integrate with a wide variety of POS, databases and operating systems
Managing Director, Lassu
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