According to the study by Attain Consulting Group, about 90% of deductions are valid, and about 60-80% of the valid deductions are related to trade promotions. Whether the deductions are valid or invalid, analysts spend the same amount of time in resolving deductions. Deduction Management teams and analysts process hundreds of thousands of deductions every year. However, even if a deduction is valid, it still requires a set of manual, time-consuming tasks to be executed before an analyst is able to determine its validity. With more than half of all deductions being valid, this means that credit and A/R teams lose productivity that could have been spent on resolving and collecting on invalid deductions. Hence, there is value in weeding out invalid deductions and adding dollars back to the bottom-line.

9.1. The Deductions Paradox

Figure 18: The Deductions Paradox: Inability to prioritize deductions

As Figure 16 highlights that a high dollar value invalid deduction should be the highest priority for the deduction?s analyst whereas a low dollar value valid deduction should be at least priority. However, the analysts do not know which deduction is valid or invalid and hence, work on all the deductions. In other words, you do not really know whether a deduction is worth researching unless and until you have already worked on it. As a result of this, the analysts end up:

• Wasting time on verifying valid deductions
• Having very low recovery-rate due to lack of time to pursue invalid deductions
• Writing-off deductions that could have been invalid

9.2. How Top Organizations Automate Deductions Operations

Figure 19: Two Steps to Automate the Deductions Process

In order to reduce the time-crunch on deduction resolution, the AI enabled system predicts the validity of a deduction by analyzing several factors like payment history, customer account etc. and then compairing against the characteristics of the new deduction in question. †With these predictions, 70% of the deductions worklist is automatically resolved. For the remaining 30% deductions, the system provides recommendations. These recommendations are based on certain confidence categories. For example, it might predict for a certain deduction that it is 80% Invalid. This means that there is an 80% probability of this particular deduction being invalid. Similarly, it can predict 65% Invalid or 50% Invalid. On the basis of these recommendations, the deductions worklist gets re-prioritized to work on the highest probable invalid deductions first.

9.3. Deductions Automation Success Story: Keurig Dr Pepper

Keurig Dr. Pepper, the beverage manufacturer, had a high volume of deductions before leveraging automation. By leveraging automation to reduce the deductions workload and focus on root cause analysis, Keurig Dr. Pepper was able to recover an additional $1.4 Million in invalid deductions and also reduced the volume of deductions by 13%.