Pricing & Discounts – Smart Monitoring with Machine Learning (ML)
Pricing and discounts represent a significant vulnerability in ERP systems and sales management.
Traditional controls usually rely on deterministic checks: whether the sales price is below the official price list, whether a discount exceeds a certain threshold, or whether a transaction was approved by a manager.
In practice, such controls fail to identify sophisticated fraud or unusual behavioral patterns, and they can easily be bypassed in different ways within ERP systems (details omitted deliberately to avoid suggesting fraudulent methods).
Organizations typically face challenges such as:
- Irregular discounts granted without passing through the official control framework.
- Direct price list changes instead of reported discounts – making them invisible to traditional controls.
- Discounts applied through tax categories, creating a double loss: a discount for the customer and damage to tax reporting.
- Sales patterns showing that goods were never delivered to the customer, yet payment was collected – a scheme Detelix has detected in real time, preventing severe financial and reputational damage.
- Discounts granted to meet sales quotas or personal bonuses, even at the expense of company profitability.
- Inconsistencies in pricing between similar customers or across repeated transactions with the same customer.
- Creation of fictitious customers or internal transactions to manipulate sales reporting.
The Detelix Solution
Detelix applies a Machine Learning model that leverages data from multiple sources.
Instead of relying solely on deterministic tests, the system identifies anomalous behavior patterns, compares them to normal business processes, and flags suspicious transactions.
What makes the system unique:
- Continuous learning of changing patterns – adapting dynamically to the organization’s business reality.
- Detection of sophisticated fraud schemes that cannot be identified by standard exception reports.
- Cross-transaction analysis that highlights systemic issues even when each individual transaction looks “normal.”
- Targeted alerts with weighted risk levels – giving management clear guidance on where to focus attention.
Real-Life Examples
- Discounts provided by bypassing the control framework – for example, by changing price list categories.
- Discounts recorded under tax categories – a double loss of revenue and tax compliance.
- Sales patterns showing goods were not delivered but payments were collected – a sophisticated fraud exposed only through anomaly detection and process-level analysis.
- Repeated transactions with the same customer at inconsistent prices without valid business justification.
- Accumulated discounts leading to significant financial losses, granted to meet sales goals and secure bonuses.
Value to the Organization
- Detection of fraud and errors that traditional controls fail to uncover.
- Prevention of revenue leakage and operational losses.
- Transparency and consistency in product pricing and discounts.
- Ongoing monitoring based on real business behavior, not theoretical assumptions.
- Peace of mind for management – certainty that every transaction and discount is checked in real time.
