Suspicious Order Detection Using Historical Data Analysis
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Solution Overview
Problem
Current systems for identifying suspicious orders of controlled substances rely on outdated threshold-based calculations that do not account for historical order information of individual customers or other customers within the same family, leading to potential missed flags and compliance issues.
Innovation Solution
A system and method that utilize processors to receive and analyze orders by querying historical data, applying checks based on upper control limits and thresholds derived from historical quantities and order frequencies to flag suspicious orders, including exponentially-weighted moving average control charts and daily rate control charts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a simple threshold-based calculation is used to determine suspicious orders, then the system is easy to operate and implement, but the measurement precision and reliability of suspicious order identification deteriorates because it does not account for historical order information or customer-specific patterns
Solution Approach 1:
The system dynamically adjusts thresholds based on historical order data and customer-specific patterns. Instead of using fixed thresholds, the system continuously learns from past orders and adapts the suspicious order detection criteria to reflect actual customer behavior patterns, thereby improving measurement precision while maintaining ease of operation through automated adjustments
Solution Approach 2:
The system performs self-service by automatically analyzing historical order information and updating its own detection parameters without requiring manual intervention. The system autonomously identifies customer-specific ordering patterns and adjusts its suspicious order detection thresholds accordingly, improving precision while keeping the interface simple for users
2Ease of manufacture
If fixed thresholds are used for suspicious order detection, then the system is simple to implement, but the adaptability deteriorates because thresholds become outdated and do not reflect changing customer behavior patterns
Solution Approach 1:
The system implements feedback mechanisms where detection results and new order data continuously flow back into the system. This feedback loop allows the system to learn from actual customer behavior and automatically adjust its detection parameters, ensuring adaptability to changing patterns while maintaining simple implementation through automated processes
Solution Approach 2:
The system performs preliminary analysis of historical order data to establish baseline customer patterns before new orders are evaluated. By pre-processing and storing historical information in structured formats, the system prepares adaptive detection parameters in advance, enabling quick adaptation to new orders without complex real-time processing
3Speed
If threshold-based detection without historical analysis is used, then the processing speed is fast, but the reliability of suspicious order identification deteriorates because the system cannot distinguish between legitimate and suspicious orders
Solution Approach 1:
The system performs preliminary organization of historical order data into structured formats and pre-calculates baseline customer patterns before suspicious order detection is needed. This advance preparation allows the system to quickly compare new orders against established patterns without performing complex historical analysis in real-time, thereby maintaining speed while improving reliability
Solution Approach 2:
The system dynamically adjusts detection parameters based on historical patterns but uses optimized algorithms that leverage pre-computed statistics and cached historical data. This dynamic adaptation occurs efficiently by comparing new orders against pre-analyzed customer profiles, maintaining fast processing while significantly improving reliability through pattern recognition
Data Source
AI summary
Various embodiments of the present invention generally relate to systems and methods for identifying suspicious orders for controlled substances based on performing checks on a received order using historical information on previous orders. According to various embodiments, these checks may include: (1) comparing the order quantity for the received order with the order quantity history for the customer who placed the order for the controlled substance; (2) comparing the order quantity and the days since the last order was shipped to the customer for the same controlled substance with the order quantity history and the days between orders for the particular customer and controlled substance; (3) comparing the order quantity with order quantity history for orders received by customers of the same type for the controlled substance; and (4) comparing the order quantity with order quantity history for any customer for the particular controlled substance.