Adaptive Fuel Fraud Detection Using Historical Baselines
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Solution Overview
Problem
Existing fuel card monitoring systems rely on predefined fixed rules for detecting fuel fraud, which are insufficient and difficult to maintain, especially as new fraud methods emerge.
Innovation Solution
The system determines if there is a mismatch between current refueling information and historical refueling information based on multiple types of data, including distance traveled, duration and progress of the refueling operation, and amount of fuel dispensed, using historical data as a baseline for non-fraudulent operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If predefined fixed rules are used for detecting fuel fraud, then the detection system is simple to implement, but the system becomes insufficient and difficult to maintain as new fraud methods emerge
Solution Approach 1:
The patent transitions from static predefined rules to dynamic adaptive rules that automatically adjust based on historical data and emerging fraud patterns. The system continuously learns from new fraud cases and modifies detection criteria without requiring manual reconfiguration, thereby maintaining both simplicity and adaptability simultaneously
Solution Approach 2:
The detection system performs self-updating by automatically analyzing historical refueling data and emerging fraud patterns to generate new detection rules. This self-service capability allows the system to adapt to new fraud methods without external intervention, resolving the contradiction between ease of implementation and adaptability
2Reliability
If multiple types of data are analyzed to improve fraud detection accuracy, then detection comprehensiveness improves, but system complexity increases
Solution Approach 1:
The system employs a multi-functional analysis platform that processes diverse data types (refueling patterns, vehicle data, location information, temporal patterns) through unified algorithms. This universal approach maintains high detection accuracy while managing complexity through standardized processing frameworks that can handle multiple data sources simultaneously
3Adaptability or versatility
If historical refueling information is used as a baseline for detection, then the system becomes more adaptive, but data processing requirements increase
Solution Approach 1:
The system applies partial analysis by focusing computational resources on specific high-risk patterns and anomalies rather than processing all historical data uniformly. By selectively analyzing only the most relevant historical refueling information for each detection case, the system achieves high adaptability while significantly reducing overall data processing requirements and energy consumption
Data Source
AI summary
A method for detecting fuel fraud when refueling a vehicle at a filling station comprises obtaining, in conjunction with a current refueling operation in which the vehicle is refueled at the filling station, current refueling information, determining if there is a mismatch between the obtained current refueling information and historical refueling information which is based on a plurality of past refueling operations in which comparable vehicles were refueled, and raising a potential fraud warning based at least in part on a determined mismatch. The current refueling information comprises at least: first information indicating a distance the vehicle traveled since at least a previous refueling operation, second information indicating a duration and/or progress of the current refueling operation, and third information indicating an amount of fuel dispensed by a fuel pump in the current refueling operation. One or more machine-readable media, a server installation and a system are adapted to perform corresponding operations or cause corresponding operations to be performed.