Fuel Card Exception Detection via Telematics and Fuel Level Correlation
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Solution Overview
Problem
Businesses face monetary losses due to undetectable fraudulent fuel card usage by drivers, including personal purchases and misuse of fuel cards, as well as inefficiencies in tracking driving habits and fuel consumption, which affect operational costs and profitability.
Innovation Solution
A system that includes a server receiving fuel card transaction data, fuel level data, and telematics data to detect exceptions such as fraudulent purchases and misuse by comparing the location and quantity of fuel purchased with the vehicle's fuel levels, generating alerts for unauthorized usage, and analyzing driving patterns to optimize fuel consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional fuel card systems are used without integrated tracking, then drivers have operational flexibility, but fraudulent usage and misuse cannot be detected
Solution Approach 1:
The patent combines multiple previously separate systems (fuel card transaction system, telematics tracking system, fuel level monitoring system) into an integrated exception detection system. The server receives and correlates data from fuel card transactions, telematics devices tracking vehicle location and usage, and fuel level sensors, merging these data streams to enable comprehensive fraud detection without requiring a completely new system architecture.
Solution Approach 2:
The integrated system performs multiple functions simultaneously: it processes fuel card transactions, tracks vehicle location via telematics, monitors fuel levels, detects fraudulent usage patterns, and generates alerts. This multi-functional approach allows a single system to address various aspects of fuel management and security, reducing the need for separate specialized systems.
2Reliability
If comprehensive tracking of fuel card usage and vehicle operations is implemented, then fraudulent usage can be detected, but system complexity and data processing requirements increase
Solution Approach 1:
The patent extracts the complex exception detection logic and data processing requirements from the local vehicle systems and centralizes them on a remote server. The telematics devices and fuel card readers generate data, but the sophisticated analysis, pattern recognition, and exception determination are performed server-side, reducing the processing burden on individual vehicle components and simplifying their design.
Solution Approach 2:
The server acts as an intermediary that receives data from multiple sources (fuel card transactions, telematics devices, fuel level sensors), processes this information through exception determination logic, and generates alerts or notifications. This intermediary approach allows complex data processing to be centralized while keeping the client-side devices relatively simple.
3Reliability
If real-time monitoring of fuel levels and transaction data is implemented, then exceptions can be detected promptly, but energy consumption and system resource usage increase
Solution Approach 1:
The system implements periodic data collection and transmission rather than continuous real-time monitoring. Telematics devices and fuel level sensors periodically report data to the server, and the server periodically processes this information to detect exceptions. This periodic approach reduces energy consumption compared to continuous monitoring while still providing timely detection of fuel card usage exceptions.
Data Source
AI summary
Determining a fuel card usage exception with the use of a computer that receives fuel card transaction data responsive to a fuel purchase event. The fuel card transaction data identifies a quantity of fuel purchased for a vehicle using a fuel card. In addition, the computer receives first fuel level data and second fuel level data of the vehicle that identifies a quantity of fuel present in a fuel tank of the vehicle prior to a fuel purchase event and after a fuel purchase event respectively. The computer can determine an exception associated with the fuel purchase event based on the fuel card transaction data, the first fuel level data, and the second fuel level data. Responsive to detecting an exception associated with the fuel purchase event, the computer may generate an alert and transmit the alert for presentation by a computing device of a user.