The Problem Area:
Fraud within vending machine operations and the associated delivery routes poses a significant challenge for companies. Unauthorized access, tampering, and theft compromise the integrity of transactions and impact the overall efficiency and profitability of the vending business.
Vending machine operators, route drivers, and the company management are the primary stakeholders facing these challenges. They would benefit from a robust fraud detection system that enhances security, minimizes losses, and ensures the smooth functioning of vending operations.
The big idea:
Machine learning can provide solutions by analyzing patterns and anomalies in data to predict potential vulnerabilities. By leveraging historical data on transactions, machine statuses, and driver routes, the system can identify unusual activities and trigger alerts or interventions to prevent fraudulent behavior. Research on similar applications of machine learning in fraud detection, such as anomaly detection algorithms and predictive modeling, will inform the project approach.
Transaction Data:
- Transaction ID: Unique identifier for each transaction.
- Timestamp: Date and time of the transaction.
- Machine ID: Identifier for the vending machine.
- Product ID: Identifier for the product purchased.
- Transaction Amount: Amount of money spent in the transaction.
- Payment Method: Method used for payment (e.g., cash, credit card).
- Location: Geographic location of the vending machine.
Machine Status Data:
- Machine ID: Identifier for the vending machine.
- Timestamp: Date and time of the status update.
- Status Code: Code indicating the current status of the machine (e.g., operational, under maintenance, out of order).
- Temperature: Temperature inside the vending machine.
- Stock Level: Level of product inventory in the machine.
- Revenue: Total revenue generated by the machine.
Route Data:
- Driver ID: Unique identifier for the route driver.
- Route ID: Unique identifier for the route.
- Timestamp: Date and time of each stop on the route.
- Location: Geographic location of each stop.
- Distance Traveled: Distance traveled between consecutive stops.
- Duration: Duration of time spent at each stop.
- Deviation from Planned Route: Indicator of any deviations from the planned route.
Incident Reports:
- Incident ID: Unique identifier for each reported incident.
- Timestamp: Date and time of the incident report.
- Description: Description of the incident or suspicion reported.
- Location: Geographic location of the incident.
- Severity Level: Level of severity assigned to the incident (e.g., low, medium, high).
- Action Taken: Action taken in response to the incident (e.g., investigation, machine maintenance).