To meet the challenges named above, we work with different unsupervised fraud detection algorithms. Algorithms that are based on different principles, e.g. neighbor-, density-based or clustering. The generated results of the algorithms provide a score for each instance, which is merged with all other score results of the algorithms to a final score. Such a method reduces the possible inaccuracy of the unsupervised machine learning method enormously.
UTZ Certified
Machine Learning
UTZ Certified
Client
UTZ certified
Industry: Labeling
Headquarters: Amsterdam, Netherlands
Technologies
- RapidMiner
- Rapid Miner Anomaly Extension
- Python
Unsupervised Fraud Detection
Initial situation
UTZ certified is a non-governmental organization (NGO) that certifies products such as coffee, tea, cocoa and hazelnuts from farmers worldwide. This labeling confirms the standards set by UTZ certified sustainable farming practices, social criteria and strict monitoring of the supply chain to ensure transparency throughout the process.
Mission
Challenges
In order to solve fraud detection with machine learning, the availability of the labels, i.e. the labeling of the data, of the data to be examined must first be checked.
A distinction is made between supervised, semi-supervised and unsupervised detection.
In this case, no labels are available at all, so we are directly in the field of unsupervised machine learning. Due to the lack of labels, the unsupervised fraud detection method is the most complex of all three machine learning branches and can lead to inaccurate results under certain circumstances.
Technologies and Methods
To enable the process to run quickly and smoothly, we used RapidMiner, which is a widely used data mining tool.
Results
Summary
- Enabling the fight against fraud before harvesting is certified
- Future-oriented implementation with the help of machine learning
- Unsupervised Fraud Detection
- Implementation with RapidMiner and various algorithms
We will gladly advise you
Do you have a similar case? Arrange an appointment with us. We are looking forward to meeting you!