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Fraud Detection: A Data Science Approach
In the fight against fraud, data scientists play a crucial role in developing innovative solutions to detect and prevent fraudulent activities. In this article, we will explore two different approaches to fraud detection using machine learning techniques.
Threshold Setting for Fraud Alert Rule
The first step in any fraud detection algorithm is setting a threshold for the alert rule. This threshold determines whether a transaction is classified as legitimate or fraudulent. A good starting point for the threshold comes from the final value of the loss function at the end of the learning phase. For our example, we used a threshold of 0.009.
Autoencoder Neural Network
Our first approach uses an autoencoder neural network to detect fraud. The autoencoder is trained on a dataset of legitimate and fraudulent transactions, and then applied to new data to classify it as either legitimate or fraudulent. Our experiment shows that the autoencoder can accurately detect fraudulent transactions with a high degree of accuracy.
Key Features
- Trained on a dataset of legitimate and fraudulent transactions
- Applied to new data to classify it as legitimate or fraudulent
- Accurately detects fraudulent transactions with a high degree of accuracy
Isolation Forest
Our second approach uses an Isolation Forest algorithm to detect outliers in the data, which are often indicative of fraudulent activity. The Isolation Forest is trained on a dataset of legitimate and fraudulent transactions, and then applied to new data to identify outliers. Our experiment shows that the Isolation Forest can accurately identify fraudulent transactions with a high degree of accuracy.
Key Features
- Trained on a dataset of legitimate and fraudulent transactions
- Detects outliers in the data indicative of fraudulent activity
- Accurately identifies fraudulent transactions with a high degree of accuracy
Deployment
Both approaches require deployment to be used in real-world applications. For our autoencoder approach, we deployed the model as a web application that takes in new transaction data and outputs a classification of legitimate or fraudulent. For our Isolation Forest approach, we deployed the model as an email notification system that sends alerts to credit card owners when suspicious transactions are detected.
Conclusion
Fraud detection is a complex problem that requires innovative solutions. By using machine learning techniques such as autoencoders and Isolation Forests, data scientists can develop accurate fraud detection algorithms that can be deployed in real-world applications. While these approaches may not be as accurate as supervised classification algorithms, they are useful alternatives when labeled fraud data is not available.
About the Authors
Kathrin Melcher is a data scientist at KNIME and holds a master’s degree in mathematics from the University of Konstanz, Germany. Rosaria Silipo, Ph.D., is a principal data scientist at KNIME and author of 50+ technical publications.
Learn More
For more information on KNIME, please visit www.knime.com and the KNIME blog.