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Fraud Detection: A Comparative Analysis of Three Approaches
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In an effort to combat the growing menace of credit card fraud, data scientists have developed a range of innovative solutions. In this article, we will explore three distinct approaches to detecting fraudulent transactions: a supervised machine learning algorithm, a neural autoencoder, and an Isolation Forest.
Threshold Setting: A Critical Parameter
To optimize the performance of these algorithms, it is essential to define a threshold for fraud detection. For the autoencoder model, we set the threshold at 0.009, which can be adjusted based on the desired level of conservatism. Similarly, the Isolation Forest algorithm employs a decision threshold of δ=6, which can also be fine-tuned.
Autoencoder Model Performance
Our study employed an autoencoder neural network to detect fraudulent transactions. The model achieved a high accuracy rate, as shown in Figure 8. However, it is essential to note that this approach requires a large dataset with both legitimate and fraudulent transactions.
Isolation Forest Algorithm
The Isolation Forest algorithm, on the other hand, can be used when labeled fraud data is scarce or unavailable. This approach utilizes an unsupervised learning technique to identify anomalies in the data. As shown in Figure 8, the Isolation Forest algorithm achieved a moderate level of accuracy, but it is essential to carefully evaluate its performance.
Deployment and Email Notification
The deployment workflow for both models involves applying the trained algorithms to new incoming transactions and sending email notifications to cardholders when fraudulent activity is detected.
Comparison of Approaches
While each approach has its strengths and limitations, our study demonstrates that a combination of these methods can be used to develop a robust fraud detection system. The supervised machine learning algorithm provides high accuracy, but requires labeled data; the autoencoder model offers moderate accuracy without requiring labeled data; and the Isolation Forest algorithm is a last resort when no labeled data is available.
Conclusion
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Fraud detection is a complex problem that requires innovative solutions. By comparing three distinct approaches to detecting fraudulent transactions, this study highlights the importance of carefully evaluating each method’s strengths and limitations. As the field of data science continues to evolve, we can expect to see even more sophisticated solutions emerge to combat credit card fraud.
Authors
- Kathrin Melcher, Data Scientist at KNIME
- Rosaria Silipo, Ph.D., Principal Data Scientist at KNIME and author of 50+ technical publications
About KNIME
For more information on KNIME, please visit www.knime.com and the KNIME blog.