Summary of Credit Card Fraud Detection in the Nigerian Financial Sector: a Comparison Of Unsupervised Tensorflow-based Anomaly Detection Techniques, Autoencoders and Pca Algorithm, by Jennifer Onyeama
Credit Card Fraud Detection in the Nigerian Financial Sector: A Comparison of Unsupervised TensorFlow-Based Anomaly Detection Techniques, Autoencoders and PCA Algorithm
by Jennifer Onyeama
First submitted to arxiv on: 8 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper investigates the effectiveness of two automated fraud detection technologies in predicting and detecting fraudulent credit card transactions. The proposed approach uses unsupervised learning-based techniques, specifically autoencoders and Principal Component Analysis (PCA), to analyze Nigerian credit card transaction data. The goal is to compare the performance of these methods in detecting anomalies and reducing dimensionality. The results show that autoencoders outperform PCA in analyzing complex datasets with minimal mislabeling. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Credit card fraud is a big problem in Nigeria, affecting many transactions and online shopping. To stop this, some financial institutions use special tools to detect fraudulent transactions. This paper compares two of these tools: autoencoders and Principal Component Analysis (PCA). Both try to find unusual patterns in data to catch scammers. The study uses real Nigerian credit card transaction data to test which method works best. The results show that autoencoders are better at analyzing complex data and making accurate predictions. |
Keywords
» Artificial intelligence » Pca » Principal component analysis » Unsupervised
