Inteligencia artificial, machine learning, deep learning y resumen de la misma IA

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When I think about financial fraud, it always strikes me how much damage it causes—not just to individuals but to the entire economy. Credit card transactions have become so common, yet they are increasingly targeted by fraudsters. I know that developing effective fraud detection methods is critical, but the skewed nature of the datasets always complicates the process. Fraudulent transactions form such a tiny percentage of the total data that it feels like finding a needle in a haystack. To me, this imbalance is the biggest challenge when it comes to machine learning models for fraud detection. When I look at the data, it’s clear that standard machine learning algorithms tend to focus on the majority class (non-fraud cases) while misclassifying the minority class (fraud cases) as noise. That’s why I think techniques like resampling are so useful—they help ensure the model doesn’t ignore the smaller, more critical fraud category. I’ve decided to use Random Under-Sampling (RUS) in this study since it simplifies the dataset and creates balance by reducing the majority class.

All six deliverables for SSNA