sharmaroshan/Fraud-Detection-in-Online-Transactions
Detecting Frauds in Online Transactions using Anamoly Detection Techniques Such as Over Sampling and Under-Sampling as the ratio of Frauds is less than 0.00005 thus, simply applying Classification Algorithm may result in Overfitting
This project helps financial institutions and mobile money service providers identify fraudulent transactions within their systems. By analyzing detailed transaction data, it flags suspicious activities such as large transfers or unusual account behaviors. The output is a clear indication of which transactions are likely fraudulent, helping financial crime analysts and risk managers protect customers and assets.
No commits in the last 6 months.
Use this if you need to detect fraud in high-volume mobile money transactions and struggle with imbalanced datasets where fraud is extremely rare.
Not ideal if you are looking for a fraud detection system for credit card transactions or require real-time flagging with extremely low latency.
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91
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Language
Jupyter Notebook
License
GPL-3.0
Category
Last pushed
May 23, 2019
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