Data-Driven ML Credit Card Fraud Detection We install the Anaconda IDE to perform Python data science and machine learning on a single machine. We use the Kaggle Credit Card Transactions Fraud Detection Dataset https://www.kaggle.com/datasets/kartik2112/fraud-detection?select=fraudTrain.csv This is a simulated credit card transaction dataset containing legitimate and fraud transactions from the duration 1st Jan 2019 - 31st Dec 2020. It covers credit cards of 1000 customers doing transactions with a pool of 800 merchants. This was generated using Sparkov Data Generation | Github tool created by Brandon Harris. The files were combined and converted into a standard csv format. The supervised deep learning binary classification algorithm consists of the following steps: Input Data Management Feature Engineering Model Training, Testing and Validation Model Performance Analysis Cost and Risk Score Estimates Final Risk Threshold Adjustment At the point of the transaction, the ML model gives each customer a score. The higher the score, the higher the probability of fraud.
You can choose what level of risk is right for your business, and set thresholds for what proportion of transactions you want to allow, block and manually review or challenge.
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