Defining the Problem: Build a model to flag transactions with an unusually high probability of being fraudulent before the transaction is completed, Preparing Data: Transaction history that includes amount, location, time, customer profile and device information, Exploring Data: Plot transaction amounts and frequencies. Discover that fraudulent transactions often cluster around specific times, Building Models: Use classification models especially deep learning for sequential transaction data. Feed the model historical data, Exploring and Validating Models: Run the model on a reserved set of transactions to measure its performance., Deploying and Updating Models: Integrate the model into the bank's transaction processing system. It provides a real-time risk score for every transaction,

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