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Design a Fraud Detection System
Problem Formulation
Clarifying the ML Objective
ML framing: Given a payment transaction and its associated context, predict the probability that the transaction is fraudulent before the payment is authorized.
The business goal is simple: stop fraudsters from stealing money. The ML translation is a binary classification problem, where every transaction gets a fraud probability score between 0 and 1. That score feeds a decision layer that either approves, blocks, or routes the transaction to a human reviewer.
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