A data scientist is evaluating the financial impact of deploying a new fraud detection model for an e-commerce platform. Based on historical data and simulation, they have determined the following potential outcomes for a typical month:
There is a 60% probability that the model will perform optimally, saving the company an estimated $300,000 in fraudulent transaction costs.
There is a 30% probability that the model will perform sub-optimally, saving the company $50,000.
There is a 10% probability that the model will underperform due to unforeseen adversarial attacks, resulting in a net loss of $100,000 from both missed fraud and blocked legitimate transactions.
What is the expected monthly financial value of deploying this new model?
The correct answer is $185,000. The expected value of a discrete random variable is calculated by summing the products of each possible outcome and its corresponding probability. In this scenario, the expected value (EV) is calculated as follows:
EV = (Probability of Outcome A * Value of Outcome A) + (Probability of Outcome B * Value of Outcome B) + (Probability of Outcome C * Value of Outcome C) EV = (0.60 * $300,000) + (0.30 * $50,000) + (0.10 * -$100,000) EV = $180,000 + $15,000 - $10,000 EV = $185,000
The other options represent common calculation errors. Calculating the simple average of the monetary values, misinterpreting the loss as a gain, or incorrectly assigning probabilities would lead to an incorrect result.
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