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Help the bank monitoring their fraud detection model and figuring out why it's not performing as expected.

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burcuyesilyurt/Monitoring_A_Financial_Fraud_Detection_Model

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Monitoring A Financial Fraud Detection Model

A summary and preview are provided below.

reference.csv and analysis.csv

Column Description

  • 'timestamp' Date of the transaction.
  • 'time_since_login_min' Time since the user logged in to the app.
  • 'transaction_amount' The amount of Pounds(£) that users sent to another account.
  • 'transaction_type' Transaction type:
  • CASH-OUT - Withdrawing money from an account.
  • PAYMENT - Transaction where a payment is made to a third party.
  • CASH-IN - This is the opposite of a cash-out. It involves depositing money into an account.
  • TRANSFER - Transaction which involves moving funds from one account to another.
  • 'is_first_transaction' A binary indicator denoting if the transaction is the user's first (1 for the first transaction, 0 otherwise).
  • 'user_tenure_months' The duration in months since the user's account was created or since they became a member.
  • 'is_fraud' A binary label indicating whether the transaction is fraudulent (1 for fraud, 0 otherwise).
  • 'predicted_fraud_proba' The probability assigned by a detection model indicates the likelihood of a fraudulent transaction.
  • 'predicted_fraud' The predicted classification label is calculated based on predicted fraud probability by the detection model (1 for predicted fraud, 0 otherwise).

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Help the bank monitoring their fraud detection model and figuring out why it's not performing as expected.

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