Explanation
Dates with absolute time/date values, such as birthdays and Numeric data such as customer age and income Reference:
Dates with absolute time/date values, such as birthdays and Numeric data such as customer age and income require no preprocessing before being used as potential predictors in adaptive analytics.

Explanation
Pega Process AI lets you bring your own predictive models to Pega and use predictions in case types to optimize the way your application processes work and meet your business goals.
To use the outcome of a predictive fraud model in the case type that processes the incoming claim, you need to use the model outcome in the condition of a decision step2. This way, you can route suspicious claims to a fraud expert for closer inspection based on the model's prediction.

Explanation
The Monte Carlo method is a way to create a data set that includes mock-up customer data. You can use this method to generate random values for customer properties, such as age, gender, income, etc., based on predefined probabilities and distributions. References:
https://academy.pega.com/module/demonstrating-adaptive-learning-archived/topic/creating-monte-carlo-data-set

Explanation
Pega Customer Decision Hub uses the PCV*L arbitration formula to select the next best action for each customer. The purpose of the formula is to balance customer needs with business objectives.

Explanation
Adaptive models can start to learn without historical evidence. The starting propensity of every action is 0.5.