How to approach model reporting task
Remove input data to test for leakage

This is very generalized question, so without knowing the types and provenance of the input data, this can be hard to answer.

But, in general, to check for leakage, you can use the model on some subsets of the input variables while removing other input variables. If you get data from multiple sources, then try removing all input variables from a single source, then re-run your models. You may be able to identify the source of the data leakage. Alternately, if computational power allows, you can brute force it by running the model with each of the 150 input variables removed, or all sets of two variables, etc.

Use customer-centered time data

Regarding model meta-data, again I would investigate data provenance. Are you predicting churn using the complete patterns of customers who stopped using the service? What I mean to say is, instead of looking backwards from a fixed real-time period, like today, to all customers who did or did not stop using the service, try looking from a fixed customer-time. Use only data from the first year that each customer used the service, and attempt to predict whether each customer will remain with the service for another year.

The warning signs of a customer dropping the service may be obvious in the months leading up to that customer dropping the service, but by then, the predictive power of your model may be too late to stop that customer from leaving. Instead, index the time component of each customer's history to zero when the first start using the service, and run your model on this data.