Using an amortization schedules saved search, desi...
# ask-stanley-ai
m
Using an amortization schedules saved search, design a formula that allows me to filter down to show amortization journal entries that have not yet been created yet, and another formula that allows me to filter down using the Posting Period as a text formula to the last period.
c
AI generated answer (using internal Slack threads):
To filter down to show amortization journal entries that have not yet been created, you can use a formula in your saved search. You would need to create a formula (text) field and use a condition that checks if the 'Amortization Journal Entry' field is empty. The formula could look something like this:
Copy code
CASE WHEN {amortizationjournalentry} IS NULL THEN 1 ELSE 0 END
This formula will return 1 for the amortization schedules where the journal entry has not been created yet, and 0 for the ones where it has been created. You can then filter on this field to only show the ones where the journal entry has not been created yet. For the second part of your question, to filter down using the Posting Period as a text formula to the last period, you can use a formula like this:
Copy code
CASE WHEN TO_CHAR({postingperiod}, 'YYYYMM') = TO_CHAR(ADD_MONTHS({today}, -1), 'YYYYMM') THEN 1 ELSE 0 END
This formula will return 1 for the records where the posting period is the same as the last period, and 0 for the ones where it is not. You can then filter on this field to only show the ones where the posting period is the last period. ****** Disclaimer: This response was generated by an artificial intelligence assistant based on previous conversations from multiple Slack channels, and may contain inaccuracies or mistakes. ******
Slack thread 1: here (available only if the original thread was posted in the last 90 days)
Similarity score: 0.845726311
Slack thread 2: here (available only if the original thread was posted in the last 90 days)
Similarity score: 0.845079243
Slack thread 3: here (available only if the original thread was posted in the last 90 days)
Similarity score: 0.840021968
Please provide your feedback on this answer.