Draft and review journal entries
An AI agent drafts recurring and adjusting journal entries from your source data and prior periods, attaches the support, and flags anomalies for a human to approve, cutting the manual grind out of month-end close.
Likely tools or environments
Scores run from 1 to 5. Ease and impact: higher is better. Risk: higher means more care and human review. Scored by our team as a general guide. Your tools, information and policies can change the picture. How we score
What this could change
What changes when recurring journal entries arrive drafted, supported and checked
- Before: Routine entries are prepared by hand every close while reviewers race through the stack; half of finance teams still take six or more business days to close. After: Draft entries arrive with their support attached, tied back to source and checked against prior periods, so reviewers spend their time on the flagged exceptions.
- What it needs: Written rules for each recurring entry (source, calculation, accounts, support and review threshold), read-only access to source files, and a sandbox to build in.
- What stays human: Nothing posts on its own: a qualified reviewer, separate from whoever runs the routine, approves each draft and posts it through your normal controls.
The exact design depends on your tools, information, policies, and tolerance for risk. Aravise explains the outcome and requirements publicly; the implementation happens inside an engagement — coached so you can run it yourself, or built with your team through Embedded AI Delivery.
Sources
- 50% of finance teams still take six or more business days to close the books, and 27% regularly take more than seven (2025). — CFO.com (citing Ledge month-end close benchmarks)
- 47% of more than 1,300 C-level executives and finance professionals surveyed were concerned about making decisions based on outdated or inaccurate information. — BlackLine
Related lessons
A short video lesson on a closely related task, with the steps shown on screen.
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