Accountants often face challenges when it comes to effectively and efficiently reviewing their journal entries.
Manually reviewing journal entries can take hours or even days, depending on the volume and are draining resources.
Reviewing journal entries often involves a large number of attributes that are difficult to assess manually.
Unusual patterns frequently go unnoticed during standard reviews, increasing the risk of errors.
Poor documentation of reviews makes traceability and reproducibility difficult.
Leverage advanced ML-models and automate your journal entry control process.
Isolation Forest algorithms identify unusual journal entries that deviate from typical patterns.
Ensures that the correct value-added tax rates have been applied.
Compares account postings with the previous month and previous year to identify irregularities.
Checks the consistency of booked accounts used for recurring business transactions.
Our ML models and other assistance systems operate completely offline. Our servers are located exclusively in Switzerland. Data protection is our top priority.
As members of ExpertSuisse and certified experts, we offer the required competence and many years of practical experience.
Four simple steps to obtain a comprehensive list of high risk entries.
Securely import up to millions of rows of journal entry data via CSV or Excel.
The Pre-Analysis Dashboard helps you assess industry standard risk criteria as well detected outliers from our ML-model.
From the Pre-Analysis Dashboard, identify your prioritized risk criteria to obtain your list of high risk entries.
Generate and download your tailored documentation and export your selected entries with complete justification for testing, ready for client delivery.
Sign up and we will get in touch with you to unlock the new standard of AI-powered journal entry controls.