Quickly discover outliers in close data
Leveraging the power of Machine Learning (ML), anomaly detection performs a complete analysis of your transactional data (GL, AP, and AR) and quickly presents you with a snapshot of the anomalies lurking within.
By providing easy-to-read dashboards and analysis organized by risk rating, anomaly detection intuitively exposes all of the questionable transactions that would have gone unnoticed.
It does this with the speed and accuracy no scale of humans could economically perform.
Reduce financial risk and fraud
One of the more complex aspects of closing the books is risk assurance.
By leveraging the intelligence of machine learning, years of industry best practices, and the power of cloud computing to accurately pre-audit any volume of transactions, anomaly detection quickly discovers all of the questionable transactions that might have gone unnoticed. Build the assurance you need that your financial figures are correct and free from errors and any suspect activities.
Greater data accuracy and confidence
As your business grows, so does the number of transactions you book, and with it, the increased chance of human error. Scale your business with greater assurance by quickly identifying risk and eliminating human errors, erroneous bookings, and unusual transactions. Knowing anomaly detection provides a complete and accurate analysis for greater confidence in your data.
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Learn moreFrequently asked questions
No coding is required to configure anomaly detection. Simply load your company‘s data set into anomaly detection to start the analysis.
Risk analysis is performed by employing a combination of methods. Anomaly detection produces a holistic, detailed risk assessment by utilizing machine learning, statistical methods, domain experience, and business rules.
Anomaly detection analyzes general ledger and sub-ledger transactions (accounts receivable, and accounts payable subsidiary ledgers) to provide you with deep analysis of your data to uncover outliers, errors, and possible fraud.
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