In this session we raised awareness about the various types of frauds and how they can be detected using automated data analysis techniques.
Also, take a look at the *ACFE Report to the Nations - 2020 Global Study on Occupational Fraud and Abuse
This webinar was presented in two parts during the same session.
Part 1: Learn about the data you should target for searches, the many ways to search for keywords, and which keywords to look for.
Part 2: Take a look at what data characteristics are worth examining for potential concentrations and how to integrate them into your analytics.
[THE BALANCE SHEET]
Explained in three episodes by Scillani Information AB, Arbutus Partner, Sweden and Arbutus.
- Accounts Receivable
- Accounts Payable
- Cash Accounts
Establish a more impactful & sustainable framework for Analytics
This webinar showcase how data analytics can help drive organizational learning (human and machine) from the various remediation processes.
[Simplify Your Fuzzy Duplicates Testing]
With live demonstration of Arbutus Analyzer’s versatile functionality, learn how users are able to detect possible duplicate payments and identify possible risks.
An ATCO presentation
ATCO explains how they have evolved its Internal Audit practice through embedding Data Analytics within its Audit Shop.
Understand the four different models for the management of data analytics in Internal Audit departments.
Learn the key characteristics of each model, as well as strengths and weaknesses.
Improve the Performance of your Audit Analytics Program.
Distinguish between exploratory and confirmatory queries and how Arbutus & visual reporting tools like Tableau can work together to streamline internal audit data prep & analysis.
Organizations can take a proactive approach to fraud detection and prevention using forensic data analytics. Learn to apply the latest analytic techniques to detect fraud, design agile exception reports and find simple ways to delegate exception analytics.
This webinar explains the significance of outliers when testing transactions and demonstrates steps to identify outliers using statistical methods.
