Keyword Analytics

AuditNet Live Webinar I Dec 10, 2020 I 10:00 AM PST

Webinar presented in two parts. Duration: 60 minutes

Register here

SPEAKER

MKano PIC

Michael Kano, Data Analytics Consultant, Arbutus

Michael has 25 years of experience in data analytics and internal audit with organizations in the USA, Canada, and the Middle East.

From 2015 to 2019, he was a senior member of the data analytics practice at Focal Point Data Risk, a US-based professional services firm. Prior to Focal Point, Michael led eBay, Inc.’s data analytics program in the Internal Audit department. He was tasked with integrating data analytics into the audit workflow on strategic and tactical levels. This included developing quality and documentation standards, training users, and providing analytics support on numerous audits in the IT, PayPal, and eBay marketplaces business areas. He also provided support to non-IA teams such as the Business Ethics Office and Enterprise Risk Management teams.

During his years at eBay, Michael supported audits throughout the organization in the IT, compliance, operations, vendor management, revenue assurance, T&E, and human resources areas. Michael's software experience includes Arbutus Analyzer, ACL Desktop/Direct Link, Alteryx, Microsoft Access, SQL, and Tableau.

He led ACL Services Ltd.’s global training team for 8 years. He is a graduate of the UCLA Anderson School of Management.

 

AuditNet Webinar Dec 10.20.

Part 1: Adding Keyword Searches to Your Analytical Techniques

Description: Keywords in descriptions, comments, and communications can indicate serious control risks or attempts at fraud. Add keyword searches to your skillset and learn how to automate them.

Learning Objectives: In this session, we’ll learn about the data you should target for searches, the many ways to search for keywords, and which keywords to look for. Demonstrations of the various analytical techniques will help you grasp the best practices for deployment.

 

Part 2: Are Your Audit Exceptions Agnostic?

Description: Once you’ve identified your exceptions, it’s important to know if their frequency distribution matches the original population. For instance, you may find that one vendor represents 35% of your exceptions. Is this out of line with the vendor’s share of the original population? What other characteristics can tell you if your control failures are agnostic or suspiciously concentrated?

Learning Objectives: We’ll take a look at what data characteristics are worth examining for potential concentrations and how to integrate them into your analytics. The processes that will be demonstrated can be readily carried out in almost any analytical application. 

 


 

Join us on December 10, 2020

Time: 10:00 am -11:00 am PST  / Registration Closes at 9:00 am PST

CPE: 1.0 CPE Credit

Program Level / Prerequisites and Advance Preparation: Basic / None