DA-101 USING DATA ANALYTICS TO IMPROVE INTERNAL AUDIT

DA-101 USING DATA ANALYTICS TO IMPROVE INTERNAL AUDIT

Description

Data analytics programs are an excellent opportunity for audit functions to truly add value to their organizations by enhancing assurance for audits, expanding coverage, enabling fraud detection and reducing efforts for Sarbanes-Oxley. Good programs help audit functions to be efficient, scalable, reduce errors and provide greater audit and fraud coverage. However, implementing and expanding a program can be challenging. A successful program requires many different skills sets and rarely does a single individual possess all the necessary skills. Proficiency with a data analytics tool is only one piece of the puzzle. Leveraging the knowledge of the entire audit team is what makes data analytics programs successful. That requires educating the entire team on data analytics – what a program can provide, and what will make it valuable for audit and the organization.

 

This course will help auditors understand what data analytics can do, what is required for a successful program, what areas to targeted, and most importantly, why everyone’s skills and knowledge are needed. Participants will learn interactively through a combination of lecture, case studies, demonstrations, and exercises.

Learning Objectives

  • Understanding data analytics concepts
  • Understanding continuous auditing and continuous monitoring
  • Identifying how data analytics adds value and efficiency to your audits
  • Learning how to implement data analytics programs and overcoming the common challenges
  • Understanding databases, schemas, entities, attributes, tables, fields, etc.
  • Learning data analytics fundamental approaches and some advanced techniques
  • Learning how to present your findings in a compelling manner

 

Course Outline

Overview of Data Analytics

  • Defining data analytics | Big data
  • Continuous auditing vs. monitoring

Data Analytics for Internal Audit

  • The need for analytics in internal audit
  • Value proposition of data analytics
  • Top 10 areas of value in an audit | Getting creative
  • Data analytics as a fraud discovery tool
  • Implementation life cycle

Understanding Databases and Schemas

  • Gaining access | Data dictionaries (or not)
  • Entity relationship diagrams | Entities and attributes
  • Schemas, tables, and fields | Sensitive data

Working with Data

  • Obtaining data | Reliability of data
  • Data integrity | Vetting and validating data
  • Data source risks | Importing, copying, and working with data
  • Corrupting your own data (you will) | Reasonableness tests

Resources and Data Analytics Team

  • The many skills required | Dedicated resources | The rest of the team
  • Choosing tools (GTAG 16) | Hosting or not | Working with IT

Fundamental Techniques

  • Duplicates | Matching | Fuzzy logic | Gaps
  • Red flag attributes

More Advanced Techniques

  • Statistics | Qualitative relationships
  • Benford’s law | Regression analysis

Presenting Findings

  • Knowing your audience | Avoiding too much detail
  • Visualization | Using “eye candy” to report your results

Best Practices

  • Compliance monitoring and auditing examples
  • Entity-wide auditing for specific processes
  • Fraud detection through continuous auditing and monitoring
  • Fraud investigations | Sarbanes-Oxley | Business improvement
Duration

CPE

Delivery

Field

Level

Who Should Attend

Prerequisites

Advanced Preparation

2 Days

16

Group-Live

Auditing

Intermediate

Internal auditor staff and management of all levels

Auditors with at least 2 years of experience

None