DA-103 LEVERAGING DATA ANALYTICS TO FIGHT FRAUD

DA-103 LEVERAGING DATA ANALYTICS TO FIGHT FRAUD

DESCRIPTION

If robust data analytics programs existed 20 years ago, would Enron, WorldCom, or the countless other major corporate fraud scandals ever have reached the magnitude they did? Would they have occurred at all? All organizations are subject to fraud risk. Association of Certified Fraud Examiners (ACFE) studies show that an average of 5% of revenue is lost to internal fraud schemes. Utilizing effective data analytics as part of an organization’s fraud management program can reduce the fraud scheme’s average duration from 18 months to mere weeks, or in some cases, eliminate it. More importantly, a robust fraud data analytics program can be the strongest deterrent in an organization.

 

This course will introduce the participants to the techniques currently being used by leading data analytics programs through the use of case studies, best practices, and interactive exercises. Participants will gain a basic understand of fraud, how it occurs in organizations, and the specific techniques used to identify and quantify fraud.

Learning Objectives

  • Understanding the Fraud Triangle
  • Learning fraud fundamentals
  • Understanding the characteristics of fraudsters
  • Learning how to identify the red flags of fraud
  • Learn specific fraud detection methodologies using data analytics
  • Understand trends and patterns in fighting fraud

 

Course Outline

Understanding Fraud

  • The Fraud Triangle
  • ACFE 2018 Report to the Nations
  • Fraudsters—characteristics and behaviors
  • Fraud schemes and scenarios—management override | corruption | financial statement | asset misappropriation

Principles for Fighting Fraud in an Organization

  • Governance | Fraud Risk Assessments
  • Prevention | Detection | Reporting

Using Data Analytics for Fraud Management

  • Using analytics in detection of fraud
  • Fraud-focused continuous auditing and monitoring
  • Fraud investigations—gathering evidence | data integrity | legal requirements
  • Prevention | Deterrence | Detection

New IIA and ISACA Fraud-Detection Standards

  • IIA GTAG #13 and GTAG #16
  • ISACA Data Analytics: A Practical Approach
  • Survey results on data analytics for fraud among auditors

Getting Started with Data Analytics/CAATs

  • Data analytics terminology | CAATs | Big Data
  • Continuous auditing vs. continuous monitoring

Fundamental Data Analysis Techniques

  • Duplicates | Matching | “Like” attributes and transitions
  • Gap testing | Compliance testing and verification
  • Red flag attributes

Understanding Trends and Patterns in Data

  • How to spot them
  • Understanding what the data is telling you
  • Red flags vs. changes in the business

Specific Data Analytics Techniques for:

  • Fraudulent disbursements
  • Payroll
  • Inventory and fixed assets asset theft and misuse
  • Corruption
  • Financial statements asset and revenue over and understatements

The Next Level—Advanced Techniques

  • Regression analysis
  • Benford’s law
  • Reasonableness testing
  • Fuzzy logic
Duration

CPE

Delivery

Field

Level

Who Should Attend

 

Prerequisites

 

Advanced Preparation

2 Days

16

Group-Live

Auditing

Intermediate

Internal auditor staff and management; financial and operational management and staff

General understanding of fraud schemes, fraud auditing techniques, and computer-based auditing

None