Detecting Fraud Using Data Analysis

Massive data sets within the organization’s systems can hide symptoms of fraud and waste from auditors and control personnel.  This tactical session delves into specific methods for auditors, investigators, and finance professionals to highlight symptoms of problems across all company processes.  Learn how to apply data analysis skills to effectively test one hundred percent of a transaction population and achieve a positive return-on-investment for your organization.

Who should attend:  Auditors, Investigators, Accounting Professionals.  This workshop is not about what buttons to click; it is about strategies employed through the use of data analysis.  Also, the workshop is software-neutral, meaning participants will benefit whether they use IDEA, ACL, SQL queries, or simply Excel and Access.  The instructor will demonstrate techniques that can be handled by all of the programs.

Please scroll down to view the course outline, or click here to download the pdf outline.

Course Objectives

Upon completion of this workshop, you will understand how to:

Overcome mindsets that prevent us from properly addressing fraud;

Apply a consistent methodology for fraud detection;

Employ data analysis techniques used to successfully detect fraud;

Blend traditional methods of auditing with data analysis techniques;

Incorporate data analysis techniques into routine daily activities to improve detective controls;

Avoid common pitfalls related to data analysis;

Apply data analysis to audits of process areas common to all organizations;

Use data analysis to test 100% of a population instead of a sample;

Apply lessons from case studies to your own unique environment.

8 CPE

Field of Study: Accounting, Auditing

Course Level: Intermediate

Group-live

No prerequisites or advanced preparation required

Course Outline

Preparing to Detect Fraud

Overcoming beliefs that hinder our ability to achieve results;

How much fraud is out there;

Our role in fraud detection;

Evaluating your environment;

Assessing Fraud Policy;

Importance of early detection;

A method for fraud detection;

Data analysis in perspective.

Fundamental Data Analysis Techniques

Importing preferences;

Using control totals to detect manipulation of reconciliations and spreadsheets;

Sorting data to highlight key missing fields, stale transactions, odd dates, and unusually large/small items;

Detecting anomalies through statistical analysis;

Effective use of extractions, using logic operators to highlight odd transactions;

How to spot patterns with summarizations and pivot tables;

Application of fundamental techniques to case studies.

Beyond Fundamentals

Traps to avoid;

Searching for descriptors symptomatic of earnings management, fictitious payments, and corruption;

Effective uses of field manipulation;

Duplicate key detection & exclusion;

Date stratification to detect spikes in activity around a period end, symptomatic of earnings management;

Numeric stratification and the circumvention of approval authority;

Joining databases to detect false vendors, ghosts on the payroll, and revenue loss;

Time block comparisons to detect escalating activity symptomatic of false vendors, cash misappropriation, and ‘black hole’ accounts;

Benford's Law and its application;

Where to look for problems in your environment;

What can go wrong & common symptoms of fraud;

Putting it all together into a plan.