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Case Studies: Using Data Analytics to Investigate Fraud

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Date(s): Aug 23, 2022
Time: 8:00AM - 4:30PM
Registration Fee: $139.00
Cancellation Date: Aug 16, 2022
City: Austin
Local Hotels:
Parking Info:

Parking for SAO, Professional Development courses is in Garage B (1511 San Jacinto Blvd.). The Garage signage may read 1511 San Jacinto or Garage B. The elevator in Garage B is not reliable. If you are unable to walk the stairs, please contact the for alternate parking arrangements. Handicapped parking is free at the meters around the downtown area.

A course coordinator will Email you a parking permit prior to the course start date. A permit must be displayed or you will be ticketed.

Course 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 session will introduce the participants to the techniques currently being used by leading data analytics programs to detect and investigate fraud.

Potential CPE Credits: 8.0
Technical Hours: This class meets 8.0 CPE credits of technical training in compliance with Texas Admin. Code Rule 523.102.

Instruction Type: Live
Experience Level:
Category: Auditing

Course Objectives

Upon completion of this course, participants will be able to:

• Learn specific fraud detection methodologies using data analytics

• Understand trends and patterns in fighting fraud

• Using analytics in detection of fraud

• Fraud-focused continuous auditing and monitoring

• Why Analytics is excellent for prevention, detection and most importantly, deterrence

• Examine data analytics used to identify, quantify and prove fraud in three real world cases

Detailed Course Outline

Considerations, Methods and Techniques

  • Transactional Volume

  • Common areas for fraud analytics

  • Duplicates

  • Damerau-Levenshtein (Fuzzy Logic)

  • Matching

  • Gaps

  • Fraud red flags

Trends and Patterns

  • Importance of visualization

  • Risk Scorecards

  • Fraudulent loans (disbursements)

  • Expense reimbursements

  • Capitalization as concealment

Advanced Techniques

  • Benford’s Law

  • Multiple Systems and sophisticated scripts

  • Case study example – Unusual hour transactions

  • Multiple systems and sophisticated scripts

  • Case study example - Benford's Law

  • Artificial Intelligence

Case Study #1: Financial Statement Manipulation & Database Fraud

A $10 million Financial Statement fraud committed and concealed through a database breach and manipulation of thousands of records.

  • Understanding the fraud

  • Mapping the fraud

  • Mapping the data flow

  • Validating the data flow

  • Testing the the theory

  • Creating the scripts

  • Validating results

  • Quantifying losses

  • Presenting results

Case Study #2: Inventory Theft

An $8 million inventory theft fraud committed through collusion and concealed through capitalization of false asset improvements.

  • Understanding the fraud

  • Mapping the fraud

  • Mapping the data flow

  • Validating the data flow

  • Testing the the theory

  • Creating the scripts

  • Validating results

  • Quantifying losses

  • Presenting results

Case Study #3: Restaurant Skimming

$1.3 million cash skimming scheme committed in a bar restaurant by a large group of bartending staff where the transactional system provided both the opportunity and a built in concealment.

  • Understanding the fraud

  • Mapping the fraud

  • Mapping the data flow

  • Validating the data flow

  • Testing the the theory

  • Creating the scripts

  • Decrypting the data – bots

  • Chain of custody using AI

  • Validating results

  • Quantifying losses

  • Presenting results

2020 – The Future is Now. Taking Data Analytics to the Next Level with Artificial Intelligence (AI)

The natural evolution of data analytics is AI. This session will walk participants through a real-world case study where AI solved significant challenges with labor, chain of custody, quality of evidence and more. The case will be covered from Whistleblower to conclusion and examine the analytics and AI utilized.


Mary Breslin

Mary Breslin is the Founder of Verracy Training & Consulting and specializes in Internal Audit transformations, Operational and Financial Auditing, Fraud Auditing & Investigations, and Corporate Accounting.


Ms. Breslin’s career spans over 20 years in Internal Auditing, Management and Accounting for companies such as ConocoPhillips, Barclays Capital, Costco Wholesale, Jefferson Wells and Boart Longyear. With significant International experience, she has managed audit programs in more than 50 countries. Most recently, Ms. Breslin held the title of Vice President and Chief Audit Executive where she transformed a checklist audit function into a value-add audit department which regularly delivered measurable business results through the use of risk-based auditing, data analytics, continuous education and skill development for her leadership team and staff.


Mary was an early adopter of analytics for audit and has been utilizing data analytics in her career in both audit and fraud work for over 15 years. She has transformed the capabilities of audit functions using data analytics programs using ACL, IDEA and Arbutus Analyzer. Through her expertise and guidance, she has helped large global organizations leverage analytics to significantly increase coverage, automate continuous auditing and monitoring, and actively fight fraud.


Additionally, Ms. Breslin has extensive Fraud Audit and Investigation experience and has conducted major fraud investigations on multiple continents including large scale federal cases domestically. She has developed and implemented fraud auditing programs in various industries focused on both accounting and operations.


Ms. Breslin attended Rutgers University and received her BS in accounting, and an MBA from the University of Phoenix while living and working overseas. She is a Certified Internal Auditor (CIA), and Certified Fraud Examiner (CFE). She maintains memberships in the Institute of Internal Auditors (IIA), American Institute of Certified Public Accountants (AICPA), ISACA, the Society of Corporate Compliance (SCCE), and the Association of Certified Fraud Examiners (ACFE) and is currently an instructor and conference speaker for the IIA, The ACFE and ISACA.

Additional Information

TAC Rule 523.142(g) requires the CPE Sponsor to monitor individual attendance and assign the correct number of CPE credits. Participants will be asked to document their time of arrival and departure in compliance with this Rule. Additionally, attendance will be monitored throughout the day and CPE certificates will reflect actual attendance of each participant.

If you are making travel plans to come to Austin, we recommend making "refundable" air and hotel reservations or waiting until 14 days before the class to actually book your reservations. Courses are occasionally canceled or rescheduled due to low enrollment. We determine whether a course has enough participants 16 days prior to the course date. If we cancel or reschedule, we will email the participant and his or her billing contact no later than 14 days before the original class date.

The course coordinator will contact you with parking information. Handicapped parking is free at the meters around the downtown area.

Vending machines with Coca-Cola products and various snack items are available. There is also a refrigerator and microwave in our coffee bar area. Feel free to bring in your own drinks and food if you prefer.

You might want to bring a light sweater or jacket, as room temperatures vary.

To see answers to our Frequently Asked Questions, visit

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