Detecting Fraud wiTH Data Analytics Workshop 
 
DAY ONE  DAY TWO  DAY THREE 
7:30 a.m. -
8:00 a.m.
 
Registration     

8:00 a.m. -
9:20 a.m.
 

Introduction to Data Analysis and the Data Analysis Process 

This session will introduce you to the uses, benefits and challenges of data analytics techniques, as well as the types of data that can be analyzed and some software options for performing data analysis tests. You will also learn how to formulate and apply an overarching methodology for building a data analytics program and how to tie the organization's fraud risk assessment to most effectively detect fraud.

Data Analysis Tests for Detecting Theft of Inventory and Cash Receipts

This session explores specific data tests that you can use to spot red flags of fraud in the customer sales cycle in your organization. You will learn to identify red flags of theft of inventory and incoming cash receipts fraud.


 

Data Analysis Tests for Detecting Payroll Schemes 

This session highlights data analysis techniques that you can use to uncover particular fraud schemes within the payroll function of your organization. Using discussion scenarios to walk through data analytics techniques, participants will learn to identify red flags of these types of fraud that appear in the data.

9:20 a.m. -
9:35 a.m.
 

Break  

Break  

Break  

9:35 a.m. -
10:55 a.m.
 

Prepping, Normalizing and Harmonizing Data

The results of any data analysis technique are only as good as the underlying data that is examined. One of the most important steps — and often one of the biggest challenges — in data analytics initiatives involves preparing and normalizing the data for analysis. In this session, participants will explore common issues and effective solutions for ensuring the data they have obtained is properly prepared, normalized and harmonized before they begin their testing.  

Case Study — Cash Receipts Fraud 

You will apply the data analytics techniques discussed  in the previous section by working on a hands-on case study involving cash receipts fraud.

Case Study — Payroll Fraud 

You will apply the data analytics techniques discussed in the previous section by working on a hands-on case study involving payroll fraud.

 

10:55 a.m. -
11:10 a.m.
 

Break  

Break  

Break  

11:10 a.m. -
12:30 p.m.
 

Fundamental Data Analysis Techniques 

In this session, participants will discuss many of the most common data analysis techniques — such as duplicate testing, matching, gap testing and compliance verification — that can be used to comb through the data and identify anomalies and red flags of fraud.

Data Analysis Test for Detecting Financial Statement Fraud

This session focuses on targeted data analysis tests to identify various financial statement fraud schemes. By using these techniques to analyze the financial statements, the related disclosures and the underlying data, fraud examiners can identify anomalies and uncover financial statement manipulation. You will then apply these techniques to data assess whether and how an organization's reported financial results have been manipulated.                     

 

Data Analysis Tests for Detecting Corruption

Corruption schemes can be particularly difficult to detect, as so many clues for these schemes fall outside the company's financial records — and thus outside the traditional realm of data analytics. This session provides you with numerous techniques that can be used to analyze the structured and unstructured data in which warning signs of corruption schemes are often found.

12:30 p.m. -
1:30 p.m.
 

Group Lunch  

Group Lunch

Group Lunch 

1:30 p.m. -
2:50 p.m.
 

Other Data Analysis Techniques 

There are numerous sophisticated analysis techniques that can be particularly useful in analyzing datasets for symptoms of fraud. This session includes discussions of Benford's Law analysis, regression analysis, reasonableness testing and several other tools that you can use to take your fraud detection efforts to the next level. You will also learn about non-traditional data analysis tests that identify the red flags of fraud, such as textual, visual, and timeline analytics, as well as transactional risk-ranking techniques.

Data Analysis Tests for Detecting Billing and Check Tampering Schemes  

This session explores specific tests that you can use to uncover fraud schemes within the accounts payable and cash disbursements functions of your organization. Using discussion scenarios to walk through data analytics techniques, you will learn to identify red flags of these types of fraud that appear in the data.


Data Analysis Tests for Detecting Expense Reimbursement and P-Card Schemes


This session focuses on data analysis techniques that you can use to uncover particular fraud schemes within the expense reimbursement and purchasing card (or p-card) functions of your organization. Using discussion scenarios to walk through data analytics techniques, you will learn to identify red flags of these types of fraud that appear in the data.

2:50 p.m. -
3:05 p.m.
 

Break  

Break  

Break  

3:05 p.m. -
4:25 p.m.
 

Effectively Communicating the Results of the Data Analytics  

Once the data has been analyzed and conclusions drawn, you often must convey what has been learned. However, effectively communicating those results can be a challenge for many examiners, both experienced and novice. This session will discuss how to effectively communicate the results of data analytics to both internal and external audiences. 

Case Study — Billing Fraud

Attendees will apply the data analytics techniques discussed in the previous section by working on a hands-on case study involving billing fraud.

Case Study — Expense Reimbursement and P-Card Fraud 

Attendees will apply the data analytics techniques discussed in the previous section by working on a hands-on case study involving expense reimbursement and p-card fraud.

 

Event Details 

 

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