Webinar series from FraudResourceNet LLC on Preventing and Detecting Fraud Using Data Analytics. Recordings of these Webinars are available for purchase from our Website fraudresourcenet.com This Webinar focused on fraud detection using data analytic software (Excel, ACL, IDEA) FraudResourceNet (FRN) is the only searchable portal of practical, expert fraud prevention, detection and audit information on the Web. FRN combines the high quality, authoritative anti-fraud and audit content from the leading providers, AuditNet ® LLC and White-Collar Crime 101 LLC/FraudAware. The two entities designed FRN as the “go-to”, easy-to-use source of “how-to” fraud prevention, detection, audit and investigation templates, guidelines, policies, training programs (recorded no CPE and live with CPE) and articles from leading subject matter experts. FRN is a continuously expanding and improving resource, offering auditors, fraud examiners, controllers, investigators and accountants a content-rich source of cutting-edge anti-fraud tools and techniques they will want to refer to again and again.
Welcome message from author
This document is posted to help you gain knowledge. Please leave a comment to let me know what you think about it! Share it to your friends and learn new things together.
This webinar and its material are the property of FraudResourceNet™ LLC. Unauthorized usage or recording of this webinar or any of its material is strictly forbidden. We will be recording the webinar and you will be provided access to that recording within five-seven business days. Downloading or otherwise duplicating the webinar recording is expressly prohibited.
You must answer the polling questions to qualify for CPE per NASBA.
Please complete the evaluation to help us continuously improve our Webinars.
Submit questions via the chat box on your screen and we will answer them either during or at the conclusion.
If GTW stops working you may need to close and restart. You can always dial in and listen and follow along with the handout.
The views expressed by the presenters do not necessarily represent the views, positions, or opinions of FraudResourceNet™ LLC (FRN) or the presenters’ respective organizations. These materials, and the oral presentation accompanying them, are for educational purposes only and do not constitute accounting or legal advice or create an accountant-client relationship.
While FRN makes every effort to ensure information is accurate and complete, FRN makes no representations, guarantees, or warranties as to the accuracy or completeness of the information provided via this presentation. FRN specifically disclaims all liability for any claims or damages that may result from the information contained in this presentation, including any websites maintained by third parties and linked to the FRN website
Any mention of commercial products is for information only; it does not imply recommendation or endorsement by FraudResourceNet LLC
Internal auditors require appropriate skills and should use available technological tools to help them maintain a successful fraud management program that covers prevention, detection, and investigation. As such, all audit professionals — not just IT audit specialists — are expected to be increasingly proficient in areas such as data analysis and the use of technology to help them meet the demands of the job.
Feb. 26, 2013 – The discovery of banks’ efforts to manipulate the London Interbank Offered Rate (LIBOR) owes a lot to statistical techniques that provide first indications of wrongdoing. If regulators (and auditors) want to uncover more misdeeds in the markets, they’ll have to use statistical screening tools more actively than they do today. Extending the analysis over a 30 year period revealed Libor submissions followed Benford’s closely for about 20 years, but began to diverge sharply in the mid‐2000’s.
If you took the annual returns of all possible investments, you would find that the population matches Benford’s Law very closely. However, the returns that Madoffwas reporting don’t. They nearly all have a 1 as a leading digit, as he consistently reported returns between 10%-20%. This would have been a clear indication that his returns were being made up.
Statistics students are asked to perform a simple task. Create a matrix of heads and tails by recording the results of 200 coin flips. The professor reviews the results and easily identifies the students that just made up the results without flipping a coin. How did he know?
Often called the first‐digit law, refers to the frequency distribution of digits in many (not all) real‐life data sources. On the right, you can see the number 1 occurs as the leading digit 30.1% of the time, while larger numbers occur in the first digit less frequently. For example, the number 3879
No. The outcome of the lottery is truly random. This means every lottery number has an equal chance of occurring. The leading‐digit frequencies should, in the long run, be in exact proportion to the number of lottery numbers starting with that digit.
As technology matures, finding fraud will increase. Best use today is to prioritize audit planning. Early warning sign past data patterns have changed. Fraud Deterrence – Potential Fraudsters may not
understand the theory of Benford’s but know audit is regularly running data analysis.
Identify Duplicates, Whole Numbers, Recurring Expenses, other data pattern Anomalies
Coupled with high dollar and stratified random sample techniques (use with other analytical tools)
The designated first or second digits in a number series will be analyzed. The expected output serves as a rough check of the actual numerical distribution in the population and is used to determine level of compliance with the Benford’s Law.
This test examines the frequency of the numerical combinations 10 through 99 on the first two digits of a series of numbers.
In particular the output serves for the analysis of avoided threshold values. Thus, these tests help to clearly visualize when order or permission limits have been systematically avoided.
The Last Two Digits test analyzes the frequency of the last two digits and is useful in auditing election results, inventory counts—any situation in which padding or number invention is suspected.
This test is used to analyze the relative increasing frequency of rounded numbers.
The determination comprises the frequencies of the numbers that are divisible by 10, 25, 100 and 1,000 (and any user‐defined values of whole numbers) without remainders.
The analysis of multiple duplicates includes all number values in the entire database that occur more than once. This test helps the user to recognize all existing duplicates in the data supply whereas the result table presents the duplicates sorted according to the descending frequency. The aim of the test is to identify certain numbers that occur more than once (for example, possible duplicate payments). Difference from the other tests: Does not analyze any numerical combinations, but the pure value of a number.
The Summation test is similar to the traditional Benford’s Law test, but instead of calculating the number of occurrences for each first two digits, it sums each amount.
Advantage: Allows you to identify clearly significant amounts that do not follow the expected results of Benford’s Law.
The Second Order test is based on the digits of the differences between amounts that have been sorted from smallest to largest (ordered). The first two digits of the differences should follow the digit frequencies of Benford’s Law. This test is particularly useful in indicating data integrity issues.
With most Benford’s Law tests in IDEA Version Nine, you have the option of extracting “suspicious” data whose digit frequencies do not follow the digit frequencies of Benford’s Law. With Advanced Settings, you can also refine this output to limit the size of the output database.
Area(s) where Benford’s Law is not a good tool (choose all that apply):A. All the numbers in a series are at or below $9.99 or frauds involving situations where nothing is recorded.B. All of the numbers are positive.C. All of the numbers are negative.D. Very large data sets over 1 billion records.
Organize Population into groups by the number of leading digits.
Analyze Groups Using Benford
Store Benford Analysis into a Table and then extract high frequency digit combinations (use the z statistic and the variance between actual and expected occurrence).
Mark Nigrini:A. Invented Benford’s LawB. Is a close relative of BenfordC. Is the only one to find fraud using Benford’s LawD. Believes auditors should use it to detect fraud
Digital analysis tools like Benford’s Law enable auditors and other data analysts to focus on possible anomalies in large data sets. They do not prove that error or fraud exist, but identify items that deserve further study on statistical grounds. Digital analysis complements existing analytical tools and techniques, and should not be used in isolation from them.
Not necessarily fraud – many False positives
Certain types of fraud will not be detectedUseful tool, setting future auditing plansLow Cost Entry into Digital continuous analysis