00:00:03 McKenzie O'Hara
Hello, welcome to getting started with where our job is to make your job easier. On this episode, we're getting started with fraud analytics and how internal auditors can use data to spot red flags that may indicate fraud. By the end of this video, you'll understand what fraud analytics is.
00:00:20 McKenzie O'Hara
Why it matters common tests auditors use and some practical tips, so let's get started.
00:00:27 McKenzie O'Hara
Think of fraud analytics as an MRI for your organization. It doesn't just look at what's visible. It's scans beneath the surface to reveal what traditional testing might miss. While manual reviews might only test a sample of transactions, analytics examine the entire data set, spotting patterns and anomalies that point to possible fraud.
00:00:48 McKenzie O'Hara
Just like an MRI helps doctors see what's really happening inside the body, fraud analytics helps internal auditors see what's really happening inside their data.
00:00:56 McKenzie O'Hara
It's all about visibility, uncovering the hidden understanding the signals and strengthening your defenses from the inside out. Fraud analytics is the use of data analysis to identify unusual transactions or patterns that may indicate fraud. It doesn't prove fraud on its own, but it helps you know where to dig deeper to get a clearer understanding. Here's a term to learn.
00:01:17 McKenzie O'Hara
Proactive versus reactive analytics proactive analytics are used continuously scanning data to prevent or detect.
00:01:25 McKenzie O'Hara
Fraud before it.
00:01:26 McKenzie O'Hara
Rows reactive analytics are used after a red flag or allegation to dig deeper into what really happened. Internal auditors may use both approaches depending on the engagement. Why is this important? Fraud can be buried in thousands of transactions, and manual testing may never catch it. Analytics helps you.
00:01:46 McKenzie O'Hara
Behind the needle in the haystack, it also strengthens internal audits role as both a deterrent and a detection partner when it.
00:01:53 McKenzie O'Hara
Comes to fraud.
00:01:55 McKenzie O'Hara
Fraud analytics can help detect risks across three main categories of fraud.
00:02:00 McKenzie O'Hara
First asset misappropriation like duplicate vendor payments or ghost employees. Second corruption like unusual vendor relationships and third financial statement fraud where analytics can spot anomalies in revenue recognition or expense time.
00:02:17 McKenzie O'Hara
Let's pause for another term to learn the fraud triangle. This model explains why people commit fraud and includes 3 elements, pressure, opportunity and rationalization, and employee experiencing financial stress could feel pressure to commit fraud. Rationalization is when someone convinces themselves.
00:02:37 McKenzie O'Hara
It's OK to commit fraud and opportunity is the weak spot in controls that lets fraud happen. Fraud analytics helps auditors detect the opportunity side, like unusual access, duplicate payments, or suspicious timing.
00:02:51 McKenzie O'Hara
Following that definition, here's a pro tip link your analytics to the fraud triangle. Analytics can help you detect opportunity in the data, like unusual access or duplicate payments, which when combined with pressure and rationalization could point to fraud risk. Some of the most common fraud analytics tests include.
00:03:12 McKenzie O'Hara
Duplicate vendor payments Ghost employees, which are payroll entries with no HR record, round dollar payments or weekend transactions transactions just below the approval thresholds. Those tests don't prove fraud, but they highlight areas where you need to investigate further.
00:03:30 McKenzie O'Hara
Next, let's focus on one of those examples. During a payroll audit and internal audit, team uses analytics to compare payroll records with HR data. They discover payments to employees who have already left the company. Ghost employees without analytics. Those fraudulent payments may have gone undetected for months.
00:03:51 McKenzie O'Hara
Here are a couple more examples of practical tests auditors often use.
00:03:55 McKenzie O'Hara
Travel and entertainment expenses repeated small dollar charges just under the approval thresholds. Procurement vendors with the same address or phone number as employees.
00:04:07 McKenzie O'Hara
These tests may look simple, but they can uncover powerful insights. Let's look at another example. An audit team uses analytics to scan vendor payments. They discovered small payments to the same vendor, all just under the managers approval limit on their own. Each payment looks fine, but together the pattern is a red flag.
00:04:27 McKenzie O'Hara
That leads to a fraud finding.
00:04:30 McKenzie O'Hara
Here's a bright idea. Don't do fraud analytics in a vacuum. Partner with your fraud risk, team compliance, or IT colleagues. They can provide context, system knowledge or even additional data sources that make your analysis more effective.
00:04:45 McKenzie O'Hara
Watch out for some common mistakes. One is treating every anomaly as fraud. Remember, unusual data doesn't always mean wrongdoing. Another is ignoring context. You need to understand the process before labeling something suspicious. And finally, don't forget data quality, incomplete or inaccurate data can send you chasing the wrong.
00:05:06 McKenzie O'Hara
Needs. So how do you avoid these mistakes? Use fraud analytics as a starting point, not the final word. Always validate anomalies with additional testing or inquiry. Tie your results back to business knowledge and make sure you've checked data quality before running tests.
00:05:24 McKenzie O'Hara
Remember the global internal audit standards require that evidence be reliable, relevant and sufficient.
00:05:30 McKenzie O'Hara
Fraud analytics supports all three by making sure you're looking at complete, accurate data, and if you want to learn more, check out the IAS Global Practice guide, internal auditing and fraud. It's a great resource with detailed examples and guidance. Thanks for watching getting started with fraud analytics. Next, be sure to check out our episodes.
00:05:52 McKenzie O'Hara
Of fraud fighting and data analytics basics to build your fraud knowledge and analytics skills. You can find these episodes and other helpful resources, including tools, podcasts and training at the links below.
