Six Sigma’s usefulness goes well beyond process improvement. It’s also a powerful tool for detecting fraud in accounting data. You should always remain vigilant, as it is often unexpected and difficult to identify. Are you worried about your organization being compromised by fraud? Six Sigma can help. Once you know what to look for, you can easily recognize the signs and actively seek out fraud should you need to.
Using Six Sigma techniques and ideas, you can also learn to prevent fraud before it occurs. Detection (and prevention) is a powerful business analytic that can help you out of a tough spot. It’s essential you’re up to speed on how to use it. Read on to learn how you can prepare for and deter it with this highly effective tool.
What is Fraud Detection?
You must first understand what fraud detection means before you can learn to use it. This detection is a business analysis tool focused on identifying certain cases. It does this by analyzing logged data from computer systems and user behavior. There are many types of fraud to look out for, including:
- Credit card
- Internet transaction and E-cash
- Healthcare and insurance
- Money laundering
- Identity theft
- Illegally accessing computers or networks
The fraud detection process is similar to each type, all of which involve data mining. Data mining involves analyzing large amounts of unsupervised data using classical statistical methods. You will conduct data mining primarily via automatic control, although there is some human guidance required. While each industry is similar, it affects them differently. Insurance fraud may constitute false claims while banking examples tend to involve credit card theft and illegal transactions. It’s outliers in your data set, instances that deviate from the norm, that act as red flags for fraud. Tools like digital frequency analysis and SAS Enterprise Miner will identify these instances for you. You can then use what you learn to judge whether it is a possibility or not.
How Do Detection Tools Work?
Fraud detection tools function by predicting the frequency of numbers in a naturally-occurring data set. If a certain number appears where it shouldn’t, we call this an anomaly. When viewed next to the probability distribution of each digit, anomalies are often the first sign. Once you’ve identified a suspicious value in a data set, it could shed light on similar anomalies, leading back to the source of the problem. You will then have a clearer picture of how far-reaching or concerning the problem is.
Remember, like any Six Sigma issue, the more you know about your problem, the more prepared you are to resolve it. Use root cause analysis and DMAIC to get to the heart of the issue. Once there, you can cut the head off the snake.
Fraud of any kind, be it embezzlement, tax-evasion, sticky-fingered accounting, and computer virus all spell bad news for your business. But with Six Sigma, you can build a company culture focused on identifying and eliminating the possibility of it.