Internal fraud is a huge risk for businesses. According to the Association of Certified Fraud Examiners, companies lose nearly 5% of their revenue each year to employee and executive fraud. This amounts to nearly $3.7 trillion globally, with a median loss for each business at around $145,000 every year. This is a significant amount for any company, especially for SMEs.

Many cases of internal fraud aren’t uncovered until sometime after they’ve occurred, when the company has already suffered losses. There are many classifications; some of the most common include:

• Employee theft of company-owned goods or merchandise
• Misappropriation of funds related to corporate credit cards and employee expenses
• Embezzlement of company funds, whether at the local or corporate level
• Payment fraud, wherein employees provide false invoices from vendors

With all these different areas to patrol, how can an organisation stay vigilant? Big Data can certainly help. Big Data is the collective term for large sets of data available from many sources that an organisation can collect and utilise. Businesses have been using it for years now to improve marketing, sales, and operations, among others, and it can also definitely have a huge role in dealing with internal fraud.

Big Data’s Role in Dealing with Internal Fraud

If you are a retailer, restaurant, or any other organisation that accepts payments, you can analyse the data collected from these transactions. Through data analytics, you will be able to identify abnormal trends or repeated voids or returns that may raise suspicion. You can then employ measures (e.g. further monitoring of anomalous transactions, cross-checking with previous payment records) that can officially confirm if fraud has indeed occurred.

Big Data also allows organisations to be proactive in their pursuit to curtail internal fraud. Data sets that you have established as normal or standard act as a benchmark. When differences begin to appear, this could be a red flag. That specific data set can then be analysed to determine if a fraudulent act has occurred. For example, if a preferred vendor has provided services to your company for many years then you have rich data related to that relationship. If the invoices suddenly become higher without any change in services, items purchased, or the contract, this is a signal that something may be wrong.

You can actually set up data queries to look for known areas of fraud like those described above. However it’s the “unknown” areas that present the biggest challenges. This is where Big Data can provide insights that were hard to extract previously. Through analysis of attributes such as transaction patterns, monitoring of changes in purchases, inventory, and company cash flow, and examination of new fraud trends from outside the company, Big Data can show decision-makers additional areas they must keep an eye on—even if fraud hasn’t been detected in those areas within the company in the past. The beauty of Big Data is that it gives you the ability to look where you previously could not or did not believe was necessary.

Big Data as a Legitimate Tool for Internal Fraud Detection

The World Economic Forum recognises Big Data as a key technology in fighting fraud. Because fraud analytics are now in use and in real-time, agencies have been able to detect, interrupt, and remediate fraud, resulting in millions of dollars in potential cost savings.

One notable example of a retailer using Big Data to discover internal fraud is JCPenney, a popular American clothing retailer. They implemented a data-driven fraud detection project that required the use of the company’s point-of-sale (POS) reporting system and the examination of item delete reports. When abnormal information or trends appeared in these reports, local loss prevention personnel were notified. Initially, JCPenney had a certain threshold for deleted items. That ratio was only flagging 1% of transactions, so the team looked at a far larger representation of item deletes and also included context data such as store associate information.

By expanding the parameters and looking at more data, JCPenney was able to recognise internal fraud more accurately as well as reduce the workload of the company’s fraud and detection team by about a month annually. This is an example of how data analytics can be used by a company to be a step closer to its business goals such as preventing internal fraud and reducing workload while maintaining productivity. Modern Big Data management solutions are now able to accommodate this multi-dimensional approach of harmonising disparate data sets much more effectively than in the past. What may seem to be of no significance may then become very significant when extra data is added to the mix.

Aside from detecting fraud, the use of data analytics provides businesses with the required information to help prevent it in the first place. Results from successful fraud detection and monitoring show businesses potential at-risk areas as well as which steps work (and those that don’t) in terms of fighting fraud. This enables them to plan ahead and come up with updated internal fraud detection protocols and strategies.

Analysis and Insights are Keys to Successfully Dealing with Internal Fraud

Big Data is useless if you don’t know how to properly analyse it. Whether for increasing business profits or detecting internal fraud, the key is getting the insights you need to take proper action. Latize’s intelligent data management platform Ulysses captures and harmonises large volumes of disparate data, leading to actionable insights businesses can use to achieve their goals.