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Employee Fraud: Big Data’s Role in keeping it at Bay

The term “Big Data” was first coined by computer programmer John Mashey in the 1990s. It refers to the plethora of information mined from the human race’s collective body of internet activity and digital devices, the true extent of which is almost inconceivable.

Unstructured data is one of the concerns of Big Data management. “Unstructured” is the data that we leave behind on social media, in chat rooms, and via similar live streams, as opposed to “structured” data that we input into things like bill-pay forms and online registration and application forms. It can be compared to an archaeologist finding a random part of a tool from the Stone Age as opposed to a carefully carved medieval proclamation. Making sense and use of unstructured information is one of Big Data’s challenges.

Big Data and the Threat of Employee Fraud

It’s hard to name an industry or sector today that doesn’t utilise Big Data in at least some small way. To illustrate, Singapore’s “Smart Nation” initiative is analysing people’s age and geographical profiles as well as their recreational, commuting, and shopping habits for the purpose of organising information into a useful resource for both business and civic planning.

Big Data discoveries can lead to improved customer relationships, more efficient marketing strategies, cost-effective utility programs, tailored educational experiences, improved logistics, and much more. It can also be used to help curb internal corporate fraud. As such, companies can no longer afford to ignore the benefits of Big Data analysis, especially in terms of combating fraudulent activity.

According to recent reports, the typical organisation loses 5% of its revenues to fraud each year. Estimates also reveal that U.S. retailers alone lose US$60 billion dollars each year to fraud, a huge part of which is perpetrated by employees.

To show you just how diverse and serious the issue is, here is a partial list of employee fraud schemes: Theft of inventory, cash, pay cheques and supplies; selling of trade secrets and customer information; forging of cheques, creating shell companies (and customers); perpetration of bribery, kickbacks, and supply chain scams; cheating on timesheets and expense reports; bill payment lapping, fund diversion frauds, concealment of securities profits (or losses), and implementing fake overdraft charges. These things hurt more than just a company’s profit—they can cost it its reputation.

The Role of Data Analytics: Software Sees What People Don’t

The speed of information gathering and interpretation via digital devices can never be matched by a human’s (or even a team of humans’) limited capabilities. Additionally, computers are unbiased in their handling of data (unless otherwise programmed). This ensures faster, more accurate, and precise fraud reporting, resulting in the faster retribution to those involved and swifter implementation and improvement of anti-fraud measures within an organisation.

Data analytics can also reveal unusual activity in “dormant” accounts, track data access points (or inconsistencies), show changes in frequency of certain activities, and unexplained variations in customer and vendor transaction behaviours. Data algorithms recognise patterns and disruptions in patterns, and provide a broad scope of the movement of goods, money, and data throughout, and in and out of, a company.

As such, proper data gathering and interpretation gives a company key insights on all its internal processes, giving managers and owners the ability to detect suspicious activity and take action—even before any potential negative impacts and before the perpetrators become aware that they’ve been found out. Using Big Data platforms also allows for macro and micro monitoring of a business, giving the appropriate/concerned internal departments and executives a precise idea of what should and what shouldn’t be happening in terms of business processes and operations.

In 2015, Australia’s Department of Human Services (DHS) discovered $1.7 billion in welfare recipient fraud thanks to data analytics, specifically, data matching software. According to DHS Secretary Kathryn Campbell, “At some points it was going to cost us more to act on that information. The cost of doing that has now decreased.” Big Data software will be used by the DHS to help deter future theft.

All these show Big Data’s potential in the realm of addressing and preventing internal corporate fraud. Through data analytics, businesses are now increasingly becoming more proactive rather than just being reactive when it comes to dealing with fraudulent activities.

However, to have a truly effective and efficient internal anti-fraud system, it will be best to partner basic anti-fraud tips with data analytics.

Basic Anti-fraud Tips

There are several practices that, when combined with data analytics, can provide businesses with a strong defence against employee fraud. These include performing background and reference checks, providing a way for employees to confidentially report suspected fraud, and using checks and balances for internal corporate transactions that are vulnerable to fraud, especially those that are monetary in nature. Part of this process could well be the use of advanced data tools to harmonise employees social media and other unstructured information with the formal employment application information.

Be attentive to changes in employee behaviour: Are they suddenly coming in early or staying late more frequently? Have they taken “ownership” of a responsibility that is generally a shared one? Do they seem overly nervous when a change in procedure is announced? Any of these can be explained by things other than fraud, so proper monitoring and research is needed. Here’s where Big Data can also come in. Observation coupled with data procedures such as analysis of historical and biometrics data, analysis of employee activity patterns, inventory checks, and correlation with unaccounted losses will let you know if these changes are harmless or if there’s an underlying cause that needs proper action or further investigation.

Simply put, Big Data isn’t just for improving sales and increasing profits, it can also improve operations from within and significantly lessen the risk and effects of internal problems. Through data management solutions such as Latize Ulysses, businesses will be able to gather the needed insights from Big Data to enhance key business processes and build an effective defence against employee fraud.

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