Using Data Analytics to Detect Fraud

The impact of fraud on companies is significant, with the Association of Certified Fraud Examiners‘ report revealing that businesses lose 5% of their revenue to fraud each year. Unfortunately, this trend is also on the rise in the Middle East, with 48% of companies in the region experiencing an increase in fraud in 2021 compared to previous years. To mitigate the financial and reputational risks associated with fraud, organizations must take proactive steps to detect and prevent fraud before it causes significant losses. Data analytics is a powerful tool that can help companies identify potential fraud patterns and anomalies in their data, allowing them to take timely action to prevent fraudulent activities from occurring. By leveraging advanced analytics techniques, such as machine learning and predictive modeling, organizations can gain a deeper understanding of their data and detect potential fraud in real time, reducing the risk of financial losses and reputational damage.

In this blog, you’ll find insights into how data analytics can help detect and reduce fraudulent activities in the early stages along with some examples of different fraud within organizations.

Intro to Fraud Definition and Types

According to the Committee of Sponsoring Organizations of the Treadway Commission (COSO), fraud is any intentional act or omission committed or planned to deceive others causing losses to the victim (organizations or individuals) and gaining for the fraudsters.

Fraud is a pervasive problem that involves the deliberate and intentional deception of others for personal gain. In many cases, fraud involves using unfair or illegal means to deprive individuals or organizations of their property, money, or other valuable assets. Despite the serious risks associated with fraud, many organizations remain vulnerable to these types of activities, particularly if they do not have robust internal controls and data analytics tools in place. Some common types of fraud include corruption, asset misappropriation, and financial statement fraud, among others. Let’s take some examples of each:

• Corruption fraud: This type of fraud typically involves the abuse of power by an individual with authority to engage in fraudulent activities or exert undue influence over decision-making processes. This can include a wide range of deceptive practices, such as money laundering, bribery, conflicts of interest, and other forms of misconduct.

• Asset misappropriation: Fraud of this type includes theft or misuse of company assets, including theft of cash, ghost employees, and false invoices.

• Financial statement fraud: This type of fraud is conducted in two ways; net worth overstatements such as fictitious revenue, concealed liabilities, improper asset valuations, or net worth understatement, such as understated revenue, overstated liabilities, and improper asset valuations.

But What Are The Main Motivations for Fraudsters to Conduct This Dishonest Behavior?

Fraudsters engage in fraudulent activities for a variety of reasons, but they are generally motivated by one or more of the factors described in the fraud triangle: opportunity, pressure, and rationalization. These three elements interact to create an environment where fraud can occur:

• First, the fraudster finds an opportunity to conduct fraud due to weaknesses in internal control or vulnerable IT systems.

• Second, need or greed are two main incentives for conducting fraudulent activities, such as pressure to perform, too much work, debt burden, or lifestyle needs.

• And the last point here is making the idea of fraud seem like a rational activity or what is called “rationalization”. Fraudsters in this case justify their behavior as being a response to not getting paid enough or that everyone else is doing it and nobody will notice the money.

Ways to Detect and Reduce Fraud with Data Analytics

A report from the ACFE showed that 77% of companies surveyed in 2020 noted an increase in fraud activities since the beginning of the COVID-19 pandemic, which proves that the likelihood of fraud is even more prevalent now than ever. Fortunately, data analytics can help in spotting red flags that traditional methods or humans might miss. Here’s how data analytics contribute to detecting potential fraud from inside and outside organizations:

Identify Potential Fraud Risks

To successfully detect and remediate fraudulent activities within your business, it’s important to first identify the areas or schemes where fraud is most likely to occur.

While internal controls are critical to reducing the risk of fraud, organizations must remain vigilant about potential areas of vulnerability. Despite the best efforts of internal controls, some areas remain susceptible to fraud due to their complex and often manual processes. For example, Fraudsters can exploit vulnerabilities in HR and payroll systems in various ways, such as creating ghost employees, falsifying timesheets, and inflating commissions or bonuses. These fraudulent activities can result in significant financial losses for organizations, as well as reputational damage and legal implications.

Identify The Red Flags or Risks Associated with This Identified Area or Scheme

After identifying the area or fraud scheme, organizations will need to identify red flags of potential fraud behavior. To spot red flags, organizations will need data analytics to detect anomalies and deviations from normal behaviors. Additionally, the implementation of data analytics enables businesses to analyze 100% of their data and identify threats automatically instead of manual sample testing.

Data analytics can’t only detect threats but can also send alerts to spot potential violations using custom triggers tailored to your business needs. For payroll and payment, red flags can include two or more employees with the same address or phone number, payments to employees for holidays or days off, terminated employee who is still on the payroll list, duplicate paychecks, etc. If a red flag is detected, organizations will need to conduct further inquiries.

Data Analytics Benefits for Detecting Fraud

Integrating a toolset with internal and external databases, including payroll and other ERP systems is vital when it comes to making informed decisions based on accurate data. All departments can benefit from data analytics, especially the IT team who can better keep an organization secure by deploying advanced machine learning.

The most common benefits of data analytics include:

• Examine and analyze large datasets: Using data analytics, organizations can guarantee that all datasets are examined and analyzed, which enables them to detect and spot high-risk suspicious activities efficiently and cost-effectively.

• Reveal and understand fraud patterns: Some unpredicted fraud patterns can be impossible to detect using conventional methods, even the smallest fraud offenses can be detected and flagged for further investigation.

• Take proactive action: The longer fraud goes unspotted, the more costly and harmful it becomes. Data analytics helps companies be proactive in any potential fraudulent activities, which reduces damage and losses.

Final Thoughts

Fraud is a highly risky and damaging crime that can often go unnoticed until after significant losses or damages have occurred. Traditional methods for detecting and preventing fraud can be time-consuming and prone to errors, making it difficult to mitigate the risk effectively. As a result, an increasing number of companies are turning to data analytics as a proactive measure to identify and prevent fraudulent activities before they cause significant harm. By leveraging advanced algorithms and machine learning, data analytics can identify patterns and anomalies in large datasets that may indicate fraudulent behavior.