Using data analytics to enhance fraud detection

23 November 2023 5 min. read
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Research shows that businesses lose up to 5% of their revenue to fraud each year. One key way of enhancing the fraud mitigation process is through data analytics. Experts from AIGC outline how data analytics can add value and what types of fraud can be pushed back.

In the Middle East, the case for tackling fraud is in relative terms larger than in most parts of the world. A recent global benchmark highlighted that fraud is more prevalent in the region, and notably, the risk is still picking up – in 2022, nearly half of the companies in the region experienced an increase in fraud compared to the year previous.

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. This is where data analytics come in.

Using data analytics to enhance fraud detection

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.

Ways to reduce fraud with data analytics

The use of data analytics can improve the fraud reduction process across the full lifecycle, from prevention and identification to resolution. Two notable use cases:

Identify the potential fraud risks
To successfully detect and remediate fraudulent activities, it is important that organizations 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
After identifying the likely areas where fraud may occur, organizations then 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.

Notably, data analytics doesn’t only detect threats but can also send alerts to spot potential violations using custom triggers. In payroll and payment for instance, 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.

The benefits of data analytics

Overall, all departments can benefit from data analytics, including the IT and business. Some of the most common benefits of data analytics include:

Examine and analyze large datasets
Using data analytics, organizations can ensure 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. With data analytics, 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 managing potential fraudulent activities, which reduces damage and losses.

What types of fraud can be reduced?

Fraud comes in numerous forms and shapes, with corruption, asset misappropriation, and financial statement fraud the most common in larger organizations.

  • 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.

Final remarks

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.

Against this backdrop, and with the rise of technological advancements, an increasing number of companies are turning to data analytics as a proactive measure to identify and prevent fraudulent activities before they cause harm.