Fraud Detection

What is fraud detection?

Fraud detection refers to the set of activities or techniques businesses implement to protect assets, identities, customer and business information, accounts, and transactions from fraudsters. This is mainly done by analyzing user activity and behavior patterns and comparing them with a baseline or profile representing normal user behavior.

To detect and mitigate fraud, companies utilize fraud detection software. Depending on the software product chosen, fraud detection will be performed in real time, near real time, or in batch. If a user activity falls outside of the accepted baseline, the concerned authorities are alerted.

Fraud detection is applied across numerous industries, including banking, insurance, and e-commerce. Therefore, fraud detection can also be defined as a method of preventing money from being acquired through deception.

In the banking industry, fraud detection is applied to identify forged checks or the use of stolen credit cards. It’s also instrumental in detecting money laundering and preventing customer account takeover. For insurance companies, fraud detection backed by computer vision software can help detect fraudulent claims or exaggerated losses.

Web fraud detection can be defined as the methods used to detect high-risk and illegitimate online activities, identity thefts, and cyberattacks. Web fraud detection tools typically focus on identifying account takeover, account creation, loyalty program abuse, and payment fraud. In the case of account creation and takeover fraud, these tools help organizations identify fraudulent users. Similarly, payment fraud detection aims to identify fraudulent payments that may have been performed using stolen credit cards.

Fraud detection tools analyze user sessions, location, devices used, transaction history, and more to detect online fraud. It’s important to note that fraud detection isn’t an intrusive activity unless a particular user’s activity is suspicious.

Fraud detection is also used extensively on social networking websites to detect bot activity. Bots are also employed to inflate click numbers on advertisements and spoil the effectiveness of pay-per-click (PPC) advertising. To combat such fraudulent bot activity, businesses can utilize click fraud software.

Types of fraud detection systems

In terms of fraud detection systems, they can be broadly classified into two categories—rule-based fraud detection systems and machine learning-based fraud detection systems.

• Rule-based fraud detection systems: As the name suggests, rule-based systems detect fraud by looking at on-surface and evident signals. They are straightforward and detect fraud based on the detection rules established by fraud analysts. This also means that maintaining rule-based fraud detection systems requires a lot of manual work.
• Machine learning-based fraud detection systems: On the other hand, machine learning-based systems are capable of detecting unlikely or hidden events that will not be detected by rule-based systems. They can find hidden patterns and correlations in data and often offer real-time processing. These solutions require less manual work and are ideal for processing large volumes of data.

Fraud detection techniques

Statistics play a critical role in fraud detection. It’s interesting to note that machine learning, which is crucial for fraud detection, is basically a form of applied statistics. The following are the statistical data analysis techniques used to detect fraud:

• Regression analysis: Regression analysis is used to examine the relationship between two or more fraud variables and estimate the relationship between dependent and independent variables. It’s useful to predict potential fraud by analyzing user behavior.

• Data matching: Data matching is the process of comparing two different data sets and matching them against each other. It’s useful to identify suspicious transactions and users.
• Statistical parameter calculation: Statistical parameter calculation refers to calculating numerous statistical parameters such as average, quantile, probability distribution, and performance metrics on the data collected to detect fraud.

Fraud detection best practices

Advances in the field of fraud detection have made it possible to quickly detect fraudulent activities and act before they become catastrophic events.  Organizations can adopt several technologies, techniques, and practices to stay ahead of malicious actors. At the same time, it’s crucial to find vulnerabilities, both in people and systems, and rectify them before they are taken advantage of by attackers. The following are some of the best practices to detect and prevent fraud:

• Monitor continuously: Continuous monitoring and auditing are crucial to fighting fraud. In the case of the banking industry, this would mean constantly monitoring transactions and looking for anomalies. Making fraud monitoring a continuous process can significantly improve the number of fraudulent activities detected and prevented.

• Invest in security awareness training: A number of cybersecurity attacks and instances of fraud can be prevented with a robust security awareness training process. For instance, fraudsters may attempt to steal information from employees, which can be easily avoided with proper training. At the same time, employees and end users need to be aware of what exactly a threat or attempt of fraud is. For this, companies should invest in security awareness training software and make the training an ongoing process.

• Develop fraud risk profiles: Organizations should list the business areas where frauds are most likely to occur. After identifying such areas and associated risks, they should reorder the list based on how likely a particular area of the organization will be affected by a risk. Reordering the list based on how damaging a fraud risk is also makes sense. This process is called fraud risk profiling, and it’s a good idea to include all stakeholders and decision makers in the process.

• Utilize machine learning: It’s easier to detect fraud with automation than just manual labor. Machine learning software can help automate several tasks associated with fraud detection, including risk prioritization, alert handling, response, and investigation. For instance, security orchestration, automation, and response (SOAR) software allows security professionals to create response workflows and automate incident management. Similarly, risk-based vulnerability management software can help prioritize vulnerabilities using machine learning.

• Invest in identity verification: Identity theft is an increasing form of fraud, both in terms of customers and employees. Organizations should invest more in identity verification software and risk-based authentication (RBA) software to identify and cut off malicious actors.
• Encourage or incentivize fraud reporting: Even employees who don’t work to prevent fraud may come across fraudulent or suspicious activities. For example, an employee might receive a phishing email, and reporting it instantly might make things easier for the IT department. To promote such precautionary measures, organizations should encourage employees or, if feasible, incentivize employees to report suspicious activities.