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Information security requirements and standards are in a constant state of evolution. Recent issues, such as COVID-19 and the growing global reliance on mobile devices and remote work solutions, have played important roles in this ongoing transformation. At the same time, the increasing sophistication of cyber attackers has added new layers of complexity to the cybersecurity landscape. In this article, I will explore the importance of implementing fraud detection systems as a crucial measure to mitigate the impact of both traditional and emerging fraudulent schemes.
Challenges faced by financial institutions
The landscape of user behavior has undergone significant shifts, primarily driven by external factors such as the COVID-19 pandemic. This factor led to an increase in online transactions, coupled with reduced income streams for many individuals, resulting in decreased spending in specific user categories. Additionally, local conflicts, like the war in Ukraine and Israel, influence spending patterns in particular regions.
The implementation of restrictive measures and the resulting increase in stress levels have provided cyber crooks with more opportunities to exploit social engineering techniques through acts of intimidation. One prevalent scam involves fraudsters posing as bank security officials to deceive unsuspecting individuals.
Another concerning trend is the rise of legitimate channels that drive people to scam schemes via mainstream advertising platforms like Google and Facebook.
Furthermore, the economic hardships some people face have led them to seek alternative income sources, driving them to engage in various forms of online criminal activities. Some individuals become involved in schemes where they act as money mules or work in illegal call centers.
It is challenging for financial institutions to guarantee absolute safety. Malicious individuals can present counterfeit identification to authorize transactions that were initially denied by the anti-fraud system. While financial institutions strive to know as much as possible about their clients and run transactions carefully, they are constrained by data retention limitations (typically several months) and the need to respond within seconds, as stipulated by Service Level Agreements. So, again, achieving complete certainty about every transaction remains a huge problem.
Detecting suspicious activities becomes even more challenging when malicious employees request details about a specific client or transaction, as this falls within their routine work tasks. Some fraud detection systems use computer webcams or video surveillance cameras to monitor employee behavior. Modern surveillance systems have become more intelligent, leveraging artificial intelligence and historical data to perform comprehensive risk assessments and take action when unusual employee behavior is detected. However, these cameras may not always be effective in identifying deceitful behavior when employees remain almost motionless.
Understanding fraud detection systems
Fraud detection systems are designed to detect and prevent various forms of fraudulent activities, ranging from account hijacking and identity theft to fraudulent financial transactions. Initially adopted by financial institutions in the early 2010s in response to large-scale attacks on e-banking systems, fraud detection systems have since found their way into various sectors, including e-commerce, client loyalty programs, gaming services, contextual advertising platforms, and insurance. They play a pivotal role whenever online transactions and trade occur. While the concept of fraud detection systems is well-established, there are different types of products with unique characteristics that cater to specific needs and challenges.
The core functionality of fraud detection systems involves the examination of online transactions and user actions to assess the level of fraud risk. Typically, fraud detection systems consist of standard and system-specific rules, filters, and lists against which each action is checked. AI and ML technologies embedded within these systems significantly enhance their performance by analyzing client data and identifying patterns indicative of fraudulent behavior.
Types of fraud detection systems
Fraud detection solutions can be broadly categorized into two main types: transaction fraud detection systems and browser fraud detection systems.
- Transaction fraud detection systems
Transaction fraud detection systems employ behavioral and technical indicators as well as machine learning algorithms to assess the risk associated with each transaction. Typically, these systems rely on predefined rules and filters that activate based on specific algorithms or triggers. Various markers are employed to flag suspicious transactions, including unusually large or frequent transactions, transactions in atypical locations, etc. For instance, a user's account may be temporarily frozen if they initiate multiple identical actions, which is an example of a behavior-based evaluation relying on technical signs.
One of the most critical aspects of transaction fraud detection is their attempts to detect targeted social engineering attacks. In such cases, high-level behavioral indicators are indispensable for preventing or slowing down illicit operations.
The system leverages machine learning to process extensive data and identify hidden correlations between user actions that could signal fraud. Historical data on blocked operations, such as unauthorized fund transfers, is used to train the system to recognize patterns leading to denied transactions. This enables the system to independently detect and halt transactions showing signs of fraud.
- Browser fraud detection systems
Browser fraud detection systems do not analyze actual transactions but instead collect various technical details about the user's session. This includes information about the device, connection channel, and user behavior, such as keystrokes, touchpad/mouse movements, and more.
Browser fraud detection systems are good at detecting credential theft resulting from phishing attacks or data breaches. They can also identify fraudulent accounts at the initial stage when a fraudster attempts to sign up.
Identifying and preventing financial fraud
To effectively identify and combat financial fraud, it is recommended to establish a comprehensive cross-channel real-time fraud detection and prevention system capable of instantly identifying illicit transactions. Such a system should leverage a combination of techniques, including machine learning technologies through a risk assessment module and rule-based methods via a policy module.
The fraud assessment process should be based on user and event profiles, which generate a set of characteristics that can be used by a probabilistic model to determine risk levels. This model can take the form of a custom-built Bayesian tree, where nodes represent probability scores for various combinations of features and events. By incorporating the policy module and its customized rules, organizations can define their unique business scenarios and combine the resulting risk evaluation with various indicators drawn from user profiles and other sources.
Selecting the right fraud detection system
Choosing the most suitable fraud detection system involves assessing your organization's specific needs and risks. Different types of fraud detection systems examine distinct datasets, and the ideal approach may involve a combination of both transaction-focused and browser-based solutions. To save funds, some organizations, such as those offering personal accounts without internal payment systems, may find that a browser fraud detection system is enough for their requirements.
When evaluating fraud detection solutions, consider the following criteria:
- Price transparency: Evaluate the cost of the fraud detection system, including deployment, fine-tuning, administrator training, and related expenses. Ensure the overall cost does not exceed the potential losses it is designed to prevent.
- Testing and objective indicators: Assess the system's functionality in terms of risk level determination and objective indicators that provide actionable insights. Look for specific criteria, such as the detection of VPN server usage during website access or the system's ability to remotely access devices. These tangible indicators offer a more accurate assessment of the system's effectiveness.
- Machine learning and AI capabilities: Consider the extent to which the fraud detection system incorporates machine learning and artificial intelligence algorithms. ML and AI can be crucial in identifying risks by analyzing extensive data sets and uncovering hidden patterns and regularities indicative of fraudulent activity. Systems with advanced AI capabilities can adapt and improve their detection methods over time.
- Data privacy: Examine whether the system requires the collection of confidential or personal client data. A robust fraud detection system should minimize the need for collecting such data or employ privacy-enhancing techniques. This not only reduces the risk of data breaches but also eliminates the need for obtaining client consent to process personal data by third parties.
Conclusion
While no single fraud detection system can provide foolproof protection against all types of cyberattacks, the primary objective of an efficient security solution is to raise the complexity and cost of executing a fraudulent attack to the point where fraudsters opt for easier targets. Many products on the market meet the criteria outlined above. Still, the performance of a specific fraud prevention system depends on its internal algorithms, which are typically proprietary and not disclosed by developers.
To make an informed choice, organizations should consider running comparative pilot projects using several fraud detection solutions tailored to their specific needs and risks. Again, fraud detection solutions are highly effective and efficient tools for combating fraudulent activities. I advise thoroughly researching, comparing, and adopting a system that aligns with your organization's unique fraud prevention requirements. By staying proactive and vigilant, organizations can significantly improve their defenses against evolving threats.