behavioral analytics in fraud detection

Frauds are getting harder to detect. Frauds are getting harder to detect. The traditional protocol of using an alphanumeric username, password, and 2FA (two-factor authentication) is no longer adequate to protect against ever-evolving fraudulent incidents. CISOs need to proactively anticipate fraudsters’ movements to effectively minimize such incidents. Behavioral analytics can help identify and detect anomalies in advance to discern suspicious patterns that might otherwise escape human notice.

In this post, let’s explore what behavioral analytics is and why it’s one of the most important strategies for businesses to detect and prevent financial crimes. 

What is Behavioral Analytics in Fraud Detection?

Behavioral analytics utilizes user behavior patterns and activities to identify and detect potential fraudulent actions, such as account takeovers, subscription fraud, and payment fraud. This is done by examining various data points, such as IP addresses, device fingerprints, mouse movements, keystroke patterns, and navigation paths. Based on the analysis, it identifies suspicious behavior, assigns risk scores, and triggers appropriate actions to prevent such risks. 

For example, assessing failed login attempts or transaction volumes to detect potential brute-force attacks and correlating user behaviour across channels, such as web and mobile apps, to build an appropriate risk model.  

Why Behavioral Analytics Matters 

Behavioral analytics is important for several key reasons:

  • Analyzes differences between normal, everyday activities and anomalous events, trends, and patterns beyond the normal range.
  • Enables a proactive approach combining big data with powerful analytics to manage the latest and most threatening emerging attacks.
  • Enhances machine learning-based behavior models as it is exposed to new data points. It reduces false positives.

Challenges in Behavioral Analytics Detection 

Building a machine learning system that detects anomalous behavior and flags red alerts is easier said than done. Here is a list of challenges faced by behavioural fraud detection:    

ChallengesDescription
Data Quality and VariabilityLack of consistency in data sources, formats, and quality can hinder the accuracy and effectiveness of fraud detection systems.
Complexity of Behavioral PatternsUnderstanding and interpreting behaviors accurately can be challenging in the evolving fraud landscape.  
False Positives and NegativesBalancing the detection accuracy to minimize false alerts while not overlooking genuine fraud instances is a significant challenge.
Difficulty in Integration 
Compatibility issues, data silos, and technical constraints may impede seamless integration. 
Difficulty in Detecting New Fraud TacticsDetecting novel fraud patterns and adapting the analytics models accordingly is a huge challenge.  
Data Privacy ConcernsSafeguarding user privacy while effectively detecting fraud.  

Benefits of Behavioral Analytics in Fraud Detection

Traditional security measures often fall short in detecting these complex threats. This is where behavioral analytics emerges as a powerful tool, offering numerous benefits in fraud detection and prevention. Here are key benefits behavioral analytics offers in fraud detection: 

Benefits of behavioral analytics

Understanding Behavioral Patterns: Behavioral analytics is an effective approach that helps establish baselines and continues monitoring. In this way, it is possible to reveal certain behavior patterns that may be considered anomalous as they drift away from normal behavior. This understanding is one of the crucial prerequisites for fraud detection, as many crimes cannot be noticed by single behavioral specialists alone.

For example, a fintech company could analyze activities such as login times, transaction amounts, or device fingerprints.

Early Fraud Identification: One of the key advantages of behavioral analytics is its ability to detect fraudulent activities at an early stage. By continuously analyzing user behavior in real-time, the system can identify suspicious patterns or anomalies as they occur. This early detection enables organizations to take prompt action, mitigating potential losses and minimizing the impact of fraud.

For instance, an e-commerce retailer can monitor user browsing patterns, cart abandonment rates, and purchase histories in real-time. If a user exhibits sudden and significant deviations from their typical behavior, such as rapid multiple purchases or unusually large transactions, the system can immediately flag these activities as potential fraud attempts.

Real-time Continuous User Behavior Analysis: This reveals any suspicious patterns or deviations from the norm as soon as they appear. Thus, businesses can act promptly to prevent serious financial losses and reduce the damage from fraudulent activities.

For example, consider the case of an e-commerce retailer’s behavior analysis monitoring consumers’ browsing behavior, previous cart abandonment, and purchase history. If a consumer shows large, sudden changes from established patterns, such as very rapid purchases or suddenly large basket sizes, this might indicate a potential fraud attempt.

Reducing False Positives: The problem with most fraud detection systems is the high number of false positives, resulting in wasted unnecessary investigations and resources. However, a behavioral analytics-based fraud detection system is based on machine learning algorithms, and it significantly reduces false positives.

For example, a credit card company can train its behavioral analytics system to recognize customers’ regular travel patterns. It can accurately distinguish between legitimate and fraudulent behavior patterns, minimizing false alerts.

Adapting to New Fraud Tactics: Fraudsters are constantly evolving their tactics and methods to evade detection. Behavioral analytics leverages machine learning algorithms that can adapt and learn from new data to upgrade their models.

For example, a cybersecurity firm can train its fraud detection models on newly emerging phishing techniques or malware signatures to refine the models.

Personalizing Security Measures: By developing detailed user profiles based on behavioral data, security teams can take personalized security measures.

For example, an online banking platform can assign risk scores to individual customers based on their behavior patterns, like login locations, transaction types, and device fingerprints. Users with higher risk scores can be subjected to additional authentication steps or stricter access controls, such as biometric verification or IP address whitelisting. 

How Behavioral Analytics Mechanism in Fraud Detection Works

Here is how the behavioral analytics mechanism involves many key components as outlined below:

How behavioral analytics works
  • Establishing Baseline Behavior

Organizations collect and analyze data on typical user interactions with their systems or applications to establish a baseline for user behavior. This baseline serves as a reference point to recognize departure from standard patterns of behavior.

 For instance, a banking institution might want to keep a tab on transaction types, login times, and devices put into usage by customers to set up a baseline for their banking activities.

  • Real-time Monitoring

Behavioral analytics systems repeatedly track user activities in real-time during their interactions with systems or applications. This live monitoring aids in the early detection of anomalies or suspicious behavior as and when they happen.

For example, an online retail firm might continuously oversee user browsing patterns, shopping cart activities, and purchase histories to identify threats of potential fraud in real-time.

  • Applying Machine Learning

Machine learning algorithms effectively process a huge amount of behavioral data accumulated from users. These algorithmic models uncover complex patterns, correlations, and anomalies that might be challenging for humans to observe and recognize. 

For instance, a cybersecurity firm might employ machine learning models to examine network traffic and user activity logs, discovering potential security hazards or malevolent behavior.

  • User Profiling and Risk Scoring

Behavioral analytic mechanisms create detailed user profiles depending upon observed behaviors. These profiles are used to evaluate the risk level of each user’s activities. Risk scores are established based on departure from traditional baseline behavior.

 For example, an e-commerce platform might profile users based on their purchasing habits, and subsequently assign higher risk scores to those displaying odd or distrustful behavior patterns.

  • Fraud Detection and Prevention

A key application of behavioral analytics is identifying fraud and  then devising means to prevent it. By examining user behavior patterns, companies can determine potential deceitful activities like identity theft, account takeovers, or illegitimate access endeavors.

 Upon detection, necessary measures can be implemented to diminish risk and impede and avert further fraud.

 For instance, a credit card company might use behavioral analytics to spot out of the ordinary spending patterns or transactions from unknown locations, signaling chances of further malicious activities.

  • Adaptive Security Measures

Behavioral analytic mechanisms allow organizations to deploy dynamic security measures that adjust to changing user behaviors. As user behavior unfolds over time, security protocols can be  updated in real-time to ensure an adequate level of protection.

For example, a cloud-based service provider might modify its access controls and need for authentication based on ascertained behavior patterns, thus warranting enhanced security for high-risk activities or users.

Potential Red-flags in Behavioral Analytics  

Behavioral analytic systems deploy a variety of red flags to determine probable threats or suspicious activities. These red flags do a good job of identifying behavior that is not in sync with the established baseline, thus demanding further investigation or setting off adaptive security measures. Here we list a few common behavioral red flags.    

Potential red flags
  1. Unusual Transaction Values: Transactions that are significantly larger or smaller than usual, or those that deviate from a user’s typical transaction range, may signal fraudulent activities like money laundering or unauthorized account access.
  2. Rapid Multiple Transactions: A sudden surge of transactions in a short period could be a potential red flag, potentially indicating attempts to drain accounts or make unauthorized purchases before being detected.
  3. Logins from Multiple Locations: Accessing a user’s account from various geographic locations within a short period of time could be indicative of account sharing, credential endangerment, or the possibility of malicious unauthorized access attempts from a variety of locations.
  4. Using Anonymous Services: Employing virtual private networks (VPNs), proxy servers, or similar anonymizing services to obscure the origin of a connection can trigger suspicions, as such tools are frequently utilized by nefarious elements to conceal their identities.
  5. Changes in User Behavior: Abrupt and notable deviations from a user’s usual behavior patterns, like alterations in browsing habits, transaction types, or activity times, can serve as warning signs suggesting potential account takeovers or compromised credentials.
  6. Deviation in Behavioral Biometrics: Behavioral biometrics, encompassing keystroke dynamics, mouse movements, and interaction patterns, serve to identify users by their distinctive behavior. Variances from a user’s usual biometric patterns may indicate an attempt at unauthorized account access.

By keeping a close eye on these and other behavioral warning signs, organizations can find themselves in a good position to swiftly identify potential threats, enact incident response protocols, and implement necessary measures to mitigate risks, in the process safeguarding their systems, data, as well as their users.

How Sensfrx Helps in Behavioral Analytics in Fraud Detection

Sensfrx is equipped with sophisticated tools and strategies to combat various forms of fraud:

  1. Strong Encryption: By employing end-to-end encryption, Sensfrx shields sensitive user data, ensuring it remains secure and inaccessible to unauthorized parties. This proactive measure prevents potential data breaches and safeguards against identity theft.
  2. Device Fingerprinting: Sensfrx captures detailed information about devices accessing the platform, including browser types, operating systems, and IP addresses. It actively monitors these devices for any abnormal behaviors that may signal fraudulent activities. By flagging suspicious devices in real-time, Sensfrx mitigates risks before they escalate into security breaches.
  3. Smart Algorithm: The platform continuously tracks and analyzes user behaviors, login patterns, and transaction activities. This intelligent monitoring system promptly identifies deviations from normal usage patterns, enabling swift detection of suspicious activities that could indicate fraud.
  4. Dynamic Risk Scoring: Sensfrx evaluates transactions in real-time using a dynamic risk scoring mechanism. This approach factors in variables such as user behavior history, device information, and transaction patterns to assess the likelihood of fraud. By assigning risk scores instantly, Sensfrx empowers businesses to respond promptly to potential threats, minimizing financial losses and protecting user trust.
  5. Fraud Validators: Leveraging advanced fraud detection capabilities, Sensfrx swiftly identifies and verifies instances of fraudulent activities. This proactive detection allows for immediate action to prevent fraud before it impacts users or compromises business operations.
  6. Smart Fraud Detection: Through the application of machine learning algorithms, Sensfrx detects sophisticated fraud patterns such as account takeovers and fraudulent transactions. By continuously learning from data insights, Sensfrx evolves its detection capabilities to stay ahead of emerging fraud tactics, ensuring robust protection against evolving threats.

Conclusion 

Legacy fraud detection tools suffer from limitations, such as too many false positive alerts, and whenever they detect fraudulent activity, it’s too little, too late. To keep pace with the evolving complexity of attackers, they need to continuously upgrade their behavioral data models to process emerging threats. .Sensfrx employs sophisticated behavioral analytics based sophisticated fraud models to examine activities throughout the customer lifecycle. Get in touch with us to know more about how it can help.