How To Use Behavioral Analytics To Understand & Prevent Fraud

Online fraud costs businesses ~$4.5 million annually. It’s high time you use AI and machine learning to prevent phishing attacks and transaction fraud. This article explores how behavioral analytics and machine learning help businesses detect fraud more effectively.

You will learn best practices for collecting relevant user data, establishing baseline behaviors, and monitoring for anomalies. By the end of this read, you will know what steps to take to protect your assets and build customer trust.

Let’s get into it.

What Is Behavioral Analysis In Fraud Detection?

Behavioral analysis involves studying users’ online activity patterns like when and where they log in or how much they usually spend. You collect and analyze this data to spot odd user behavior that indicates fraud, such as a sudden big purchase or an account login from an unusual location.

It uses machine learning to catch fraudsters before they cause trouble. In other words, behavioral analysis is a proactive way to keep your accounts and information safe. The best part? You can prevent 90% of fraud using machine learning algorithms.

4 Steps To Use Behavioral Analytics To Prevent Fraud

Consider which strategies align with your security needs as you apply these steps.

1. Collect Relevant Data

Gather comprehensive user information to build a robust dataset to analyze. Make sure that your data collection processes comply with privacy regulations to keep user trust and integrity.

Here are the important types of data you should collect:

1.1 Transaction Logs

• Record all financial data (transaction amounts, dates, times, and locations).
• Track the payment method types used (credit card, bank transfer, etc.).
• Check how often transactions occur for each user.

How to collect transaction data:‍

• Implement database logging to automatically record all transaction details.
• Integrate with payment gateways like PayPal to capture financial information.

1.2 User Activity Records

• Collect data on login times, IP addresses, and devices used.
• Track the pages visited, the visits' duration, and actions taken on each page.
• Log interactions like clicks, form submissions, and downloads.

‍How to collect user activity data: ‍

• Use web analytics tools like Google Analytics, Adobe Analytics, or Crustify to track user interactions and browsing patterns.
• Develop custom tracking scripts to log specific user actions like clicks. Embed these scripts into your web pages and applications.

For example, you can use the User ID view on Google Analytics to view user engagement across devices. It shows the device category, platform, and data source for each user so you can track their activity online.

1.3 Device Information

• Identify the user device like a computer, smartphone, or tablet.
• Record the operating system and browser used for each session.
• Capture location information to detect unusual access patterns.

‍How to collect device information: ‍

• Use device fingerprinting techniques to collect data on device types, operating systems, browsers, and geolocation. Fingerprints can help identify unique devices for fraud detection.
• Extract device and browser information from user agent strings. This data is available in server logs and can be parsed for detailed insights.

1.4 Account Information

• Maintain detailed user profiles. They should include account creation dates, contact information, and preferences.
• Track changes to account settings like password updates or new payment methods added.

‍How to collect account information: ‍

• Implement a robust user profile management system. Make sure that your system captures account creation dates, contact information, and preferences.
• Log all important account events like password changes and new payment methods. Use event-driven architecture to capture these changes in real-time.

1.5 External Data Sources

• Use external sources like credit bureaus, social media profiles, and cybersecurity firms.

‍How to collect external data sources: ‍

• Integrate with third-party data sources via APIs to collect additional information. For example, credit scores, social media profiles, and industry fraud reports. Make sure you have the needed permissions and comply with data privacy regulations.
• Subscribe to threat intelligence feeds from cybersecurity firms and government agencies like FireEye, CISA, and the Federal Trade Commission. These feeds provide real-time updates on known fraud tactics and trends.

2. Establish Baseline Behaviors

Analyze historical data to define the normal users' behavior. This will serve as a reference point to identify anomalies. Make sure to update and refine these baselines so that your fraud detection system stays responsive to new threats.

Here’s how to establish baseline behaviors:

2.1 Segment Users

Segment users into different categories based on their behavior, and other relevant factors. Common segments can include regular users, high-frequency transactors, and occasional users. For example, high-frequency transactors will need more real-time monitoring than occasional users.

Use clustering techniques to group users with similar behavior patterns. This helps in understanding the typical behavior for each segment more accurately.

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2.2 Define Normal Behavior Patterns

Perform statistical analysis to identify key metrics and patterns for each user segment. This includes calculating averages, medians, and standard deviations. Identify these key behavioral metrics to detect unusual patterns:

• Average transaction amount and frequency
• Typical login times and locations
• Common browsing paths and interactions
• Regular device usage patterns

2.3 Use Machine Learning Models

AI and machine learning models process and analyze large datasets with multiple variables to detect complex fraud patterns and chargeback trends that humans might miss. They learn from historical data, identifying correlations and anomalies in real-time. They spot unusual behaviors that indicate fraud, even if they haven't seen those specific patterns before.

For instance, MedicalAlertBuyersGuide uses reCAPTCHA v3 to identify between human users and bots. This integration adds an extra layer of security by preventing automated attacks and making sure that only legitimate users access the service.

Similarly, let’s say an online retailer processes millions of transactions daily. A traditional rules-based system might flag transactions from a specific country as high-risk. However, a fraudster could bypass this by using a VPN to change their location. AI and machine learning models, on the other hand, analyze a combination of factors like transaction amount, time, device used, and user behavior patterns.

If a habitual low-spending customer suddenly makes a large purchase from a new device in a different location, the model would flag this as suspicious. The retailer can detect this fraud attempt more accurately and quickly to prevent potential losses.

If you're an eCommerce business, use an ML tool like Chargeflow to boost your fraud prevention. Chargeflow automates chargebacks and disputes with advanced machine learning and artificial intelligence. This saves you time, reduces manual work, and recovers lost revenue.

3. Monitor & Analyze Anomalies

This process involves continuously observing user behavior, identifying deviations from established baselines, and investigating these irregularities to determine if they show fraudulent activity.

Here’s how to do it:

3.1 Monitor User Behavior In Real-Time

Implement real-time monitoring systems that track user behavior as it happens. Use Splunk, Grafana, or Kibana to provide real-time data visualization. Connect your monitoring tools to transaction logs, user activity records, and device information.

3.2 Detect Anomalies

Set thresholds and rules based on established baseline behaviors. These can include limits on transaction amounts, frequency of logins, or geographic locations.

At this stage, you can also use machine learning models trained to detect deviations from normal behavior. Models like Isolation Forest or Autoencoders can identify subtle patterns and anomalies that rule-based systems might miss.

You can also use ML tools to detect specific fraud types. For example, in the case of chargeback fraud, Chargeflow can perform a comprehensive fraud analysis. It calculates the ChargeScore based on your account history, evidence strength, and other data points. It pulls evidence from over 50 sources, including third-party data.

It then optimizes ChargeResponse in real-time based on your store type, dispute history, and an ever-improving algorithm.

To better understand how anomaly detection works, let’s find out its 3 layers with their respective methods.

Here’s a list of anomalies to watch out for:

• Sudden changes in user profile information.
• Frequent switching between multiple devices.
• Logins from unfamiliar or suspicious IP addresses.
• Login attempts outside typical user behavior hours.
• Transactions or logins from distant geographic locations.
• Frequent small transactions testing stolen payment methods.
• Changes in account settings without typical preceding behavior.
• Transactions involving high-risk regions that are known for fraud.
• Multiple failed login attempts indicating possible brute force attacks.
• Uncharacteristic browsing patterns or accessing unusual services.
• Sudden large transactions that deviate from normal spending habits.
• Multiple login attempts from different geographic locations in a short time.
• Addition of new beneficiaries or transfer destinations never interacted with before.

3.3 Analyze & Investigate Anomalies

3.3.1 Risk Scoring

Behavioral monitoring systems create user profiles based on their behavior patterns and assign them a risk score. This score shows how likely a user is to be involved in fraud. Risk scores are calculated using past behavior, detecting anomalies, and machine learning algorithms.

Users with higher risk scores show unusual behavior and are given extra checks, like additional authentication steps or manual reviews.

3.3.2 Contextual Analysis

Analyze the context of anomalies to understand their nature. Look at factors like the user’s location, device, time of activity, and other environmental details to see if they match expected behavior patterns. For instance, a login from an unusual location or an unfamiliar device can signal possible fraud.

3.3.3 Manual Review

Have fraud analysts manually review high-risk anomalies. This human element adds a layer of scrutiny to ensure accurate detection and reduces false positives.

4. Leverage Automated Responses

Configure automated alerts for significant anomalies. These alerts can be sent to security teams or directly to affected users for immediate action.

Also, implement automated responses like:

• Temporarily locking accounts
• Flagging transactions for review
• Requiring additional verification steps for suspicious activities

The automated response type will depend on the sector and product type you're selling. For example, in the banking sector, unusual withdrawal patterns may trigger a credit freeze.

Similarly, if you're selling high-value eCommerce products like electronics, medical devices, or premium vegan supplements, consider the following measures:

• Dynamic Pricing Alerts: Automatically flag transactions with significantly altered or discounted prices beyond typical ranges.
• Context-Aware Verification: Add extra verification steps for transactions made during unusual hours or shipped to high-risk regions to confirm they are legitimate before processing.
• Delayed Shipping for High-Risk Transactions: For orders flagged because of unusual purchasing patterns, hold the shipment for 24-48 hours to conduct a thorough review.

Case Study: How PayPal Uses Behavioral Analysis to Prevent Fraud

PayPal uses behavioral analysis to prevent fraud by feeding its algorithms various types of data. This includes device information, email checks, identity scores, session data, and enrollment details. Hundreds of signals are built on this data to spot issues like differences in a user's real and stated locations.

For login fraud, PayPal uses machine learning to check if a customer is legitimate in real time. They analyze device, email, IP, phone, transaction, and behavior information. For payment fraud, they compare past transactions and look for red flags like address mismatches and large orders. This approach helps keep users and the company safe.

Case Study: How Transparent Labs Prevents Credit Card Fraud

Transparent Labs, an eCommerce store selling fitness supplements, uses advanced analytics to prevent credit card fraud and analyze login times, browsing habits, and purchase patterns to detect unusual activity quickly.

With the "buy now, pay later" feature, Transparent Labs closely monitors deferred payment activities to prevent fraud by tracking the user’s payment history and purchase behavior. The system flags these activities for further review if patterns suggest potential misuse, like frequent last-minute payment failures.

Benefits Of Behavioral Analysis In Fraud Detection

To get a better sense of how much you need behavioral analytics, consider these benefits and see if they match your needs.

i. Proactive Fraud Detection

63% of businesses have faced fraud in the past 12 months. The good news, behavioral analysis catches fraud early by spotting unusual behavior patterns before they cause major harm. Early detection can reduce fraud costs by up to 42% and minimize the need for extensive manual reviews.

ii. Enhanced Accuracy

Behavioral analysis reduces false positives which are incorrect fraud alerts that mistakenly flag legitimate activities as suspicious. This means fewer disruptions for real customers and more accurate detection of actual fraudulent activities. This precision improves customer experience and operational efficiency, with studies showing a 30% reduction in false positives using advanced analytics.

iii. Real-Time Monitoring

Continuous surveillance means immediate responses to suspicious activities. Real-time monitoring can decrease fraud detection time so that you can act quickly and minimize potential damage.

i.v. Adaptive Learning

Machine learning models in behavioral analysis improve over time, adapting to new fraud tactics. Organizations using machine learning for fraud detection see a 50-90% improvement in detection rates annually.

v. Improved Customer Trust

Strong online security measures build customer confidence and loyalty. A PwC survey found that 85% of consumers are more likely to do business with companies that protect their data effectively.

vi. Regulatory Compliance

Behavioral analysis helps meet regulatory requirements by keeping detailed logs of user activities. This way you can reduce your compliance-related fines and penalties.

Conclusion

Apply the strategies and techniques discussed to protect your assets, and build customer trust. As you implement these measures, constantly consider the evolving nature of fraud. Ask yourself: How can I keep up with advanced fraud tactics?

For a robust solution tailored to your needs, try Chargeflow – the first fully automated dispute and chargeback automation service for eCommerce merchants. Chargeflow uses advanced machine learning and artificial intelligence algorithms to recover chargebacks on autopilot.

‍Start your free trial today and automate your chargebacks efficiently.