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Transaction & Fraud Monitoring

At Hello Clever, we prioritise the security of every transaction, balancing a smooth user experience with strict fraud prevention. Our monitoring system combines rule-based and machine learning (ML)–based detection to prevent fraud, with processes in place for real-time and post-payment screening. Through our partnership with Flagright, we strengthen these capabilities with advanced fraud prevention tools and threat intelligence.

Here’s an overview of our approach to transaction and fraud monitoring.

Smart Monitoring: Detecting Suspicious Activity​

Hello Clever’s transaction monitoring uses rules and ML to flag suspicious activity in real time. Our rules are carefully crafted to catch fraud without interrupting legitimate users. By tuning each rule, we strike the perfect balance between security and usability.

Relevant Signals for Fraud Detection​

Our rule-based monitoring system looks for the following signals that may indicate fraudulent behaviour. Here’s how we strike the balance between user-friendly transactions and vigilance.

Unusual Transaction Amounts​

Transactions that are unusually high or low compared to a user’s typical spending patterns may trigger an alert.

Example: A user who typically makes $50 purchases suddenly attempts a $2,000 transaction. This raises a flag in our system, as it deviates significantly from their usual behaviour. However, if the user has a history of gradual spending increases, the system recognises this and adjusts its sensitivity, allowing legitimate spending growth without unnecessary friction.

Geolocation and IP Address Mismatch​

Transactions originating from unexpected or high-risk locations are carefully monitored, with flexible rules to minimise impact on genuine travellers.

Example: A customer who usually transacts in Sydney makes a purchase from Moscow. Our system detects this change in location and flags it for review, knowing that a legitimate customer could simply be traveling. To balance usability, our rules account for recent travel signals, such as airline purchase history or notifications of upcoming trips, ensuring only high-risk location mismatches are flagged.

Device Anomalies​

Our system monitors the device used for transactions. A sudden shift in device, browser, or operating system can signal potential fraud.

Example: A user who usually transacts from an iPhone suddenly initiates a transaction from an unfamiliar device and operating system. Our system is trained to flag such changes, but it also recognises device upgrades and syncs with the user’s account settings to avoid interruptions. Legitimate changes in device don’t stop transactions, while suspicious ones receive added scrutiny.

Velocity of Transactions​

High-frequency transactions within a short period are flagged, as this can be a sign of automated scripts or malicious behaviour. However, our system is flexible, ensuring it doesn’t disrupt users with valid high-frequency transactions.

Example: A user makes five transactions in under a minute, triggering an alert for excessive activity. The system checks if this pattern is new or part of an established behaviour, such as online shopping during a sale event. If consistent with past behaviour, the system lets it through. If it’s unusual, the transactions are flagged for review.

Behavioural Patterns​

Our monitoring captures deviations in spending patterns. If a user who consistently spends on groceries and utilities suddenly makes multiple large luxury purchases, it’s flagged.

Example: A customer with a steady, predictable pattern of daily grocery purchases suddenly makes two high-value transactions on designer goods. This triggers a closer look, as it falls outside their usual behaviour. However, our rules also check for recent salary deposits or seasonal spending spikes, adjusting the detection sensitivity to balance security with flexibility.

Multiple Failed Payment Attempts​

Repeated failed attempts within a short time frame may indicate card testing or account takeovers.

Example: A user tries multiple failed transactions in succession, raising a potential flag for card testing. If the failures continue from different devices or locations, our system escalates the case. However, if the same device reattempts with minor changes, such as adjusting an expiration date or retrying after a network disruption, the system recognises it as legitimate and avoids blocking the user unnecessarily.

Real-World Scenarios for Rule Application​

Here are a few ways Hello Clever applies these rules while keeping user experience top of mind:

  • High-Risk Geolocation with Real-Time Adaptation: A user frequently transacting in Melbourne suddenly purchases from an overseas location. Instead of blocking it immediately, the system checks recent travel activity, such as ticket purchases, and uses intelligent risk scoring to weigh the risk. This allows legitimate transactions from travellers while blocking true threats.
  • Increased Transaction Velocity with Seasonal Adjustments: During high shopping seasons, such as Black Friday, users may make quick, consecutive purchases. Our system adjusts for seasonal trends, allowing more flexible transaction frequencies while maintaining an alert for excessive or unusual activity patterns.
  • Device Change with Flexible Monitoring: Our system recognises common device changes, like moving from a mobile phone to a desktop computer. If both devices have been used previously by the customer, the system permits the change smoothly. However, an entirely new and unrecognised device prompts a check for verification.

Through carefully balanced rules, Hello Clever ensures a seamless customer experience while vigilantly monitoring for fraud.

Internal Process: Automated Blocking and Compliance Review​

Our transaction monitoring system includes automated responses to certain high-risk activities, while other cases are escalated for review by our compliance team.

  • Automated Blocking: For transactions that meet specific high-risk criteria, such as a known fraud pattern or a flagged geolocation, the system can automatically block the transaction in real time, preventing further action.
  • Case Creation and Compliance Review: For transactions that don’t warrant immediate blocking but show unusual patterns, a case is created in our system and sent to our compliance manager for further review. Our compliance team analyses the transaction details and determines if any additional actions, such as customer verification, are needed.
  • Customer Notification: In cases where further verification is required, customers may be contacted to confirm their identity, ensuring a smooth and secure transaction experience.

This internal review process balances automated security with human oversight, ensuring accurate detection and appropriate action for each transaction.

Regular Post-Payment Screening​

In addition to real-time monitoring, Hello Clever conducts regular post-payment screening to identify potentially suspicious transactions after they are completed. Post-payment screening is crucial for catching fraudulent activity that may not be immediately apparent during the transaction.

  • Ongoing Behavioural Analysis: Transactions are reviewed for patterns that may emerge over time, such as repeated low-risk behaviours that may collectively indicate risk.
  • Retrospective Review of High-Risk Transactions: Transactions previously marked as high-risk are periodically reviewed to identify potential links to fraud rings or coordinated attacks.
  • Enhanced Machine Learning Models: By incorporating post-payment data, our ML models continuously improve, refining our fraud detection capabilities to better identify evolving fraud patterns.

Regular post-payment screening allows us to catch additional risks and maintain a proactive stance on transaction security.

Enhanced Fraud Detection with Flagright Partnership​

To further strengthen our fraud detection, Hello Clever has partnered with Flagright, a leading provider of fraud prevention and transaction monitoring solutions. Flagright’s advanced monitoring tools work seamlessly with our own fraud detection system, providing enhanced capabilities and real-time insights.

  • Enhanced Pattern Recognition: Flagright’s technology helps identify subtle patterns and correlations that may otherwise go undetected, allowing us to spot and prevent sophisticated fraud schemes.
  • Real-Time Threat Intelligence: Flagright provides up-to-date intelligence on emerging fraud trends, helping us stay ahead of potential threats and adjust our detection criteria accordingly.
  • Customisable Rule Sets: With Flagright’s support, we can refine our rule-based monitoring to reflect the latest fraud tactics, creating a more adaptable and effective fraud prevention system.

Our partnership with Flagright gives us the tools to detect fraud more accurately and respond faster, enhancing overall transaction security for Hello Clever’s payment gateway.

How Hello Clever’s Approach Keeps Your Transactions Secure​

Hello Clever’s transaction monitoring system combines rule-based detection, machine learning, compliance review, post-payment screening, and continuous enhancement through our Flagright partnership to provide comprehensive fraud protection.

  • Real-Time, Rule-Based and ML Detection: Predefined rules and machine learning models allow our system to detect suspicious patterns and behaviours immediately, identifying high-risk transactions before they’re completed.
  • Automated Blocking and Manual Review: High-risk transactions are either automatically blocked or sent to our compliance team for additional review, ensuring swift and accurate responses.
  • Proactive Post-Payment Screening: Ongoing analysis of completed transactions helps us detect hidden fraud patterns and refine our security processes.
  • Advanced Monitoring with Flagright: Our partnership with Flagright enhances our fraud detection capabilities, allowing us to respond proactively to emerging fraud trends.

Hello Clever’s transaction and fraud monitoring system helps ensure that every transaction is secure, keeping your business protected from fraud. For more information on our transaction security, feel free to reach out to our support team or explore our API Reference for secure integration details.