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Digital Fraud Wiki

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Transaction Monitoring for Fraud Detection

What is transaction monitoring?

Transaction monitoring is the continuous, automated analysis of financial transactions by a fraud prevention system to detect and flag suspicious, potentially fraudulent activity for manual review. In fraud prevention, it’s critical to catching transaction fraud and other illicit activities. Detecting these transaction anomalies is not only a standard practice across the financial industry, it’s also required by anti-money laundering (AML) regulations.

But transaction monitoring software has many uses beyond AML compliance. This versatility comes from its core capabilities, which include:

  • Pattern recognition – predefined rules to identify patterns and behaviors that indicate fraud.
  • Anomaly detection – flagging transactions that strongly deviate from a customer’s normal behavior.
  • Real-time monitoring – immediate alerts when the system detects a suspicious transaction.

What is AML transaction monitoring?

AML transaction monitoring is a mandated part of any AML program. Financial institutions (FIs) worldwide must have AML programs that reliably detect and report transactions indicating fraud, money laundering, or terrorist financing.

In AML transaction monitoring, any flagged transactions trigger a suspicious activity report (SAR).

Learn more about AML transaction monitoring and how it catches money launderers.

How does transaction monitoring work in fraud prevention?

Transaction monitoring in fraud prevention works by leveraging automated systems using a combination of predefined rules, statistical models, machine learning algorithms, and historical data to identify patterns of behavior indicative of potential fraud. There are a few notable components of this process.

1. Rule-Based Systems

Transaction monitoring systems are set up with a set of predefined rules based on known patterns of fraudulent behavior. These rules can include limits on transaction amounts, frequency thresholds, and geographic restrictions.

When a transaction violates one or more of these rules, an alert is triggered. For example, if a customer suddenly makes an unusually large transaction or if there are multiple transactions within a short time frame, it may raise suspicion.

2. Pattern Recognition

The system builds models of normal behavior for each customer by analyzing historical transaction data. This includes the typical transaction amounts, locations, times, and other relevant parameters.

Deviations from established patterns are flagged as anomalies. For instance, if a customer who usually makes small, local transactions suddenly initiates a large international transfer, it might trip synthetic transaction monitoring systems and trigger an alert.

3. Risk Scoring

Each transaction is assigned a risk score based on various factors, such as the customer’s historical behavior, the type of transaction, and the relationship between the parties involved.

Transactions that exceed certain risk thresholds are flagged for further investigation.

4. Machine Learning and AI

Advanced transaction monitoring software incorporates machine learning and artificial intelligence to adapt and learn from new data. These systems can dynamically adjust their models and rules based on emerging fraud patterns.

Machine learning enables the system to evolve and improve its accuracy over time, allowing it to stay effective against new and sophisticated fraud techniques.

5. Alert Generation and Investigation

When a transaction is flagged as suspicious, an alert is generated and sent to fraud analysts for further investigation.

Analysts review the alerts, gathering additional information as needed. They may contact the customer for verification or take other steps to assess the legitimacy of the transaction.

6. Regulatory Compliance

Transaction monitoring software are designed to help financial institutions comply with anti-money laundering (AML) regulations and Know Your Customer (KYC) requirements by identifying and reporting suspicious activities.

What can transaction monitoring detect?

Transaction monitoring systems detect behaviors that reveal larger bank frauds. This includes many of the most common fraud attack types. Here are a few examples and how transaction monitoring detects them.

Money Laundering, Smurfing, and Money Mules

Transaction monitoring doesn’t spot money laundering alone. It can reveal related schemes too, like smurfing and money mules. Smurfing involves breaking down large transactions into smaller amounts to launder money undetected. Transaction monitoring finds patterns and connects the smurfs together.

Large cash deposits and frequent transfers, especially to high-risk countries, are signs of money mules. Transaction monitoring can flag these transactions for further scrutiny.

Account Takeover and Synthetic Identity

If a customer’s account is compromised, transaction monitoring can flag unauthorized activities.

It does this by spotting sudden changes in account behavior which indicate an account takeover. Transaction monitoring also spots shared attributes and identity consistencies that reveal fake identities.

Card fraud

For credit and debit cards, transaction monitoring can detect abnormal usage patterns. Multiple transactions in different locations within a short period indicate potential card cloning.

Social engineering scams

Phishing and other social engineering scams are among the most common fraud attacks. By monitoring transactions, FIs can uncover fraud patterns that deviate from normal behavior. Once alerted, investigators can connect the dots and spot the attacks.

Transaction monitoring and AML

Transaction monitoring and Anti-Money Laundering (AML) are closely related components within the broader framework of financial crime prevention in the banking and financial services sector. AML refers to the set of regulations, policies, and procedures designed to detect and prevent money laundering, terrorist financing, and other illicit financial activities. Transaction monitoring is a key operational aspect of AML efforts.

AML transaction monitoring rules

Anti-Money Laundering (AML) transaction monitoring rules are predefined criteria or conditions that financial institutions use to analyze and assess customer transactions for potential money laundering or other illicit activities.

Read our full wiki page on anti-money laundering to learn more about how transaction monitoring functions in AML specifically.

Is transaction monitoring used outside AML?

Yes, transaction monitoring is very useful outside of AML. The principles and techniques of transaction monitoring adapt well to catch fraud in many other domains.

Transaction monitoring is a foundational piece of fraud prevention and case management. FIs and lending companies also use it to assess customer creditworthiness.

Cybersecurity, a close relative of fraud prevention, leverages it to reveal patterns of cyberattacks. This is especially relevant in preventing data breaches and safeguarding sensitive information.

Retailers and e-commerce platforms use transaction monitoring to establish baseline purchasing patterns. Deviations like bulk purchases of high-value items or many transactions from different locations signal fraud.

What are the benefits of anti-fraud transaction monitoring?

The entire fraud team benefits from a strong transaction monitoring solution. It quickly identifies unusual or suspicious patterns in transaction data. That early detection lets fraud teams intervene before attacks escalate, minimizing potential losses.

Modern transaction monitoring systems can generate real-time alerts when they detect suspicious transactions. In the era of instant payments, that capability is crucial for fraud teams to act fast and stop fraud.

Best in class systems can minimize false positives by refining rules and algorithms. This helps fraud teams focus on genuine cases, improving their efficiency and effectiveness.

The data insights that transaction monitoring generates about fraud trends, customer behavior, and vulnerabilities are invaluable. Fraud teams can analyze this data to refine their strategies, enhance training, and improve fraud prevention.

Is transaction screening different from transaction monitoring?

Yes, transaction screening is different from transaction monitoring. Both are related concepts, but they serve different purposes.

Transaction screening checks individual transactions against predefined watchlists, sanctions lists, or risk databases. This is more closely related AML as it uncovers transactions that signal money laundering.

Transaction monitoring continuously analyzes transactional data to detect unusual patterns. That’s why it has uses beyond AML and can detect an array of other financial frauds.

Cryptocurrency transaction monitoring

Conducting cryptocurrency transaction monitoring is an essential aspect of risk management, compliance, and fraud prevention in the cryptocurrency space. While cryptocurrencies are known for their pseudonymous nature, there are methods and tools available to monitor and analyze transactions to detect suspicious activities. Here are some common approaches to conducting crypto transaction monitoring:

Blockchain Analysis Tools

  • Transaction Tracking – Blockchain explorers and analysis tools allow users to track transactions on public blockchains. These tools provide visibility into the movement of funds and can help identify patterns or anomalies.
  • Address Clustering – Some tools use algorithms to cluster addresses belonging to the same user, providing a more comprehensive view of their transaction history.

Transaction Pattern Analysis

  • Unusual Transaction Volumes – Monitor for large or rapid changes in transaction volumes, as these could indicate potential fraudulent or illicit activities.
  •  Irregular Time Patterns – Analyze the timing of transactions to identify any unusual or suspicious patterns, such as transactions occurring at odd hours.

Address Monitoring

  • Watchlists – Maintain watchlists of addresses associated with illicit activities, known hacks, or sanctioned entities. Regularly check transactions against these watchlists.
  • Address Risk Scoring – Assign risk scores to addresses based on their historical involvement in suspicious activities.

Anomaly Detection

  • Deviation from Normal Behavior – Utilize algorithms to identify deviations from normal transaction behavior for specific users or addresses.
  • Automated Alerts – Set up automated alerts for transactions that exceed predefined thresholds or exhibit unusual characteristics.

Machine Learning and AI

  • Behavioral Analysis – Implement machine learning algorithms to analyze transactional behavior and detect anomalies or patterns associated with fraudulent activities.
  • Adaptive Models – Use adaptive models that can evolve and learn from new data, allowing for the detection of emerging threats.

It’s important to note that the effectiveness of crypto transaction monitoring relies on the integration of these methods and tools, often in conjunction with regulatory compliance measures. Given the dynamic nature of the cryptocurrency landscape, continuous improvement and adaptation of monitoring strategies are essential to stay ahead of evolving risks and threats. Additionally, collaboration with industry partners and sharing threat intelligence can enhance the overall effectiveness of crypto transaction monitoring efforts.

How does machine learning work for transaction monitoring?

Machine learning forms the backbone of any modern fraud prevention platform. As you’d expect, that means it fuels core aspects of transaction monitoring.

Transaction monitoring collects data from payment gateways and other online platforms. This data is often noisy and unstructured, so preprocessing is necessary to clean and organize it for analysis.

Machine learning models use features, or specific attributes of transactions, to make predictions. This feature engineering involves selecting, transforming, and creating relevant features from raw data. Features can be transaction amounts, time of day, location, account history, and more.

This way, it can automate detection and adapt to evolving fraud tactics. With ML transaction monitoring, organizations can analyze vast amounts of data quickly. Then, teams can make more informed decisions in real-time.

DataVisor’s industry-leading transaction monitoring platform utilizes both supervised and unsupervised machine learning. To learn how this delivers hyper-accurate fraud detection in real-time, schedule a chat with our team.