Transaction Monitoring for Fraud and Payments

Fraud transaction monitoring involves the systematic and continuous review and analysis of financial transactions to identify suspicious behaviors, patterns or anomalies that may indicate fraudulent activity or other illicit intentions. The objective is to scrutinize, detect and prevent unauthorized and illegitimate transactions that result in financial losses.

The focus for payment transaction monitoring is slightly adjacent. It ensures smooth processing and compliance for legitimate payments, primarily verifying transaction details, validating customer credentials, screening against sanctions and watchlists, and ensuring compliance with regulations. This monitoring occurs before a transaction is approved or processed and aims to facilitate legitimate transactions while identifying potential regulatory risks.

Fraud transaction monitoring focuses on detecting criminal activities, while payment transaction monitoring ensures regulatory compliance and proper processing. Both overlap and contribute to an organization’s risk management and compliance efforts.

Fraud Monitoring Versus Fraud Transaction Monitoring

The difference between fraud monitoring and fraud transaction monitoring lies in the scope and focus of the monitoring activities. Fraud monitoring is a broader term encompassing the overall detection and prevention of fraudulent activities within an organization. These may include various types of fraud, such as identity theft, account takeover, payment fraud and application fraud. Fraud transaction monitoring’s scope is narrow. It focuses on monitoring transactions to identify and detect fraudulent activities related to money movement.

These types of monitoring contribute to a robust fraud prevention strategy, with fraud monitoring providing a more comprehensive defense mechanism across the entire customer journey and fraud transaction monitoring offering targeted protection for transactions.

How Fraud Transaction Monitoring Works

Fraud transaction monitoring involves a systematic process to detect and prevent fraudulent and illicit transactions. The process typically follows these steps:

  • Data Collection: The transaction monitoring system captures transaction data as it occurs, including details like transaction data (amount, location, time and account information), device data, and IP addresses, among other sources. 
  • Analysis: Advanced algorithms and machine learning models analyze collected data and compare it against predefined rules, historical patterns, and known fraud indicators.
  • Risk Scoring: Transactions are assigned risk scores based on various factors such as transaction amounts, geographic locations and user behavior
  • Anomaly Detection: The system identifies transactions that deviate from standard patterns or exceed predefined thresholds
  • Alert Generation: When suspicious activity is detected, the system generates alerts for further investigation
  • Investigation: Fraud analysts review flagged transactions to determine if they are indeed fraudulent
  • Action Taken: Based on the investigation, appropriate actions are taken – such as blocking transactions, contacting customers or reporting to authorities

Features of Fraud Transaction Monitoring

These features enhance the ability of businesses to swiftly identify and respond to suspicious transactions and illicit activities, thereby safeguarding the integrity of the financial system. Key aspects include:

  • Real-Time Analysis: Transaction monitoring for fraud involves automated real-time surveillance of transactions, including the examination of transaction details, patterns, and contexts to detect anomalies and flag suspicious activities that deviate from normal behavior as they occur to catch fraud attempts at speed.
  • Behavioral Analysis: Transaction monitoring can identify deviations from established customer behaviors by analyzing current transactions against historical data. It uses predefined rules and thresholds, such as sudden large transactions, frequent small transactions or transactions to high-risk regions. 
  • Machine Learning: Using advanced technologies, such as artificial intelligence (AI) and machine learning (ML), sophisticated patterns and potential fraud can be predicted and stopped. In addition, these technologies adapt to new fraud patterns and improve detection accuracy over time. 
  • Customer Risk Profiling: Creating detailed risk profiles for customers helps tailor monitoring efforts effectively, allowing organizations to focus on high-risk individuals and transactions.
  • Alert Generation: Assigning risk scores to transactions based on factors like amount, location, frequency, and customer history helps prioritize monitoring efforts and manage potential threats.
  • Continuous Improvement: Incorporating feedback from investigations and regularly updating transaction monitoring rules and algorithms – including adding transaction labels for machine learning models to apply feedback –  enables companies to adapt to emerging fraud tactics and continuously enhance their monitoring systems>  improve monitoring systems.
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Benefits of Fraud Transaction Monitoring

For businesses and financial institutions, fraud transaction monitoring delivers numerous advantages. By quickly flagging suspicious transactions in real time, organizations can immediately stop fraud before significant damage is caused – minimizing losses and protecting customers’ assets. Reducing financial crimes helps companies avoid substantial expenses related to investigating and rectifying them. Moreover, monitoring enhances customer trust and regulatory compliance, further solidifying a company’s reputation and operational integrity.

Transaction Monitoring in Payments

Payment transaction monitoring ensures payment systems’ integrity, security and efficiency in facilitating legitimate transactions. In payments, transaction monitoring aims to improve detection rates across fraud, AML and payments by:

  • Payment Screening
    Vetting payments against sanctions and watchlists before they enter the system to help prevent any fraudulent transactions from being processed
  • Cross-Border Transaction Monitoring
    Scrutinizing international payments for compliance with regulations and potential risks ensures that cross-border transactions are legitimate and secure
  • Volume and Velocity Monitoring
    Detecting unusual spikes in transaction volume or frequency that can indicate potential fraudulent activities or system abuses
  • Beneficiary Analysis
    Monitoring patterns in payment recipients helps identify potential shell companies or suspicious entities that can reduce the risk of fraud and money laundering
  • Payment Type Monitorins
    Applying specific rules based on different payment methods (e.g., wire transfers, ACH, mobile payments) can ensure that each payment type is monitored appropriately for unique risks

The Importance of Securing Payment Methods and Channels

Securing various payment methods and channels is essential for maintaining trust and integrity in the financial system. As payment technologies advance, robust security measures across multiple platforms are crucial to protect against sophisticated threats and ensure seamless, legitimate transactions. Advanced features of payment transaction monitoring include:

  • Multi-Channel Monitoring: Multi-channel monitoring systems track and analyze transactions across ATMs, mobile devices, and contactless payments, ensuring consistent security measures and protection against fraud across all channels.
  • Real-Time Decisioning: Advanced transaction monitoring services can make quick, real-time decisions about allowing or blocking transactions. This capability ensures a seamless experience for legitimate customers while effectively preventing fraudulent activities, thus maintaining the integrity of financial transactions.
  • Sanctions Screening: Sanctions screening involves checking transactions against sanctions lists, watchlists and other regulatory lists. Transaction monitoring systems often include this feature to prevent dealings with restricted entities, ensure compliance with regulatory requirements and reduce financial crime risk.
  • Adaptive Models: Machine learning models in transaction monitoring systems adapt to new fraud patterns and evolving payment technologies, continuously improving detection accuracy to stay ahead of emerging threats and maintain robust security.

Fraud Transaction Monitoring in Banking

Digital banks and financial institutions face unique fraud monitoring challenges, primarily within account monitoring, which involves several key components:

  • Continuous Transaction Monitoring: Financial institutions employ systems to monitor transactions in real-time online and in-app banking sessions, serving two crucial purposes: fraud prevention and regulatory compliance. For fraud prevention, these systems detect unusual patterns or suspicious activities indicative of fraudulent transactions. For regulatory compliance, they meet AML requirements by monitoring transactions above certain thresholds.
  • Account Activity Surveillance: Beyond transactions, banks monitor various account-related activities to prevent fraud, including address changes, adding or modifying beneficiaries, new card issuance requests and loan applications.
  • Enhanced Identity Verification: Financial institutions strengthen fraud prevention by using identity verification processes such as Know Your Customer (KYC) checks, Know Your Business (KYB) checks, AML screening, regular Customer Due Diligence (CDD), Enhanced Due Diligence (EDD) and multi-factor authentication.

What’s the Role of Machine Learning in Fraud Monitoring?

Machine learning systems analyze historical transaction monitoring data to suggest new risk rules and patterns that human analysts might overlook. Trained on existing fraud cases, these systems can identify subtle patterns and anomalies indicative of fraudulent activity, enhancing the accuracy and efficiency of fraud detection.

For organizations handling vast amounts of transactions and numerous attack vectors, machine learning has become essential to process millions of data points simultaneously. This supports the real-time detection of complex fraud schemes that traditional methods and manual oversight might miss.

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SEON’s Approach

SEON’s real-time transaction monitoring solution provides advanced, integrated approaches to fraud detection and payment security. By leveraging cutting-edge technologies such as machine learning and AI, SEON’s fraud prevention platform enhances detection accuracy and adapts to emerging threats. While other fraud prevention solutions might only focus on transaction monitoring, SEON safeguards the entire customer journey from onboarding, login, activity, and transactions, providing a holistic approach to fraud prevention.

With multi-channel monitoring, real-time decisions, and comprehensive sanctions screening to ensure robust protection across various transaction platforms, SEON integrates proprietary data with a business’s data to inform decision-making processes across every transaction.

Frequently Asked Questions

What are the challenged of fraud monitoring?

Modern transaction monitoring solutions are crucial for businesses that process millions of transactions daily to analyze all data in real time. Some systems are overly sensitive and generate too many false alarms, leading to alert fatigue or overlooking genuine fraud cases.

What is fraud risk monitoring?

Normally used as a synonym of fraud monitoring, fraud risk monitoring highlights the risk management aspect of this process, sometimes being an integral part of an organization’s risk management software and/or strategy.

Why is fraud transaction and payment monitoring important?

Fraud costs the global economy over $5 trillion annually. Fraud and payment transaction monitoring safeguard the financial system, maintain consumer and business trust, and prevent significant financial losses by quickly detecting unauthorized transactions. Effective monitoring also ensures compliance with regulations, helping financial institutions avoid legal penalties and reputational damage while maintaining customer confidence in secure operations.

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Author avatar
Bence Jendruszak

Bence Jendruszák is the Chief Operating Officer and co-founder of SEON. Thanks to his leadership, the company received the biggest Series A in Hungarian history in 2021. Bence is passionate about cybersecurity and its overlap with business success. You can find him leading webinars with industry leaders on topics such as iGaming fraud, identity proofing or machine learning (when he’s not brewing questionable coffee for his colleagues).