As businesses continue to expand and grow, transaction monitoring systems have become a critical component of their operations. These systems help companies track their financial transactions, identify potential risks and fraud, and ensure they stay within legal limits.
To ensure that transaction monitoring systems are as effective as possible, it’s crucial to have a thorough understanding of their limitations. In this article, we’ll explore what transaction monitoring systems are, how they work, and what their limitations are.
How Do Transaction Monitoring Systems Work?
Transaction monitoring system use a combination of rules-based and behaviour-based monitoring to identify suspicious activity. Rules-based monitoring involves setting specific criteria that trigger an alert when met. Transaction monitoring systems use predefined rules and thresholds to identify potentially suspicious activity. If a transaction meets or exceeds a certain threshold amount or violates other predetermined criteria, it may trigger an alert. This can include transactions that are outside a customer’s usual behaviour, transactions with a high-risk country, or transactions involving certain industries that are more prone to fraudulent activity. The alert prompts the system to investigate the transaction further and potentially flag it as suspicious.
Behaviour-based monitoring involves analysing transaction data to detect unusual or abnormal behaviour. Suspicious activity can manifest in different ways, and transaction monitoring systems are designed to detect anomalies that may indicate fraudulent behaviour. One example of such activity is when a customer makes several transactions within a short period. Such activity could be an indication of fraudulent behaviour and would trigger the system to investigate further. Other anomalies could include transactions with unusual patterns or irregularities, transactions with mismatched information, or transactions that exceed normal thresholds. By detecting and analysing these anomalies, businesses can identify potential risks and take appropriate actions to prevent fraud..
Transaction monitoring also uses machine learning algorithms to adapt to changing patterns and behaviours. This enables them to identify new types of fraud or suspicious activity that may not have been detected previously.
Limitations of Transaction Monitoring Systems
While transaction monitoring systems are useful for identifying suspicious activity, they do have limitations. Here are some of the key limitations to be aware of:
a. False Positives: Transaction monitoring systems can generate false positives, which are alerts triggered by legitimate transactions that appear suspicious. The occurrence of false positives can have significant consequences, including wasted time and resources and potential damage to customer relationships. This is because false positives can result in unnecessary investigations, which can take up valuable resources and lead to delays or denials of legitimate transactions. Additionally, customers may become frustrated and dissatisfied if they are repeatedly subjected to additional scrutiny and denied legitimate transactions. Therefore, it’s crucial to minimise false positives as much as possible to maintain customer trust and ensure efficient business operations.
- Limited Data Sources: Transaction monitoring systems rely on data from internal sources, such as transaction data and customer information. They may not have access to external data sources, such as social media activity, which can be useful in identifying fraudulent activity.
- Lack of Context: Transaction monitoring systems can only analyse transaction data and may not have access to additional context such as customer behaviour or account history..
To address this limitation, businesses can incorporate more data sources and context into their transaction monitoring systems. For example, by analysing a customer’s transaction history, businesses can identify patterns and behaviours that are normal for that customer and detect any deviations that could indicate fraudulent activity.
- Inability to Detect Insider Fraud: Transaction monitoring systems may not be able to detect insider fraud, which occurs when an employee within the organisation commits fraud. The detection of insider fraud is frequently challenging as the employee holds sensitive information and could potentially conceal any evidence of their wrongdoing. To address this limitation, businesses can implement additional controls and oversight to prevent insider fraud. To mitigate the risks associated with transaction monitoring limitations, businesses can adopt several risk management strategies. These may include measures such as establishing strict internal controls and procedures, conducting regular risk assessments, and implementing advanced fraud detection technologies. By having a robust risk management framework in place, businesses can detect potential fraud and protect themselves from financial and reputational damage. In addition, businesses can take steps to train their employees on fraud detection and prevention techniques, and regularly monitor and review their internal controls to ensure they remain effective in mitigating risks. By taking a proactive approach to risk management, businesses can significantly reduce the chances of fraudulent activity and maintain the trust of their customers. Additionally, businesses can implement whistleblower hotlines to encourage employees to report any suspicious activity.
Conclusion
Transaction monitoring systems are critical tools for businesses to identify suspicious activity and stay within legal transaction limits. However, these systems do have limitations that businesses need to be aware of to maximise their effectiveness.
By understanding the limitations of transaction monitoring systems, businesses can take steps to address them and ensure that their systems are operating effectively. This includes incorporating more data sources and context, refining system rules and thresholds, implementing additional controls and oversight, and leveraging human expertise to investigate alerts thoroughly.
