Rules-Based vs Machine Learning Models in Transaction Monitoring Systems
Rules-Based vs Machine Learning Models in Transaction Monitoring Systems
The fundamental difference between Rules-Based and Machine Learning Models for conducting Transaction Monitoring is that a rules based monitoring system relies on pre-defined criteria and thresholds, whereas a machine learning-based transaction monitoring system relies on the tool’s ability to proactively identify, decipher, and analyse data fed into the system.
The Anti-Money Laundering (AML) and Counter-Financing of Terrorism (CFT) laws and regulations require businesses to implement a transaction monitoring system which scans every single transaction for whether there is any evidence for potential engagement in Money Laundering (ML), Terrorism Financing (TF), or Proliferation Financing (PF). With advancements in technology, businesses now have the option to choose between rule-based monitoring and machine-learning models for their transaction monitoring processes. Let’s understand the meaning, pros, and cons of these approaches.
Rule-Based Monitoring
A rules-based monitoring system operates on predefined criteria and thresholds established by regulatory guidelines and internal AML policies. The transaction monitoring system functions with a straightforward approach to compliance. Here are some pros and cons of rule-based transaction monitoring:
Pros of Rule-Based Monitoring
- A rule-based transaction monitoring system flags and alerts on every transaction exceeding the set threshold, ensuring no significant activity is overlooked. It monitors and alerts on every transaction frequency inconsistent with parameters set for customer profile type, highlighting any anomalies. Further, a rule-based monitoring software Identifies and alerts on every transaction routed to or through blacklisted or grey-listed jurisdictions, enhancing compliance for businesses.
- A rule-based transaction monitoring software helps achieve 100% compliance and accuracy for regulatory reporting requirements, thereby reducing legal risks.
- In the case of rule-based transaction monitoring tools, rules can be tweaked and fine-tuned by manual adjustments, allowing businesses to respond to evolving risks. Further, it employs straightforward implementation without requiring complex data and workflow management, making it easy for staff to use. It is based on clear and transparent rules, providing accountability and clarity in monitoring processes.
- A rule-based transaction monitoring system has proven effective for standard scenarios, ensuring reliability. It facilitates quick detection of straightforward violations, reducing the financial risk associated with fines and penalties due to non-compliance and fraudulent activities.
Cons of Rule-Based Monitoring
- Rules-based transaction monitoring needs to be manually updated every single time when needed, which can be resource-intensive.
- A rule-based monitoring system primarily identifies issues after they occur, making it reactive in nature, which may allow suspicious activity to go unnoticed initially.
- With rule-based transaction monitoring software, rules can be reverse-engineered, which can be exploited by those seeking to evade detection.
- Rules-based monitoring systems may inadvertently reflect biases present in the rule-setting process.
- Rules-based systems tend to generate numerous false alerts, leading to resource strain and potential desensitisation.
- Rules-based transaction monitoring systems have limited ability to adapt to emerging and sophisticated patterns of suspicious activities, leaving the business open to financial crime threats, including ML, FT, and PF.
- Rules-based transaction monitoring software is rigid and may miss hidden risks due to a fixed rule structure, which makes compliance ineffective.
Transaction Monitoring through Machine Learning Model
A machine learning-based transaction monitoring system with advanced analytics can be leveraged as part of AML compliance technology. Let’s discuss the pros and cons of transaction monitoring, which utilises a machine learning model.
Pros of Transaction Monitoring through Machine Learning Model
- Machine Learning-based transaction monitoring systems come with features like proactive identification that can decipher hidden behavioural patterns and complex and interdependent data, allowing for earlier detection of suspicious activities.
- Machine Learning-based transaction monitoring software supports automatic re-tuning and fine-tuning of historical data, resulting in improved accuracy and efficiency over time.
- Machine learning-based transaction monitoring software is massively scalable to handle increasing transaction volumes and complexities. Further, it requires minimal human intervention, saving time and costs.
- Machine learning-based models can obscure the decision-making process and are difficult to reverse engineer, which enhances the security and privacy features.
- Machine learning-based transaction monitoring tools are capable of learning from historical data and picking out algorithms, reducing the incidence of false positives.
Cons of Transaction Monitoring through Machine Learning Model
- Machine Learning models require extensive and high-quality training data points to devise a decision-making system and function effectively.
- Implementing and maintaining models necessitates skilled experts, which can be expensive and costly for businesses.
- The setup and ongoing management of machine learning models are complex to implement and require significant technical expertise.
- Machine learning-based transaction monitoring systems often lack clear transparency and explainability, which can complicate regulatory compliance and stakeholder trust.
- Machine learning-based transaction monitoring software is resource-intensive regarding computing power, and costs can be prohibitive.
- Regulatory and compliance concerns about machine learning-based model validation and effectiveness can be challenging.
Conclusion
Identification and verification are significant in AML laws and regulations. Leveraging identity verification APIs for AML compliance is a strategic move that can help DNFBPs implement effective identification and verification processes and make informed compliance decisions.