Combining Scenario-Based and AI-Based Monitoring for Holistic Transaction Monitoring

Transaction monitoring is an essential part of anti-money laundering (AML) and financial crime prevention efforts. As financial institutions process trillions of dollars daily, effective monitoring systems help to identify suspicious activities and protect the financial ecosystem. 

However, traditional scenario-based monitoring systems are inefficient and costly, generating up to 90% false positives! To overcome these limitations, institutions are now adopting a modern combined approach that integrates scenario-based and AI-driven monitoring strategies.

This hybrid model combines the stability of more traditional scenario-based systems with the adaptability of AI, enabling a more comprehensive transaction monitoring system. Let’s explore this approach in detail.

The Role of Scenario-Based Monitoring

Scenario-based monitoring has been a foundational method in transaction monitoring, particularly for detecting known financial crime patterns. Built on predefined scenarios, this approach flags transactions based on specific criteria—such as transaction amounts, frequencies, and geographic locations—that align with established risk factors and regulatory mandates. 

This method’s strength lies in its predictability and regulatory compliance, which gives institutions a structured framework to catch commonly recognized suspicious activities.

Key Features of Scenario-Based Monitoring:

1. Custom Scenarios: Financial institutions can tailor scenarios based on specific transaction behaviors, adapting to unique operational needs. For example, they may set higher scrutiny for high-value transactions involving certain jurisdictions, which aligns with known financial crime tactics.
2. Regulatory Compliance: Scenario-based monitoring facilitates adherence to compliance requirements, as it flags transactions that deviate from standard customer behavior or known risk patterns.
3. Historical Backtesting: Scenario-based systems can test scenarios against past transaction data, providing a way to refine scenarios and reduce false positives over time.

However, this approach has some limitations. Its reliance on static scenarios often results in high false positive rates, as it struggles to adapt to the rapid changing of criminal tactics. As financial crime techniques grow more sophisticated, scenario-based monitoring alone may miss new and complex threats, leading to alert fatigue within compliance teams.

AI-Based Transaction Monitoring: A Game Changer

AI-based transaction monitoring introduces an adaptive and dynamic approach that can complement the strengths of scenario-based systems while addressing their limitations. By employing machine learning algorithms, AI can analyze historical data, detect patterns, and even identify previously unknown anomalies. This adaptability allows AI to reduce false positives and keep pace with emerging threats.

Benefits of AI-Based Monitoring:

1. Reduction in False Positives: AI can adjust risk thresholds dynamically based on previous investigations, reducing the volume of unnecessary alerts that compliance teams must review.
2. Real-Time Detection: AI systems can process high volumes of transaction data in real-time, enabling faster identification of suspicious behavior and reducing response times.
3. Anomaly Detection: AI models excel at identifying irregularities by comparing transactions against baseline patterns of customer behavior, flagging activity that falls outside these norms. This is especially valuable for catching unconventional or novel forms of fraud.

Scenario-based approaches provide improved standardization and compliance while AI provides more nuanced insights. Together, they can enhance your ability to identify suspicious activity promptly and accurately. Let us discuss this combined approach in more detail.

Combining Scenario-Based and AI-Based Monitoring for a Holistic Approach

Merging scenario-based monitoring with AI-driven tools provides a balanced transaction monitoring solution, leveraging the strengths of both methods to address each other’s limitations. This hybrid approach allows financial institutions to detect known risks while adapting to emerging threats, resulting in a more robust system for combating financial crime. Here are the advantages of a hybrid approach:

1. Enhanced Detection Accuracy: Combining predefined scenarios with machine learning models enables institutions to catch both conventional and novel threats. Static scenarios capture established suspicious patterns, while AI’s adaptable learning capabilities spot less obvious anomalies, boosting detection rates.
2. Reduced False Positives: By adjusting risk thresholds dynamically, AI reduces the volume of false positives that scenario-based systems tend to generate. This minimizes alert fatigue, enabling compliance teams to focus on genuine threats and optimize their resources.
3. Scalability: As transaction volumes grow, AI’s processing capabilities make it easier to scale up without sacrificing performance. This flexibility is critical as financial institutions handle increasingly large datasets, ensuring that compliance efforts remain efficient and effective.
4. Real-Time Monitoring: Scenario-based monitoring often functions in batches, meaning there can be delays in flagging transactions. AI-powered tools, however, operate in real-time, allowing institutions to act immediately on suspicious activities.

Lucinity’s platform exemplifies this hybrid approach by integrating customizable scenario-based monitoring with external AI-powered tools such as Resistant AI for behavioral analytics.

How Lucinity Supports Holistic Transaction Monitoring

Lucinity’s platform offers a powerful, hybrid approach to transaction monitoring, combining configurable scenario-based monitoring with partnerships for AI-driven detection. Designed to provide full coverage with minimal noise, Lucinity’s tools enable compliance teams to target real risks efficiently, cutting down on alert fatigue and operational costs.

1. Scenario-Based Monitoring

Lucinity’s Scenario-Based Monitoring system allows compliance teams to create, modify, and manage detection scenarios to meet specific risk and regulatory requirements. Using a no-code interface, compliance professionals can adapt to new risks without technical intervention. Key features include:

• Pre-built Detection Templates: Lucinity offers a library of ready-to-use scenarios, allowing teams to deploy detection mechanisms quickly.
• Custom Scenario Creation: Teams can configure scenarios to address unique risks, ensuring flexible and precise detection.
• Snooze Alerts: This feature reduces alert fatigue by pausing repeat alerts on previously reviewed cases, allowing teams to focus on new, critical alerts.
• Time Travel for Backtesting: Lucinity’s Time Travel tool enables historical analysis, allowing compliance teams to test and validate scenarios using past transaction data to optimize accuracy and reduce false positives.
• Segmentation: Compliance teams can create scenarios based on segmented customer behaviors and risk levels, providing tailored monitoring and prioritizing high-risk activities.

2. AI-Driven Monitoring via Partnerships

Through integrations with partners like Resistant AI and Neterium, Lucinity enhances its platform with advanced AI-driven monitoring for real-time transaction analysis. These partnerships allow Lucinity to provide:

• Real-Time Anomaly Detection: AI models identify suspicious transaction patterns that could indicate fraud, money laundering, or other types of financial crimes.
• Behavioral Risk Insights: AI-powered analysis uses behavioral models to detect high-risk activities and prioritize alerts.
• Reduced False Positives: Partner-driven tools help reduce noise in alerts by focusing on high-risk transactions, and enhancing detection precision.
• Additional Coverage with Sanctions and Watchlist Screening: Integrated watchlist screening with partners like Facctum provides real-time sanctions monitoring, ensuring compliance with regulatory requirements.

3. Case Manager for Streamlined Investigations

Lucinity’s Case Manager is a centralized hub for handling alerts, combining data from scenario-based and AI-driven monitoring sources. This all-in-one system improves efficiency and supports streamlined investigations:

• Unified Alert Management: Consolidates alerts from all monitoring sources, allowing compliance teams to review and manage cases in a single platform.
• Customizable Workboards: Enables tailored workflows based on teams, processes, or case types, ensuring efficient case organization.
• Auditability and Reporting: The Case Manager supports end-to-end tracking and documentation, helping teams meet regulatory requirements with detailed audit trails.

4. Luci AI Copilot for Enhanced Analysis

Luci, Lucinity’s AI-powered assistant, augments the investigative process by providing intuitive insights and case visualizations, facilitating faster and more thorough analyses:

• Case Summaries and Transaction Flow Visualization: Luci delivers clear case overviews, highlighting key transactions and visualizing money flows to reveal suspicious patterns.
• Risk Indicator Highlights: Luci points out high-risk indicators within cases, helping teams prioritize investigations.
• Report Generation: Luci assists with creating and standardizing investigation reports, saving time and ensuring accuracy across cases.

5. Integrated Compliance Tools for Full Coverage

Lucinity’s platform offers comprehensive compliance coverage with flexible, self-service configuration options. Compliance teams can easily adjust their monitoring setup to adapt to regulatory changes, reducing reliance on technical teams. Benefits include:

• Full control over monitoring and alert thresholds, allowing compliance to fine-tune detection accuracy.
• Reduced alert fatigue and enhanced efficiency, supporting compliance teams as they focus on high-risk cases while managing operational costs.

With these features, Lucinity provides a robust transaction monitoring solution, empowering compliance teams to maintain regulatory compliance, reduce alert noise, and focus on truly suspicious activity.

Summing Up

By adopting a hybrid approach that combines scenario-based systems with AI-driven techniques, financial institutions can better detect both known risks and emerging threats, enhancing their ability to prevent financial crime effectively. To reiterate, here is what a hybrid approach offers-

1. Reduced False Positives: Combining static scenarios with adaptable AI models significantly lowers false positive rates, enabling compliance teams to focus on genuine threats.
2. Real-Time Detection: AI’s capability to analyze large volumes of data in real time allows for immediate responses to suspicious activities.
3. Customizable Scenarios: Scenario-based scenarios provide a customizable, regulatory-compliant foundation, while AI adds adaptability for emerging risk patterns.
4. Holistic Monitoring: A hybrid approach offers a well-rounded perspective on customer behavior, improving overall compliance efforts.

For financial institutions looking to enhance their transaction monitoring without replacing existing systems, Lucinity’s platform offers a streamlined and scalable solution.

FAQs

What is the benefit of combining scenario-based and AI-based transaction monitoring?
Combining both methods enhances detection accuracy while reducing false positives, leveraging predefined scenarios alongside adaptive machine learning models.

How does Lucinity integrate scenario-based monitoring with AI?Lucinity merges customizable scenario-building tools with external AI-powered solutions, like Resistant AI, for real-time fraud detection and behavioral analytics.

Can Lucinity’s platform operate in real-time?
Yes, Lucinity integrates with real-time monitoring tools through partnerships like Resistant AI, allowing for immediate responses to suspicious activities.

Does Luci Copilot assist in detecting suspicious transactions?
Luci Copilot supports investigations by summarizing cases and visualizing transaction flows but does not detect suspicious transactions directly; it enhances decision-making based on available data.