Advanced AML Transaction Monitoring Rules for Fraud & Compliance Teams

Businesses face a daunting challenge today: how to sift through massive amounts of transactional data to identify the few critical instances of suspicious activity or money laundering. Many compliance teams are burdened by this “needle in a haystack” problem, where rigid, rules-based systems generate a high volume of false positives – wasting time, resources and leading to operational inefficiencies. Many of these systems lack the nuance to distinguish between genuinely suspicious behaviors and the normal variations in customer activity, leaving businesses overwhelmed with alerts without a clear path to action.

Adding to the complexity, batch transaction monitoring – once the industry’s golden standard –is rapidly becoming outdated. In the current era defined by high-velocity transactions and with increasing speeds of financial crime, having the agility to adapt and apply more proactive methods to detect risk is emerging as a critical differentiator. Applied real-time monitoring supports the targeting of suspicious, higher-risk activities as they happen, rather than reacting after the fact.

Effective transaction monitoring (TM) requires a delicate balance – rigid rules alone aren’t enough. Instead, organizations need systems that combine traditional rules-based monitoring with behavioral pattern detection and machine learning. This provides not only comprehensive coverage but also the ability to adapt in real time, significantly reducing false positives and uncovering real financial crime risks.

SEON’s anti-money laundering (AML) rules engine empowers businesses to achieve this balance by providing flexibility, granularity and real-time insights, making compliance more efficient without compromising on thoroughness. Through its advanced rule customization and intelligent machine learning capabilities, SEON helps compliance teams efficiently cut through the noise and focus on the high-risk activities that matter most.

Why Transaction Monitoring Matters

Transaction monitoring is the backbone of AML compliance, and maintaining robust, real-time insights can significantly reduce the risk of financial crime. SEON’s solution allows companies to configure precise, customizable rules that fit their risk profile and regulatory requirements – whether for onboarding new customers, monitoring transaction patterns or detecting suspicious behavior.

Key Features to Look for in a Modern AML Transaction Monitoring Solution

When evaluating the capabilities of a high-performance AML transaction monitoring system, it’s essential to prioritize features that go beyond basic compliance. A truly effective solution delivers operational efficiency and robust risk management. The most advanced systems seamlessly integrate compliance, fraud detection and efficiency into a unified approach. Here are the key features every top-tier AML transaction monitoring solution should provide:

• Unmatched Flexibility: A robust AML TM solution should enable you to customize rules in real time, adapting to the specific needs of your business and its evolving risk profile. Whether it’s adjusting for high-risk regions, transaction thresholds or emerging fraud patterns, retaining flexibility and the ability to test and validate the impact of such changes, is crucial for staying agile. Look for systems that allow easy modifications without requiring complex overhauls allowing compliance teams to remain efficient and responsive.
• Granular Fraud Insights: Modern transaction monitoring must be able to cast a wide net, leveraging multiple data signals for precise detection. Granularity is key to distinguishing normal customer behavior from potentially suspicious activity. Your system should be able to analyze a range of signals – such as device fingerprinting, IP addresses, digital footprints, transaction velocities and behavioral patterns – granting deeper insights into both compliance risks and fraudulent behavior. A solution that integrates fraud insights into its detection model ensures that your system identifies money laundering and other sophisticated financial crimes.
• Real-Time Monitoring: In today’s high-velocity financial environment, batch monitoring isn’t fast enough. Suspicious activities happen in real time, and your system must be able to adapt to keep pace with changing and newly emerging threats. A modern AML solution should offer the ability to conduct real-time monitoring that flags anomalies immediately, allowing your team to act swiftly. This proactive approach helps intercept threats before they escalate, minimizing risk and preventing rapidly moving financial crime.
• Balanced Approach: Rules, Behavior and Machine Learning: Relying solely on strict rules can lead to a flood of false positives, overwhelming your team and reducing efficiencies. A top-tier AML TM system must be able to combine rules-based monitoring with behavioral insights and machine learning to intelligently detect emerging risks and patterns. This system can reduce false positives and enhance detection accuracy, allowing your team to focus on the threats that truly matter. Additionally, machine learning’s ability to adapt and refine itself based on new behaviors is imperative staying ahead of evolving fincrime tactics.

By ensuring your AML transaction monitoring system includes these critical capabilities, you can enhance your compliance efforts, reduce operational inefficiencies and evolve alongside changing regulations.

Examples of AML Rules for Transaction Monitoring

Behavior-Based Rules

High-Risk Currency and Products

Flag transactions involving high-risk currencies or products that are often associated with money laundering.

• Purpose: Monitor high-risk transactions in real time, adapting to shifting risks.

Transaction Value Exceeds Customer’s Declared Activity Limit

If a transaction exceeds a customer’s declared activity limit, this rule can automatically flag it for further investigation.

• Purpose: Align transactions with the customer’s expected behavior, reducing money laundering risks.

Structuring Patterns

Structuring – Inbound

SEON’s rules can flag multiple transactions just below regulatory reporting thresholds. For instance, more than 20 deposits under $10,000 within a 30-day period may suggest a deliberate attempt to avoid detection.

ATM Structuring – Inbound

Flagging more than 15 ATM deposits just below $2,000 within a month helps identify structuring via ATMs, which are often used to spread deposits and avoid scrutiny.

Purpose for both: This rule is essential for identifying classic money laundering patterns, such as structuring, where small amounts are deposited over time to avoid triggering reports.

Transaction Velocity

High Velocity – Inbound

Identify accounts that receive more than 20 incoming transactions within a single week.

High Velocity – Outbound

Similarly, track more than 20 outbound transactions in a single week to detect frequent money transfers, potentially signaling attempts to launder funds.

Purpose for both: Detect unusual frequency of deposits or withdrawals, which may indicate structuring or money laundering activities.

Transaction Limits

High-Value Transactions

With SEON, you can set customizable limits for both incoming and outgoing transactions. For example, you can flag any inbound or outbound transaction exceeding a pre-set limit (e.g., $25,000).

• Purpose: This ensures that high-value transactions are subject to enhanced due diligence and scrutiny.

Cumulative Transaction Volume

Set rules to monitor cumulative amounts over a specified period. For example, flagging accounts where incoming transactions exceed $200,000 over three months or outgoing payments exceed $100,000 in one month.

• Purpose: This rule helps detect potential layering or money laundering schemes by monitoring large sums of money over time.

Unusual Activity Patterns

Dormant Account Activation

Flags the first transaction after 12 months of inactivity. Dormant accounts suddenly active can signify account takeovers or money laundering attempts.

• Purpose: This rule helps identify accounts that might have been compromised for illegal use.

Cash Deposits Followed by Cash Withdrawals

For example, you can configure a rule that flags accounts where cash deposits exceeding $50,000 are followed by withdrawals of over $10,000 within 72 hours.

• Purpose: This combination of rules helps detect layering, where money is moved quickly through a series of transactions to obscure its origins.

How to Set Up a Rule in SEON

Balancing Rules, with Behavior Analysis and Machine Learning

One of the key strengths of SEON’s AML solution is the ability to combine rules-based detection with machine learning. While rules can catch well-defined suspicious patterns, machine learning adds an adaptive layer that detects anomalies and emerging threats that traditional rules might miss.

• Machine learning can detect patterns in customer behavior that suggest they are structuring payments, even when those payments don’t fit the typical rule-based definition of structuring.
• Behavioral analysis enhances rule-based monitoring by adjusting the risk score based on changes in customer activity, flagging anything unusual that deviates from the norm.

How SEON’s Rules Engine Enhances Compliance

SEON’s engine not only offers flexibility and granularity but also:

• Adapts to changing regulations by allowing quick modifications to rule sets that you can do all by yourself without engineers, support teams or coding experience.
• Enables real-time monitoring, providing instant alerts and updates.
• Integrates easily with existing workflows using a single API that provides access to fraud and AML data signals, ensuring efficient compliance efforts without disrupting normal business operations.