Powering the Risk-Based Approach with Dynamic Risk Scoring and AI-Driven Profiling 

Key Takeaways:

The Risk-Based Approach (RBA) has evolved into the gold standard for compliance practices in financial institutions. By concentrating resources on high-risk areas, it ensures that organizations stay ahead of evolving financial crime threats. However, despite the widespread endorsement from regulators, many institutions still struggle to implement it effectively. 

This article explores how dynamic risk scoring and behavioral profiling form the backbone of an effective RBA—and how Siron^®One brings these capabilities to life through AI-powered compliance. 

What is the Risk-Based Approach and Why Regulators are Endorsing It 

The Risk-Based Approach (RBA) focuses on high-priority threats using contextual data, while streamlining efforts for low-risk areas. This shift moves organizations away from one-size-fits-all controls towards tailored, data-driven decisions, that make them more resilient to evolving financial crime. 

Regulators across jurisdictions have increasingly endorsed the Risk-Based Approach, recognizing that traditional rules-based systems often lead to conflicting rules, overlapping requirements, siloed processes, and higher compliance costs, without effectively reducing risk. 

• In its guidelines on risk-based supervision, the European Banking Authority (EBA) calls for supervisory resources to be “allocated in proportion to the level of money laundering and terrorist financing risk associated with credit and financial institutions.” ¹

• The Financial Action Task Force (FATF) reinforces this in its guidance, stating: “the risk-based approach is central to the effective implementation of the FATF Recommendations.” ² 

• The Financial Crimes Enforcement Network (FinCEN) similarly emphasizes flexibility in its proposed modernization rule, stating: “Through its emphasis on risk-based AML/CFT programs, the proposed rule seeks to avoid one-size-fits-all approaches to customer risk.” ³

How the Risk-Based Approach Works: Dynamic Profiling and Risk Scoring 

The principles of the Risk-Based Approach have long been endorsed by regulators, but fully realizing its potential remains a challenge for many financial institutions. While RBA is widely considered the gold standard, only those with best-in-class capabilities are able to meet its demands. This is because RBA requires more than a framework shift: it demands advanced capabilities to dynamically profile customers, segment risk, and continuously assess behaviors—features that many institutions still lack. 

At its core, RBA has always been about monitoring customer behavior and adjusting risk assessments based on evolving patterns. However, dynamic risk scoring and behavioral profiling are what truly bring RBA to life. These capabilities allow compliance teams to detect subtle shifts, anomalies, and emerging risks that traditional, rules-based systems simply cannot. 

How It Works

Unlike rules-based systems, which assign static risk scores based on limited, pre-set data (e.g., KYC info, geography), RBA adapts by continuously monitoring real-time behavior and updating risk scores accordingly. AI adds another layer of sophistication to the process, improving risk detection by analyzing complex behaviors and patterns. 

• Initial Risk Score: At the beginning of the customer lifecycle, an initial risk score is assigned based on static data (e.g., KYC, geography) 

• Dynamic Profiling: AI dynamically segments customers into peer groups/clusters based on behavior and risk profiles. These clusters evolve as new behavioral data is collected. 

• Continuous Monitoring: As customers engage in transactions, their behaviors are continuously assessed against the baseline risk score and peer group data. If deviations are detected, the system adjusts the risk score and triggers alerts. 

• Proactive Alerts: Alerts are prioritized based on the updated risk score, allowing compliance teams to focus on the riskiest cases first, improving efficiency and responsiveness. 

Risk-Based Approach vs. Static Risk Rating

While rules-based systems can fulfill formal obligations, they often leave institutions exposed to unseen risks. In today’s fast-changing environment, only a dynamic, risk-based approach offers the flexibility, precision, and intelligence required to stay ahead.

How Siron^®One brings the Risk-Based Approach to Life 

Transitioning from a static, rules-based system to a risk-based approach is much more than a framework shift. It requires technology that can continuously assess, adapt, and act on real-time risk signals. 

Siron^®One enables this transformation through AI-powered monitoring, scoring, and automation. Key capabilities include: 

• Dynamic Risk Scoring

Siron^®One assigns a contextual risk score at onboarding by analyzing customer data, KYC information, geography, and business rules, and adapts it as new behaviors, transactions, or contextual factors emerge.

• Behavioral Profiling & Clustering

Customers are grouped into dynamic peer groups based on transaction patterns and risk level, enabling contextualized risk assessment.

• Entity Deviation Detection

Siron^®One continuously monitors customer behavior and assigns a deviation score to reflect the change in risk when behavior significantly deviates from its cluster. 

• Alert Prioritization

Siron^®One uses an Alert Prediction Score (APS) to rank alerts by relevance, helping teams focus on the riskiest and most urgent cases.

• Perpetual KYC (pKYC)

Continuously monitors customer data in real time and updates risk scores dynamically based on emerging behaviors and contextual factors.

• Automated Enhanced Due Diligence (EDD)

Based on updated scores from pKYC, Siron^®One automatically triggers tailored actions (e.g., PEP scans, name screening) based on updated risk scores.

• Unified Data & Intelligent Document Management

All compliance-relevant data (customer profiles, risk factors, business rules, and regulatory logic) is consolidated into a single source of truth, ensuring accuracy and consistency.

Siron^®One combines advanced AI, machine learning, deep learning, predictive analytics, and GenAI to continuously monitor activity, detect anomalies, and update risk profiles in real time. By automating key compliance functions, it turns the risk-based approach into a scalable, efficient, and operational reality. 

A dynamic world demands dynamic compliance.

The Risk-Based Approach is not just best practice—it’s a regulatory imperative and strategic advantage. With Siron^®One, financial institutions can transform compliance from a cost center into a strategic enabler for smarter, faster decisions. 

Interested in seeing how Siron^®One puts the risk-based approach into action?  Book a demo with us today! 

Sources:

1. https://www.eba.europa.eu/legacy/regulation-and-policy/regulatory-activities/anti-money-laundering-and-countering-financing/guidelines-on-risk-based-supervision 
2. https://www.fatf-gafi.org/en/publications/Fatfrecommendations/Risk-based-approach-banking-sector.html 
3. https://www.fincen.gov/news/news-releases/fincen-issues-proposed-rule-strengthen-and-modernize-financial-institutions 
4. https://www.afponline.org/training-resources/resources/articles/Details/7-steps-to-an-effective-risk-management-process