
AML Investigations and Case Management: A Guide to Detecting and Combating Money Laundering
Money laundering can be a challenge for leading financial institutions. Learn more about how AML practices help prevent and combat this financial crime.
As retail banking becomes more inter-connected, financial crime is increasingly dispersed across payments, cash activity, cross-border flows, and third-party facilitators. Legacy, rules-based AML systems lack the context advanced analytics and AI capabilities need to distinguish real risk from noise, allowing criminal networks to evade detection. Without a unified view of customers, counterparties, and networks, banks struggle to surface true risk with confidence.
Illicit financial flows are projected to hit $4.5 trillion to $6 trillion by 2030.
Source: Secretariat
Generative AI is now the #1 external risk to financial crime functions.
Source: ACAMS
HOW WE SOLVE IT
Unify fragmented internal and external data into enriched, trusted views of personal and SME customers, counterparties, and activity.
A connected data foundation provides a holistic view that powers investigations beyond isolated alerts, bringing full behavioral and network context to retail AML risk detection.
Graph Generation uncovers suspicious activities, hidden facilitators, and coordinated behavior spanning across the broader retail banking ecosystem.
Connected, contextual data powers explainable AI and typology driven scoring across Contextual Monitoring, detection, and investigations, accelerating action and strengthening Retail AML programs.
THE IMPACT
in investigation time (at scale)
in false positives
sent for further investigation
Explore how connected data, typologydriven scoring, and network analytics surface true risk across retail customers and activity, cutting through alert noise to fight AML in retail banks.
Build a trusted data foundation to deliver context for decision-making.
Learn moreEnhance customer experience and accelerate revenue growth with a 360-degree connected view of customers.
Learn moreDetect risk in real time to identify unknown risks and deliver more accurate risk ratings.
Learn moreRevolutionize risk assessment with a holistic understanding of borrowers, their counterparties and relationships.
Learn moreUncover hidden fraud risks with a contextual approach to detection and prevention.
Learn moreReduce false positives and focus on real risk by modernizing your AML monitoring, detection, and investigation.
Learn moreRules-based transaction monitoring systems have several limitations and challenges, which have historically hindered their effectiveness in detecting and preventing financial crimes.
For example, the rules and thresholds need to be manually created and updated, making the system less adaptable to evolving and emerging threats. Similarly, rules-based systems also typically analyze individual transactions in isolation and may not provide a comprehensive view of customer and counterparty behavior or relationships. They may miss the connections between seemingly unrelated transactions or accounts, which can be crucial in identifying more complex fraud networks or money laundering schemes.
In contrast, by combining multiple internal and external datasets all at once, Contextual Monitoring transforms the view of risk to a gain clearer understanding of customers, counterparties, their relationships, and behaviors in real time. Using advanced Entity Resolution and network generation techniques, Contextual Monitoring focuses on holistic relationships rather than transaction risk in isolation.
This added context helps to identify hidden risk and generates fewer, more accurate alerts. Institutions can reduce rising compliance and operational costs and conduct more effective and efficient intelligence-driven risk processes without replacing existing systems.
Turning data into intelligence is imperative for reducing manual processes, identifying critical connections and breaking down silos between teams. For AML investigations, shifting to an intelligence-led approach goes beyond a single event, relationship or activity enabling a more holistic understanding of customers, employees, counterparties and their related risk. With the broad capabilities of Quantexa’s platform, intelligence officers have a powerful tool that can run different analytical tasks, covering a risk-based approach and integrating third-party data and external sources for deeper investigation. Unlike traditional systems, Quantexa's AML software visualizes hidden connections through additional context.
Evolving AML intricacies and typologies driven by multiple channels, lines of business and products have driven major challenges in financial services. With Quantexa's Decision Intelligence platform, Quantexa's AML software can transform a banks approach to financial crime compliance. Improving risk coverage, identifying complex typologies, pinpointing real risk, reducing false positive and uplifting efficiencies.