Solution Brief
Financial crimes analytics
Augment your financial crime compliance program with decisions powered by governed AI.
The issue
Many anti-money laundering (AML) departments at financial institutions (FIs) use rules-based transaction monitoring systems that create far too many false positives. While detecting real threats of money laundering, terrorist financing or sanctions violations is essential, excessive false positives vastly increase the number of investigators needed. That can divert resources and allow complex threats to go undetected. For example:
- Fake, synthetic identities that are developed from breached data.
- Misrepresentation of business ownership.
- Cryptocurrencies being used to help criminals move illicit funds across borders.
As criminals become more sophisticated and GenAI augments their capabilities, FIs need an innovative, multi-layered approach to fighting financial crime. Next-generation capabilities powered by automation, AI, GenAI, and machine learning (ML) intelligently adapt to changing financial crime patterns. These technologies help to create more effective AML programs that cost less and allow investigators to focus on high-risk alerts, entities and events. When you integrate these technologies with existing compliance systems, you can augment your current AML infrastructure rather than having to replace it.
The challenge
Reducing false positives and uncovering more unknown threats
It’s estimated that money laundering activities account for 2% to 5% of the global GDP, equating to trillions of dollars laundered annually. However, only 1% to 5% of these are detected and successfully prosecuted. Every false positive diverts essential resources from investigating and reporting serious threats.
Lowering the cost of compliance
The projected total cost of financial crime compliance across FIs worldwide was $206.1 billion in 2023. As the challenges increase, it is becoming harder for compliance teams to maintain an adequately resourced compliance function until they can fully optimize their resource utilization.
Improving operational efficiency
Time-consuming manual AML tasks prevent you from spending time on what matters most. Automating these tasks allows investigators to focus on high-risk threats.
Implementing cutting-edge technology.
Legacy systems can’t keep up with modern financial crime activities and changing business landscapes. The most effective AML solutions today rely on advanced analytics techniques – AI, GenAI, and ML – to identify unknown risks and hidden relationships.
(Deploy fast, data-backed decisions without replacing existing compliance systems; Ensure that AI is used responsibly and is trustworthy; Reduce false positives, find more unknowns and spot financial crime patterns)
Our approach
SAS’ compliance analytics solution seamlessly interfaces with your existing AML platform to optimize monitoring, improve efficiency, reduce compliance costs and detect more “true positives” through advanced AI, GenAI and ML.
With SAS, you’ll gain:
Earlier detection
Predictive analytics evaluates hundreds of financial crime features and helps to detect financial crime threats at an early stage. This promotes a proactive approach to fighting and reporting financial crime activities to authorities.
Holistic view of financial crime risk
Financial crime risk dashboards visually identify points of optimization as well as emerging risks and typologies across large volumes of data.
Vastly reduced false positives
Through dynamic segmentation, you can create smart peer groupings with risk-based thresholds to accurately identify anomalies and significantly reduce false positives.
Prioritized alerts and investigations
ML models effectively learn from past decisions to expedite high-risk case investigations and hibernate low-risk events until they’re deemed worthy of investigation.
Automated case and regulatory report narration
Natural language generation translates massive data sets into readable text, which automates the creation of narratives for regulatory report filings and enhances operational efficiency.
Machine learning techniques provide that layer of intelligence that allows us to identify risk situations, analyze them very quickly and intervene when necessary. We have reduced false positives by 40% and increased our ability to handle anomalies by more than 20%.Raffaele Panico Head of Fraud Management, Poste Italiane
SAS difference
SAS helps FIs fight money laundering and terrorist financing with AI/ML, intelligent automation and advanced network visualization. With SAS, you can gain the advantage over criminals as you:
Reduce false positives
- Reduce false positives, uncover more unknowns and increase your anti-financial crime (AFC) solutions’ effectiveness using SAS’ industry-leading data and AI capabilities coupled with extensive AFC domain expertise.
Lower the cost of compliance
- Lower the cost of compliance by improving investigators’ productivity and optimizing resource utilization.
Improve operational efficiency
- Improve operational efficiency without needing to remove and replace existing AFC systems.
Make analytics accessible
- Make analytics accessible and ensure that data-backed decisions are transparent to regulators
Ensure the responsible use of trustworthy AI
- Ensure the responsible use of trustworthy AI through native data lineage, model governance and explainability features.
Adapt to business and organizational changes
- Adapt to business and organizational changes with a nimble, AI-enabled AFC platform.
SAS helps FIs stay ahead of increasingly sophisticated financial crimes with AML technologies that continually adapt to changes.
SAS facts
SAS has helped customers across the world achieve remarkable results.
3x
increase in regulatory report conversion rate - with a SAS neural network model that replaced 10 cash activity scenarios.
50%
improvement in alert productivity – with an explainable alert scoring model and hibernation approach that identified worthy investigations.
30%
increase in detection of high-risk customers and 1.7 billion transactions processed in less than 10 minutes - with an ensemble AI model.