Supercharge Your Anti-Money Laundering Program with Advanced Analytics
Oracle Financial Services Compliance Studio is an advanced analytics application that supercharges anti-money laundering and anti-financial crime programs for better customer due diligence, transaction monitoring, and investigations. It comprises a suite of advanced tools that leverage the latest innovations in artificial intelligence, machine learning, graph analytics and data management. Purpose-built for fighting financial crime, it unites the best open source technologies with breakthroughs from Oracle Labs. It is fully integrated with other Oracle Financial Crime and Compliance Management applications for maximum ease of use and scalability.
Anti-Money Laundering Teams are More Effective with Compliance Studio
Get more value from your data
Bring all your data—both internal and third-party—together and get seamless, secure access to it. Explore in real time and extract previously inaccessible insights.
Work smarter in Investigation Hub
Surface hidden relationships using graph visualizations—embedded in your case manager—that put data at your fingertips. Natural language processing writes case narratives so you don’t have to.
Outsmart new criminal schemes
Use our in-memory graph to find emerging financial crime patterns, then create new scenarios or models with your preferred open source programing language.
See How Oracle Uses Advanced Analytics to Fight Money Laundering
Oracle Financial Services Crime and Compliance Studio provides access to Investigation Hub, which puts in-memory graph visualizations directly in any case manager. With it, AML investigators can explore complex criminal networks in real time. See how Investigation Hub uses the power of graph analytics to uncover money laundering by drug traffickers.
Play Beyond the Rules: Fight Money Laundering with Graph Analytics
Learn how to leverage this powerful technology to maximize the long-term success of your anti-money laundering program.
The Most Complete Advanced Analytics Application for Anti-Money Laundering Teams
Open all Close allGraph analytics for entity resolution and interactive visualizations of criminal networks
Uncover and explore previously hidden relationships in real time
- Succinctly express complex money patterns, detect multi-hop relationships, and identify hubs and spokes of activity using 30+ supplied graph algorithms and a built-in, SQL-like query language.
- Intuitive, dynamic graph visualizations in Investigation Hub (PDF) boost the speed and accuracy of investigations.
- Drive better modeling through better understanding of the network.
- Enhance detection by applying deep learning to find similar criminal networks.
- Boost efficiency by applying machine learning to graphs to automate case decisions.
- Preconfigured, extensible entity resolution provides a single view of each customer and entity, and ensures that your global graph is accurate.
Machine learning for adaptive customer behavior models
Augment existing rules-based models and increase effectiveness
- Manage both rules-based and advanced machine learning models in one integrated environment.
- Explainable AI provides insight into sophisticated black box machine models.
- Advanced, time-series feature engineering techniques to enrich data and detect trends.
- Easily customizable with 300+ out-of-the-box risk indicators.
Model management and governance
Address governance and regulatory obligations
- A visual canvas for building machine learning models.
- Interactive model testing.
- Model monitoring and alerts.
- Easy to create sandboxes for business users and citizen modelers.
- Fast point and click model deployment.
Advanced data management maximizes the value of your data lake
Explore all your data, in one place
- Embedded, pre-defined graph analytics engine automatically loads data from the Financial Services Data Foundation into a lighting fast, in-memory graph.
- Explore internal and third-party data together, in memory.
- Preloaded data from the International Consortium of Investigative Journalists adds context to investigations and highlights areas of unknown risk.
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