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Power Your Anti-Financial Crime Program with Augmented Intelligence

Oracle Financial Services Crime and Compliance Studio is a workbench for data scientists and compliance professionals that enhances AML and fraud detection and investigations. It comprises a suite of advanced tools that leverage the latest innovations in artificial intelligence, open source technologies, and data management. Purpose-built for fighting financial crime, it is fully integrated with other Oracle Financial Crime and Compliance Management applications.

power your anti-financial crime program with augmented intelligence


More accurate and efficient investigations

Investigators have access to a resolved, in-memory graph that allows them to quickly uncover hidden relationships and unusual financial activity patterns, all without leaving Oracle’s Enterprise Case Management application.

Increased agility through faster discovery and modeling of new financial crime patterns

Use our in-memory graph to find emerging financial crime patterns, then use your preferred open source programing language to create new scenarios or models.

More value from your data

Studio brings all your data—both internal and third-party—together and provides seamless, secure access to it so you can explore it in real time and extract valuable, previously inaccessible insights.

Play Beyond the Rules: Fight Money Laundering with Graph Analytics

Learn how Oracle has embedded this powerful technology in our Financial Crime and Compliance Management platform, how AML investigators can use it on a day to day basis, and how chief compliance officers can leverage it to maximize the long-term success of their anti-money laundering programs.

fight money laundering with graph analytics

Key Product Features

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Graph analytics

Uncover hidden relationships and power intuitive visualizations

Easily explore any relationship among entities in real time to extract valuable, previously inaccessible insights from your data.

  • 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.
  • Power intuitive, dynamic visualizations in Investigation Hub for faster, more accurate 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.

Entity resolution

Gain a single view of each customer and external entity

Preconfigured and extensible entity resolution ensures your global graph is accurate.

  • Targeted techniques for different attributes allows for missing or multiple information sources.
  • UI-based configurations for weighting scoring methods.
  • Machine learning tuning for enhanced resolution scoring.
  • Increase the confidence of resolved entities by examining their surrounding graph structure.

Open source technologies

Build models and scenarios using the tools you love

Leverage popular open source languages, technologies and standards, minimizing the need to re-train staff, increasing productivity

  • Publish machine learning notebooks in R and Python. Use open source packages in R, Python, Spark ML, or Oracle’s optimized libraries for machine learning, such as Oracle R Enterprise and Oracle Advanced Analytics for Hadoop.
  • Author, deploy, and test scenarios in SQL, Scala, Python, or R.
  • Leverage Apache Zeppelin and Jupyter notebooks, the de facto standard development tools for data scientists.

Advanced data management

Explore all your data, in one place

Maximize the value of your data lake.

  • 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.