Graph Analytics for Next-Level Financial Crime Detection
Learn how to use advanced graph analytics techniques to detect and prevent financial crimes in this online event!
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- Event lasts 30 minutes
Uncover the Power of Graph Analytics in Financial Crime Detection
In 2023, financial crime cost the global economy trillions, with fraud alone reaching $485 billion. Traditional detection methods are hitting limits, but graph analytics offers a breakthrough—boosting detection accuracy by up to 20% by identifying hidden patterns and reducing false positives.
Graphs unify data, cut through fragmentation, and accelerate detection by revealing complex, multi-hop connections that other tools miss. Join our experts to explore real-world examples of how graph analytics can transform financial crime detection, empowering compliance officers, data scientists, and finance professionals to stay ahead of emerging threats.
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Frequently asked questions
Graph analytics uncovers complex relationships in data by focusing on connections between entities (like accounts, transactions, or individuals), rather than analyzing each data point in isolation. This approach excels at detecting hidden networks and patterns that traditional methods may miss.
Graph analytics is especially effective at identifying fraud, money laundering, insider trading, and complex scams that involve multi-level, interrelated transactions and relationships.
Many banks and financial institutions have seen a 15-20% improvement in detection rates by using graph analytics. For example, some institutions have used it to identify previously undetectable fraud rings and suspicious transaction patterns across vast data sets.
By analyzing relationships across multiple levels and pinpointing patterns indicative of fraud, graph analytics provides more context, reducing false positives. This precision helps compliance teams focus on actual risks, saving time and resources.
TigerGraph provides high-speed, scalable graph processing, allowing organizations to analyze massive amounts of financial data in real-time. Its platform is optimized for handling complex queries quickly, crucial for timely fraud detection.
Financial institutions can start with pilot projects focused on high-risk areas, integrate graph analytics with existing data warehouses, and gradually expand to cover more complex analysis as they observe results.
Challenges include data integration, scalability, and team expertise. These can be addressed by using robust graph platforms like TigerGraph, training teams on graph modeling, and establishing clear data pipelines to manage large volumes efficiently.
Graph analytics excels at tracing multi-hop relationships—connections that link entities through multiple steps. This enables the detection of layered, indirect relationships that could signify a criminal network or money-laundering scheme.
A mix of data science, graph theory, domain expertise in finance, and knowledge of anti-money laundering (AML) practices is essential. Familiarity with graph databases and querying languages, like TigerGraph’s GSQL, is also beneficial.
Trends include the integration of AI with graph analytics for predictive insights, broader adoption across industries, and enhanced regulatory support for graph-based systems due to their effectiveness in combating sophisticated financial crimes.