Financial fraud is now a multi-billion-dollar business and growing rapidly, with Juniper Research predicting that online fraud alone will climb from $10.7 billion in 2015 to 25.6 billion in 2020. Financial fraud prevention is critical as failure to detect and prevent fraud can harm the reputations of financial firms and reduce confidence in the industry as a whole.
Protecting their customers from fraud and protecting themselves from fraud-related losses are high priorities for financial institutions. However, fraud prevention is not a simple task, and firms must tackle it simultaneously with other crucial tasks such as ensuring regulatory compliance. To accomplish these data-intensive tasks in a timely manner, financial firms need solutions that are flexible, scalable, reliable, and fast enough to analyze extremely large datasets in real-time.
Financial Fraud Prevention with In-Memory Computing
Fortunately, today’s in-memory technologies provide powerful tools to combat fraud – tools that perform complex processing, modeling, and analysis of big data in real-time.
Financial institutions use the GridGain in-memory computing platform for a variety of fraud detection use cases involving high-volume transaction processing and big-data analytics, such as checking for compliance with anti-money-laundering (AML) and “know your customer” (KYC) regulations, looking for market manipulation, or monitoring other regulated areas. They are using complex event processing for real-time or near real-time customer views and analysis of positions, so they require ultra-low latency in real-time or near real-time data processing and analytics.
The key modules of the GridGain in-memory computing platform are that relevant to financial fraud prevention use cases are:
- Data grid – Essentially an in-memory key value store that can be queried
- SQL grid - provides the ability to interact with data in-memory using ANSI SQL-99 via JDBC or ODBC APIs
- Compute grid - A stateless grid that provides high-performance computation in memory using clusters of computers and parallel processing
- Service grid - A service grid in which grid service instances are deployed across the distributed data and compute grids
- Streaming – The ability to consume an endless stream of information and process it in real-time
- Advanced clustering – The ability to automatically discover nodes, eliminating the need to restart the entire cluster when adding new nodes
Powering Financial Fraud Prevention with In-Memory Computing - White Paper
If your organization is developing or trying to improve a financial fraud prevention solution, please download Powering Financial Fraud Prevention with In-Memory Computing, a new GridGain Systems white paper that takes a detailed look at financial fraud prevention requirements and how in-memory computing can deliver the performance and scale financial fraud prevention use cases demand.
As always, if you have questions or comments, please let us know!