by Nikita Ivanov, Bob’s Guide (www.bobsguide.com)
Americans currently lose $50 billion a year to a variety of fraudulent practices, according to estimates from the Financial Fraud Research Center at Stanford University. These practices include theft of credit cards and personal financial information, unauthorized checks, forged documents, tax evasion, and the manipulation of mortgages, corporate financial statements, securities trading, and computerized banking. Financial services firms that fail to prevent this fraud also suffer significant damage to their reputations.
Financial fraud is a rapidly growing, multi-billion-dollar ‘business’, with Juniper Research predicting that online fraud alone will climb from $10.7 billion in 2015 to $25.6 billion in 2020. A reason for this is that the amount of data that needs to be processed and analysed now is overwhelming. The solutions that have been designed to automatically verify, analyse, and audit transactions to detect and prevent the fraudulent activity cannot handle the volume.
Automatic fraud prevention requires sophisticated approaches. These include:
- Statistical and multi-channel analysis
- Models and probability distributions
- Comparisons with user profiles
- Algorithmic analysis
- Data clustering and classification
- Artificial intelligence and machine learning
The one common element is that performing these activities in real-time or near real-time on extremely large datasets requires high performance and highly scalable technologies. It must also be accomplished while firms are simultaneously tackling other crucial and processing-intensive activities, such as ensuring regulatory compliance. For a deep dive into this topic, read “Powering Financial Fraud Prevention with In-Memory Computing,” a new white paper by GridGain Systems, a leading provider of open source in-memory computing solutions for the financial services industry.
Technologies used for fast data analysis
To attempt to detect and prevent fraud in today’s data-intensive environments, most financial firms rely on a variety of technologies, including:
Big data The first step in using financial data for fraud prevention is preparing the data for analysis. Big data technologies provide ways to organise large datasets into multiple pools and connect them for fraud detection and analysis.
Apache™ Hadoop® with MapReduce Financial transactions typically execute within milliseconds. The fraud detection technology must analyse a transaction, validate it, and check all available data pools without impacting overall transaction performance.
Complex Event Processing (CEP) with data streaming This technology involves looking at multiple incoming data streams and using artificial intelligence (AI) to identify potential fraud.
Data partitioning and parallel processing clusters In high-volume transaction systems, checking one transaction at a time for fraud is not an option. Systems with data partitioning and parallel processing across clusters make it possible to process multiple transactions simultaneously and distribute the processing load across the cluster – while maintaining data consistency.
Scalable data architecture Large financial institutions are experiencing 20-30% data growth year-over-year, and they can’t risk running out of space. A highly scalable data architecture should enable firms to keep adding additional storage without impacting performance…….
Nikita Ivanov is the Founder & CTO of GridGain Systems.