Detect Fraud As It Happens

Identify false positives by using machine learning

The combination of legislation, market dynamics, and increasingly sophisticated fraud strategies requires you to be proactive in detecting fraud quicker and more effectively. Dynamic detection systems need to adapt to evolving compliance regulations, scale to deal with growing transaction volumes, detect sophisticated fraud patterns, and reduce false-positives. The combination of predictive analytics, streaming analytics, and business process management provides a powerful and cost-effective system for detecting fraud.

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Benefits

Faster Incident Resolution

With data discovery, statistical modeling, and aggregation within a single tool, you can create dashboards that pull from multiple databases and real-time information. Users can filter, slice and dice, and zoom in and out of data to determine if the alert needs further investigation or can be discarded as a false-positive.

Faster Surveillance Deployment

Surveillance projects can span multiple assets, with data and scenarios that range from monitoring abuse in cash equities to complex schemes involving foreign exchange (FX) derivatives. With connectivity to over 150 options, all your data feeds can be included using graphical flow development that reduces complexity and increases collaboration.

Flexible Surveillance Scenarios

You’ll want to tailor thresholds that trigger alerts and exclude certain events from the analysis altogether. Perform correlation tests to detect complex potential abuses on one asset type so you can determine alerts and performance for related assets. This helps to maximize identification of anomalous data points and minimize false-positives.

Capabilities
Image: Supervised and Unsupervised Models

Supervised and Unsupervised Models

Data is analyzed against both a supervised and unsupervised model to produce a score that indicates the probability of fraud and another score that shows how the transaction deviates from what’s normal, also known as an oddity.

Image: Real-time Transaction Scoring

Real-time Transaction Scoring

When a transaction is scored, it either passes or is flagged as probable fraud, an odd transaction, or both. By scoring transactions as they happen, you can respond to raised alerts provided by an event processing platform and create a case that facilitates the investigation of potential fraud.

Image: Case Management

Case Management

With a capable and integrated case management tool, you can take an alert from when it is raised, through the investigation stage, and if applicable, onto the regulator. Capture inputs from historical data, surveillance analyst notes, and transaction log extracts to determine what’s fraud and what’s not.

Image: Event Processing

Event Processing

Detect fraud by using a combination of event processing and analytics rather than a series of concrete rules for detection. The agility of these platforms allows you to modify the framework quickly and easily to adapt to other scenarios.

Image: Multiple Use Cases

Multiple Use Cases

Fraud detection spans many different domains including anti-money laundering, trade surveillance, credit/debit card monitoring, health insurance fraud, insurance fraud, and online operations.

Image: Rigorous Auditability

Rigorous Auditability

Justify to regulators your decisions about what events were determined to be fraud or not and what actions were escalated or not. Show consistency over time, reasons for changes in approach, and documented changes and approvals.

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