If we combine market dynamics, legislation and growing fraud strategies then we can detect fraud more quickly and efficiently. There is an increasing need for the adaptation of the dynamic detection systems to evolving compliance regulations, detection of sophisticated fraud patterns, scale for the dealing with increasing transaction values and reduce false positives. Streaming analytics, predictive analytics and business process management in combination provides a strong, easy and cost-effective system for fraud detection.

Benefits

• Faster Incident Resolution

We can create dashboards that attain information from multiple databases and real-time information with the help of statistical modeling, data discovery and aggregation within a single tool. Users can filter, zoom in and zoom out and slice or dice data in order to know that whether the alert needs more investigation or can be discarded as false positive.

• Faster Surveillance Deployment

Surveillance schemes can have multiple assets with data that go from checking of abuse in cash equities to complex schemes which involve Foreign Exchange Derivatives. With the help of connectivity in 150 options, all your data can be involved by using graphical flow development that reduces problems due to complexity and increases collaboration.

• Surveillance Scenarios

There will be a need to create thresholds that trigger alerts and remove certain events from the analysis together. Performing of correlation tests for the detection of complex potential abuse with one type of asset so there can be assessment of performances and alerts of related asset types. This in turn helps in maximizing identification of anomalous data point and diminishes the false positives.

Supervised and Unsupervised Model

There is analysis of data of both supervised and unsupervised model. This is done for the production of a score which shows indicates of fraud and some other scores that shows how the transaction shifts from normal which is also known as oddity. There are many service providers like  digital harbor  which work in this direction.

• Real-Time Transaction Scoring

Whenever there is scoring of a transaction, it is either passed or is flagged as potential fraud, or both. On scoring the transactions happening, there can be a response generated against the raised alerts which is given by an event processing platform and form a case that provides the investigation of potential fraud.

• Case management

If we have a integrated and capable case management tool, we can take the alert from whenever it is raised, with the investigation stage and if its applies, onto the regulator. There is capturing of inputs from historical data transaction log extracts and surveillance analyst notes in order to determine what fraud is and what is not. You can trust  digitalharbor   in this blindly.

• Rigorous Auditability

There is a justification to the regulators about your decisions regarding what events were determined to be fraud or not and actions were increased on pace or not. There needs to be shown consistency over the time, documentation of changes and approval needs to be done and reasons for changes in approach needs to be verified. The fraud detection needs to be done by using the combination of analytics and event processing instead of a series of rules for detection.