Accurate Risk Score
IFM-X blocks APP fraud through data enrichment, including collective intelligence for known scam risks and multidimensional profiles. This analysis provides insights that produce an accurate risk score.
IFM-X for Scams and APP
Financial institutions are combating multiple scam typologies covering a wide range of data points that are difficult to manually investigate. It grows even more complex with authorized push payment (APP) fraud, because the actual client is conducting the transaction.
To solve this multi-faceted problem, we have introduced typology-centric, multi-model execution.
This approach compares multiple scam typologies and models at once,
giving you a best fit to resolve the specific fraud in action.
Traditional Approach vs. NICE Actimize Approach
Traditional Approach
Current transaction-centric approaches triage and investigate alerts by transaction type, which is inefficient
NICE Actimize Approach
Typology-specific fraud detection models create strategies for fraud types, routing investigation alerts to specialized teams best suited to resolving them, which speeds up investigation
APP Fraud Challenges and Solutions
Typology-Centric Approach
There are many different scams and typologies to monitor, and current transaction-centric approaches won't work. With typology-specific fraud detection, models identify and adapt to different scams, routing cases to experts trained in that typology.
IFM-X for Scams
Typology-Centric, Multi-Model Execution
Scam-Specific Analytics and Risk Scores
Data-driven expert features and AI models to increase detection rates and reduce false positives
Target Specific Scam Typologies
Multidimensional
Profiles
Parallel processing of customer behavior, profiling data, channel, transactions, and more, in addition to cross-FI data
Stop Scams Throughout Customer Life Cycle
Scam-Specific
Alerts and Workflow
Multi-Model Execution produces alerts specific to scam typologies routed to specialists
Increase Operational Efficiencies