Fraud in the financial services industry is a long-standing problem that needs sophisticated methods to prevent and manage. It adversely affects customer loyalty and results in the loss of value/affinity. Hence, modern financial institutions are looking for fraud prevention solutions that are non-intrusive and don’t disturb customer experiences.
iFusionTM solution for fraud enables banks and financial organizations to analyze, detect, and prevent fraud using advanced analytics and sophisticated machine learning models. It provides a risk-scoring methodology to highlight the likelihood of fraud, attribute fraud to the underlying change in behavior, and provide reasons for fraud.
Ability to combine data from multiple systems and various formats through data virtualization and feature extraction, which improves risk scoring and provides a rich context for fraud investigators
Provides a rich set of configurable scenarios based on the behavioral patterns of the transactions, usage, and user dimensions to detect anomalous or suspect behavior
Supports a range of anomaly detection techniques, including sophisticated deep learning methods to model for fraud even at low signal-to-noise ratios (< 0.1%)
Provides fraud visualization to understand the risk from various dimensions and also to gain insights into the behaviors or patterns that are causing fraud
Automatically stops high-risk transactions to prevent losses and allows the low-risk transactions to minimize the impact on customer experience
The client needed a real-time fraud detection solution that can enable precautionary measures like step-up authenticationDownload