Overview
Our Risk Intelligence solution uses advanced AI and machine learning to uncover hidden risks and emerging threats that traditional rule-based systems miss.
Behavioral analytics establish normal patterns for each customer, enabling detection of subtle anomalies. Network analysis reveals hidden connections between seemingly unrelated entities.
Predictive models forecast which customers are likely to become high-risk, enabling proactive intervention before problems occur.
Key Features
Advanced analytics for comprehensive risk understanding
Behavioral Analytics
Machine learning models establish baselines and detect deviations from normal patterns
Network Analysis
Graph analytics reveal hidden relationships and money flow patterns between entities
Predictive Modeling
Forecast future risk levels to enable proactive risk management
Peer Group Analysis
Compare customer behavior against similar customers to identify outliers
Risk Dashboards
Interactive visualizations for portfolio risk monitoring and trend analysis
Anomaly Detection
Unsupervised learning identifies unusual patterns without predefined rules
Included Modules
Platform components that power this solution
| Module | Included | Purpose |
|---|---|---|
Risk Assessment Engine |
AI-powered risk scoring and analytics | |
Transaction Monitoring |
Behavioral pattern analysis and anomaly detection | |
Alert Management |
Risk-based alert prioritization | |
Regulatory Reporting |
Risk dashboards and trend reporting | |
Case Management |
Optional |
Optional: Deep-dive investigation on high-risk entities |
Sanctions & Watchlist Screening |
Optional |
Optional: Watchlist and adverse media screening |
Intelligence Workflow
From data to actionable insights
Data Ingestion
Collect and integrate data sources
Transaction data
Customer profiles
External data sources
Step 1
Model Training
Build behavioral baselines
Pattern recognition
Peer group analysis
Network mapping
Step 2
Anomaly Detection
Identify deviations from normal
Behavioral anomalies
Network anomalies
Risk score changes
Step 3
Alert Generation
Create prioritized alerts
Risk-based prioritization
Context enrichment
Analyst routing
Step 4
Insights
Strategic risk understanding
Portfolio risk views
Trend analysis
Predictive insights
Step 5
Key Benefits
Find Hidden Risks: AI uncovers complex patterns and relationships that rule-based systems miss
Proactive Detection: Predictive models identify emerging risks before they become problems
Reduce False Positives: Behavioral baselines reduce alerts on legitimate unusual activity
Network Visibility: See connections between customers, accounts, and transactions
Strategic Insights: Portfolio-level analytics support risk appetite decisions
Continuous Learning: Models improve over time as they learn from new data and analyst feedback
Integration Points
Inputs
Core banking systems
Transaction systems
CRM/customer data
External data providers
Outputs
Alert management
Case management
BI/Analytics platforms
APIs
Risk scoring API
Network analysis API
Anomaly detection API
Scheduled Jobs
Model retraining
Batch scoring
Network refresh
Performance Metrics
85%
Detection Rate
Suspicious activity detected by AI models
95%
Prediction Accuracy
High-risk customer prediction accuracy
3x
Efficiency Gain
Improvement in analyst productivity
24/7
Monitoring
Continuous real-time risk monitoring
Use Cases
Network Analysis
Discover hidden connections between entities to identify money laundering networks
Peer Group Comparison
Identify customers with unusual behavior compared to similar peers
Emerging Risk Detection
Proactively identify customers likely to become high-risk