Solutions · Module 04 · Risk Intelligence

Risk Intelligence

Operational

AI-powered analytics to detect patterns and unusual behaviors

Scope · Behavioral Network Predictive Insights
// Overview

Overview

Advanced analytics for comprehensive risk understanding

// the brief

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.

amlshield · behavioral analyticslive
Behavioral risk69 / 100
Elevated
Anomalies · 24hBehavioral 14Network 63Cleared
amlshield · intelligence scopeonline
AnalyticsBehavioralNetwork
PredictivePeer group
LearningContinuous
// Features

Key Features

Advanced analytics for comprehensive risk understanding

F.01

Behavioral Analytics

Machine learning models establish baselines and detect deviations from normal patterns

F.02

Network Analysis

Graph analytics reveal hidden relationships and money flow patterns between entities

F.03

Predictive Modeling

Forecast future risk levels to enable proactive risk management

F.04

Peer Group Analysis

Compare customer behavior against similar customers to identify outliers

F.05

Risk Dashboards

Interactive visualizations for portfolio risk monitoring and trend analysis

F.06

Anomaly Detection

Unsupervised learning identifies unusual patterns without predefined rules

// Modules

Included Modules

Platform components that power this solution

Modules 4 included · 2 optional
Risk Assessment Engine AI-powered risk scoring and analytics incl
Transaction Monitoring Behavioral pattern analysis and anomaly detection incl
Alert Management Risk-based alert prioritization incl
Regulatory Reporting Risk dashboards and trend reporting incl
Case Management Optional: Deep-dive investigation on high-risk entities opt
Sanctions & Watchlist Screening Optional: Watchlist and adverse media screening opt
amlshield · platform fabriconline

A single platform — one client record, one risk profile, one audit trail — shared across every module. Four components compose this solution, with case management and screening available as optional extensions.

4 included2 optional
// Workflow

Intelligence Workflow

From data to actionable insights

01

Data Ingestion

Collect and integrate data sources

Transaction data
Customer profiles
External data sources
02

Model Training

Build behavioral baselines

Pattern recognition
Peer group analysis
Network mapping
03

Anomaly Detection

Identify deviations from normal

Behavioral anomalies
Network anomalies
Risk score changes
04

Alert Generation

Create prioritized alerts

Risk-based prioritization
Context enrichment
Analyst routing
05

Insights

Strategic risk understanding

Portfolio risk views
Trend analysis
Predictive insights
// Benefits

Key Benefits

What an institution gains from AI-driven risk intelligence on one platform.

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
// Integrations

Integration Points

How the solution connects to your existing systems — inputs, outputs, APIs, and scheduled jobs.

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
Models are trained on your institution's own data and tuned for specific risk types, customer segments, and regulatory requirements — with full audit trails retained for examinations.
// evidence

Performance Metrics

Indicative outcomes when risk intelligence is AI-assisted on one platform.

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
Outcomes vary by deployment & data·full audit trail retained

Ready to uncover hidden risks?

See how AI-powered risk intelligence can transform your compliance program

Request a Demo
// Use Cases

Use Cases

Where institutions apply risk intelligence across their monitoring posture.

UC/01

Network Analysis

Discover hidden connections between entities to identify money laundering networks

UC/02

Peer Group Comparison

Identify customers with unusual behavior compared to similar peers

UC/03

Emerging Risk Detection

Proactively identify customers likely to become high-risk

// FAQs

Frequently Asked Questions

The questions compliance and data teams ask before deployment.

How does behavioral analytics work?

Machine learning models analyze historical transaction patterns to establish a behavioral baseline for each customer. Future activity is compared to this baseline to detect deviations.

What types of networks can be analyzed?

We analyze multiple network types including transaction flows, shared identifiers, beneficial ownership chains, and relationship networks to reveal hidden connections.

How accurate are the predictive models?

Model accuracy depends on data quality and volume. Typically we see 80-90% accuracy in identifying customers who will become high-risk within 6-12 months.

How does network analysis work?

Graph algorithms analyze connections between entities through shared identifiers, transaction flows, and relationship data to reveal hidden networks.

What data is needed for AI models?

The models work best with 12-24 months of historical transaction data, customer profiles, and outcome labels from previous investigations.

Can models be customized?

Yes, models are trained on your institution's data and can be tuned for specific risk types, customer segments, or regulatory requirements.

Contact Sales

Ready to uncover hidden risks?

See how AI-powered risk intelligence can transform your compliance program