Fraud Detection & Security

Protect Your Organization with AI-Powered Threat Intelligence

Kaman's Intelligent Memory and pattern recognition capabilities provide powerful tools for detecting fraud, identifying security threats, and protecting your organization from both external attacks and internal risks. Discover anomalies before they become incidents.

The Security Challenge

Organizations face sophisticated and evolving threats:

How Kaman Detects Threats

Intelligent Pattern Recognition

The core of Kaman's fraud detection capability is its Intelligent Memory system, which automatically discovers patterns and relationships in your data:

What Gets Detected

Detection Category	Example Patterns
Account Takeover	Unusual login locations, device changes, behavior shifts
Payment Fraud	Transaction anomalies, velocity violations, network patterns
Insider Threats	Access pattern changes, data exfiltration indicators
Identity Fraud	Synthetic identity indicators, application anomalies
Compliance Violations	Policy breaches, unauthorized activities

Auto-Discovered Relationships

Kaman's ontology discovery reveals hidden connections:

Discovered Patterns:

Accounts sharing devices or addresses
Communication networks between entities
Transaction flow patterns
Time-based activity correlations
Behavioral similarity clusters

Detection Capabilities

Real-Time Monitoring

Catch threats as they happen:

Behavioral Analytics

Understand normal to detect abnormal:

Behavior Type	Baseline Elements	Anomaly Indicators
Access	Typical times, locations, devices	Off-hours access, new locations
Transaction	Amount patterns, frequency, recipients	Unusual amounts, new recipients
Data	Access patterns, volume	Mass downloads, sensitive queries
Communication	Normal contacts, tone	New external contacts, urgency

Network Analysis

Uncover fraud rings and collusion:

Response Automation

Automated Response Actions

React to threats instantly:

Threat Level	Automatic Response
Critical	Block activity, alert security team, preserve evidence
High	Suspend pending review, notify stakeholders
Medium	Flag for review, add monitoring
Low	Log, continue monitoring

Investigation Support

Accelerate investigations:

Investigation Features:

Automatic context assembly
Timeline visualization
Related event discovery
Evidence preservation
Report generation

Case Management

Manage security incidents systematically:

Incident tracking
Assignment and workflow
Documentation requirements
Resolution tracking
Post-incident analysis

Use Case Applications

Financial Fraud Detection

Protect against financial crimes:

Detection Capabilities:

Payment fraud patterns
Account takeover attempts
Money laundering indicators
Synthetic identity fraud
Collusion networks

Example Patterns Detected:

Unusual transaction velocities
Geographic impossibilities
Device fingerprint anomalies
Behavioral profile deviations

Insider Threat Detection

Identify internal risks:

Indicators Monitored:

Access pattern changes
Data movement anomalies
Off-hours activity
Policy violations
Resignation risk factors

Cybersecurity Applications

Enhance security operations:

Access anomaly detection
Privilege escalation monitoring
Data exfiltration detection
Credential compromise indicators
Lateral movement tracking

Compliance Monitoring

Ensure policy adherence:

Policy violation detection
Unauthorized access attempts
Segregation of duties violations
Data handling compliance
Regulatory requirement monitoring

Benefits

Detection Effectiveness

Metric	Typical Improvement
Detection Rate	40-60% more threats detected
False Positives	50-70% reduction
Time to Detect	Minutes vs. days
Coverage	100% of monitored activity

Operational Efficiency

Metric	Typical Improvement
Investigation Time	60-80% reduction
Analyst Capacity	Handle 3-5x more cases
Response Time	Real-time vs. batch
Documentation	Automated evidence collection

Business Impact

Benefit	Impact
Loss Prevention	Reduced fraud losses
Compliance	Demonstrated monitoring
Reputation	Protected brand trust
Insurance	Better risk profile

Implementation Approach

Phase 1: Foundation

Data Integration
- Connect activity data sources
- Establish data quality baselines
- Configure retention policies
Baseline Establishment
- Historical pattern analysis
- Normal behavior profiling
- Risk categorization

Phase 2: Detection

Rule Configuration
- Business rules implementation
- Threshold setting
- Alert routing
ML Enablement
- Model training
- Anomaly detection activation
- Pattern recognition tuning

Phase 3: Optimization

Response Automation
- Automated response rules
- Investigation workflows
- Reporting automation
Continuous Improvement
- False positive reduction
- Model refinement
- Coverage expansion

Transparency & Governance

Explainable Detection

Understand why alerts are generated:

Clear alert reasoning
Contributing factors displayed
Confidence levels shown
Supporting evidence linked

Audit Trail

Complete documentation:

All alerts logged
Investigation actions recorded
Decisions documented
Outcomes tracked

Privacy Considerations

Balanced monitoring:

Purpose limitation
Proportional monitoring
Data minimization
Employee notification where required
Access controls on sensitive data

Getting Started

Assessment Questions

What are your highest-risk fraud scenarios?
What data sources are available for monitoring?
What is your current detection capability?
What compliance requirements apply?
What is your response capability?

Quick Wins

Start with high-impact, low-complexity detections:

Known fraud patterns
Policy violation monitoring
Access anomalies
Transaction velocity

Building the Program

Expand methodically:

Add data sources
Refine detection models
Automate responses
Integrate with security operations

Fraud Detection & Security - Proactive protection, intelligent response