Crime Linkage Analysis
Crime Linkage Analysis focuses on determining whether multiple criminal incidents are related through common offenders, groups, or patterns of behavior. Instead of viewing each incident in isolation, this application connects cases based on shared characteristics such as modus operandi, location, timing, and network relationships among suspects and victims. The goal is to surface linked crimes, reveal hidden structures like co‑offending networks or gangs, and prioritize investigations more effectively. AI enhances this area by learning similarity patterns between incidents and modeling social networks of offenders and victims. Techniques such as Siamese neural networks and social network analysis help automatically flag likely linked crimes, identify high‑risk groups, and expose influential actors within criminal networks. This enables law enforcement and public‑safety agencies to allocate investigative resources more efficiently, disrupt organized crime, and design targeted prevention and victim support strategies.
The Problem
“Connect related crimes and networks across cases to accelerate investigations”
Organizations face these key challenges:
Linking cases relies on manual analyst intuition and inconsistent criteria across units
Key patterns are buried in narrative reports and disparate systems (RMS/CAD/jail intel)
Too many potential links create noise; investigators miss true series and prolific offenders
Network views (co-offending/gangs) are incomplete or stale, delaying disruption actions
Impact When Solved
The Shift
Human Does
- •Searching through narrative reports
- •Building ad-hoc link charts
- •Making linkage decisions based on intuition
Automation
- •Basic keyword matching
- •Manual data entry
- •Simple pattern recognition
Human Does
- •Reviewing AI-generated link suggestions
- •Making final linkage decisions
- •Providing context and insights from investigative experience
AI Handles
- •Scoring potential case links
- •Identifying hidden networks
- •Analyzing spatiotemporal patterns
- •Generating comprehensive linkage reports
Solution Spectrum
Four implementation paths from quick automation wins to enterprise-grade platforms. Choose based on your timeline, budget, and team capacity.
Analyst-Guided Linkage Triage Console
Days
Behavioral Similarity Link Finder
Siamese MO Linkage Scoring Engine
Autonomous Series Discovery and Network Disruption Planner
Quick Win
Analyst-Guided Linkage Triage Console
Stand up a lightweight analyst console that supports filtered search across incidents and highlights candidate links using simple similarity heuristics (shared MO codes, distance bands, time windows, shared entities). Analysts can quickly shortlist candidate related incidents and export a linkage packet for case review. This level validates demand, defines linkage criteria, and establishes baseline metrics without heavy modeling.
Architecture
Technology Stack
Key Challenges
- ⚠Data quality issues (duplicate incidents, inconsistent MO coding, messy addresses)
- ⚠High false positives from simple similarity rules
- ⚠Handling sensitive data access and audit logging even in a prototype
- ⚠Establishing a baseline metric for 'useful candidate links'
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Market Intelligence
Real-World Use Cases
Using social network analysis to understand crime and victimisation
This is like drawing a big map of who knows who in a city, then using math to see which people or groups are at the centre of crime activity or at highest risk of becoming victims. Instead of only looking at individual incidents, it looks at the web of relationships around them.
Enhancing Binary Encoded Crime Linkage Analysis Using Siamese Network
This is like giving police a smarter ‘pattern-matching’ assistant that looks at the details of different crimes and learns which ones were likely committed by the same offender, even when the clues are encoded as simple yes/no (binary) fields in a database.