Using AI for Crime Analysis in Australian Policing (2026)
How Australian police forces are using AI for crime analysis — pattern detection, hotspot mapping, intelligence analysis, and digital forensics — with a clear-eyed view of the ethical considerations and the limits of predictive policing.
Crime analysis is one of the most established applications of AI in policing. Police forces have been using data analysis tools to identify crime patterns, allocate resources, and support investigations for many years. AI and machine learning are making these tools more powerful — and raising important questions about how they should be used.
Crime Pattern Analysis
Hotspot Mapping
Hotspot mapping uses crime data to identify geographic areas with elevated crime rates. AI tools can analyse crime data to identify hotspots more accurately and update them in near-real time as new incidents are recorded.
Australian police forces use crime mapping tools that incorporate AI features. The specific tools vary by jurisdiction, but most state police forces use GIS-based crime mapping systems.
Hotspot mapping is used to inform patrol deployment decisions — directing officers to areas where crime is concentrated. Research evidence suggests that focused patrol in crime hotspots can reduce crime in those areas, though the evidence on displacement effects is mixed.
Temporal Pattern Analysis
AI tools can identify temporal patterns in crime data — when crimes are most likely to occur, how crime rates vary by day of week and time of day, and how seasonal factors affect crime rates. This information supports shift scheduling and resource allocation decisions.
Crime Series Detection
AI tools can help analysts identify crime series — groups of offences that are likely to have been committed by the same offender or group of offenders. By analysing patterns in crime data — location, time, method, target type — AI tools can flag potential series for analyst review.
This is one of the most valuable applications of AI in crime analysis, as identifying crime series can significantly improve the efficiency of investigations.
Intelligence Analysis
Link Analysis
Intelligence analysis often involves identifying connections between people, organisations, events, and locations. AI tools can help analysts identify these connections in large datasets — flagging relationships that might not be apparent from manual analysis.
Tools like i2 Analyst's Notebook (IBM) and Palantir are used by Australian law enforcement agencies for link analysis.
Open Source Intelligence (OSINT)
AI tools can help analysts collect and analyse open source information — from social media, news sources, and public databases. This includes tools for monitoring social media for relevant content, analysing large volumes of text, and identifying patterns in publicly available data.
Consideration: OSINT activities must comply with the Privacy Act 1988 and relevant legislation. The collection and use of personal information from open sources requires appropriate legal authority and oversight.
Digital Forensics
Mobile Device Analysis
Mobile devices contain large volumes of potentially relevant evidence — messages, call records, location data, photos, and app data. AI tools can help forensic analysts process this data more quickly and identify relevant content.
Cellebrite is a widely used mobile device forensics platform that incorporates AI features for evidence analysis.
Large-Scale Document Analysis
Investigations involving financial crime, corruption, or organised crime often involve large volumes of documents — emails, financial records, contracts. AI tools can help analysts identify relevant documents in large datasets, extract key information, and identify patterns.
Nuix is an Australian-developed platform used by law enforcement agencies globally for large-scale document analysis.
Image and Video Analysis
AI tools can analyse images and video footage to identify relevant content — faces, vehicles, objects, and activities. This includes tools for searching large volumes of CCTV footage and for identifying specific individuals or vehicles.
Consideration: The use of facial recognition technology in policing is subject to significant public debate in Australia. The Australian Human Rights Commission has called for a moratorium on the use of facial recognition in high-risk settings pending appropriate regulatory frameworks.
The Limits and Risks of Predictive Policing
Predictive policing — using AI to predict where crimes will occur or who is likely to commit crimes — is one of the most controversial applications of AI in law enforcement.
Bias and Discrimination
Predictive policing tools trained on historical crime data can reflect and amplify existing biases in policing. If historical data reflects over-policing of certain communities, AI tools trained on that data may recommend directing more resources to those communities — creating a self-reinforcing cycle.
Research from the United States has documented cases where predictive policing tools produced racially biased recommendations. Australian police forces considering predictive policing tools need to assess them carefully for potential bias.
The Limits of Prediction
Crime is influenced by complex social, economic, and environmental factors that are difficult to predict. AI tools can identify patterns in historical data, but they cannot reliably predict future crime at the individual level. Using AI predictions to make decisions about individuals — who to stop, who to investigate, who to arrest — raises serious concerns about due process and civil liberties.
Accountability
Decisions about who to investigate, who to arrest, and what charges to lay must be made by accountable human officers based on evidence, not by AI systems. AI can provide information and analysis to support these decisions, but the decisions themselves must be made by humans.
Getting the Most from Crime Analysis AI
The most valuable applications of AI in crime analysis are those that help analysts work more efficiently — identifying patterns in large datasets, flagging potential crime series, and supporting intelligence analysis. These applications augment human analytical capability without replacing human judgement.
The most problematic applications are those that attempt to predict individual behaviour or that are used to make decisions about individuals without adequate human oversight and accountability.
Stay informed
Get AI news every Friday
The AI Digest delivers the week's most important AI stories — free, in plain English.
Subscribe free →Related Articles
More Professions →
Using AI for Policy Research and Analysis in the Australian Public Service (2026)
How Australian public servants can use AI to accelerate policy research, synthesise evidence, analyse stakeholder submissions, and produce better-quality policy analysis — with guidance on maintaining rigour and accountability.
Using AI for Impact Reporting in Australian Non-Profits (2026)
How Australian non-profit organisations can use AI to improve impact measurement, reporting, and storytelling — from analysing program data and producing ACNC reports to creating compelling impact stories for donors and boards.
Using AI for Volunteer Management in Australian Non-Profits (2026)
How Australian non-profit organisations can use AI to improve volunteer recruitment, onboarding, scheduling, communication, recognition, and retention — making the most of their most valuable resource.