In a move to modernize law enforcement systems, the Ministry of Home Affairs has announced plans to integrate artificial intelligence into the upgraded Crime and Criminal Tracking Network and Systems (CCTNS 2.0). The platform currently connects 17,000 police stations across the country.
The upgrade aims to introduce advanced AI capabilities to support crime detection and data analysis. This update was shared with a parliamentary panel as part of a report by the Standing Committee on Communications and Information Technology for 2024–25, chaired by Nishikant Dubey.
According to the National Crime Records Bureau, AI will be used for tasks such as entity resolution and criminal profiling. The system will link multiple cases to a single suspect and create consolidated profiles using FIR data from different police stations.
The ministry highlighted that AI is increasingly being used to analyse large datasets, detect anomalies, and identify patterns. These capabilities are expected to improve intelligence gathering, support counter-terrorism efforts, and enhance decision-making speed and accuracy.
The Indian Cyber Crime Coordination Centre is also applying AI to detect online crimes. In collaboration with IIT Bombay, efforts are underway to analyse behavioural and transactional patterns to identify mule accounts and assign suspect scores.
Authorities are also exploring real-time suspect scoring models in partnership with the Reserve Bank Innovation Hub. These systems aim to help banks flag and prevent fraudulent transactions.
Additional AI applications include automatic number plate recognition and facial recognition through CCTV systems to identify suspects and manage traffic. The ministry also mentioned tools that can extract relevant legal sections from FIR content using custom AI models.
AI is further expected to support predictive policing through risk terrain modelling, which combines crime history with social and local factors to identify potential crime locations. It may also help detect repeat offenders and generate risk scores based on compiled data.
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