AI-Powered Criminal Network Analysis System
Ministry of Home Affairs · Blockchain & Cybersecurity · Software
The link-analysis concept is valuable and demos well, but you synthesise the investigation data so the network only shows what you built in, and commercial tools already exist — make robust entity extraction from messy text your real contribution, since that is the actual bottleneck.
What it actually is
Criminals operate through networks of associates, money, phones and locations, and investigators have the data — FIRs, call records, financial records, surveillance — but it is fragmented and manual analysis misses connections. The ask is a system that extracts entities from these varied sources, builds a relationship graph, identifies key players, and flags suspicious patterns to help investigators see the network.
What to build
A criminal-network analysis system that ingests structured and unstructured law-enforcement data — FIRs and reports, call detail records, financial transactions, surveillance notes — extracts entities like people, phone numbers, vehicles, locations and organisations via NLP, builds a relationship graph linking them across sources, identifies central and influential individuals through graph centrality, detects suspicious patterns and unusual activity, and presents the network visually with analytical insights so an investigator can explore how suspects connect and who the key nodes are.
Smallest thing that wins the room
Ingest a set of mock FIRs and call records, extract the people, numbers and locations, build the relationship graph, and highlight the central individual connecting otherwise separate clusters with the suspicious link pattern that reveals them, all explorable on the graph.
How crowded this one gets
A guess, projected from the 2025 statements — the last year where both the submission counts and the winners were published.
Quieter than 78% of the 226 · #50 of 226 by expected field
Few teams are likely to go here. The best odds on the board come from statements like this.
Why: defence, intelligence and space bodies drew small fields.
This is a guess, not a fact
Nobody has published 2026’s numbers yet. This is an analysed estimate from last year’s pattern, so please do not take it as the truth — check the live counter on the SIH portal before you decide anything. The range covers the middle half of likely outcomes, so one statement in two lands outside it. Entry closes at 500 ideas per statement, so no range goes past that — a statement that reaches the cap fills and shuts rather than drawing an unlimited crowd. The model reads only three things a team can see before choosing — software or hardware, the theme, and what kind of body posted it — and those explain about a quarter of the variation in last year’s field sizes (R² 0.25 on held-out statements). Trust the band more than the number, and the ordering more than either. It cannot see how good your idea is, which is the part that actually decides it.
The scores
The number is the shorthand. The line under it is the reason.
Acceptance potential
3/5The link-analysis concept is genuinely valuable and demos well, but tools like this already exist commercially, you must synthesise the investigation data so the network only reveals what you built in, and the entity-extraction accuracy on messy real reports — the actual bottleneck — cannot be shown on clean synthetic data.
Feasibility
3/5Entity extraction, graph construction and centrality analysis are standard, but there is no real criminal-investigation dataset so you synthesise FIRs and call records, and the value of the network analysis depends heavily on how realistic and interconnected that synthetic data is.
Innovation scope
3/5Entity extraction plus graph analytics is an established pattern, so your room is in the multi-source fusion and the suspicious-pattern detection rather than in the concept, which the description largely specifies.
Clarity
4/5The data sources, the entities to extract, the relationship-mapping, key-individual and pattern-detection requirements and the visual output are enumerated clearly, so the target is well defined even without data.
Effort
MassiveMulti-source ingestion, NLP entity extraction, graph construction, centrality and pattern detection and an interactive visualisation is a broad, multi-part build.
Demo-ability
EasyA relationship graph revealing the central individual linking separate clusters is a compelling, investigative-looking demo that reads instantly.
In its favour
- Green flag: Entity extraction and graph analytics are mature with strong libraries, so the core is buildable and reliable
- Green flag: A graph revealing the central connector between clusters is a compelling, self-explanatory investigative demo
- Green flag: Multi-source fusion linking phone, financial and report data is genuinely useful and the real value beyond single-source analysis
- Green flag: Graph centrality gives a principled, defensible way to surface key individuals
Against it
- Red flag: No real investigation dataset exists, so you synthesise FIRs and call records and the network only reveals the connections you built in
- Red flag: Commercial link-analysis tools already do this, so the novelty bar for a student build is high
- Red flag: Entity extraction from messy real police reports is the actual bottleneck and cannot be shown on clean synthetic text
- Red flag: Automatically flagging individuals as key criminal actors carries real consequences if the extraction or linking is wrong
What you will be writing
- NLP entity extraction from reports (spaCy / LLM)
- Neo4j / NetworkX relationship graph
- Graph centrality for key-actor identification
- Call detail record and transaction linking
- Suspicious-pattern / community detection
- Link-analysis visualisation (Cytoscape.js)
- Criminal intelligence
- Graph analytics
- Investigative tooling
Prior art to read before you start
entity extraction and relationship mapping · criminal network key-actor detection · multi-source investigative link analysis
Analysed by Claude Opus. Every score above is a judgment call with its reasoning attached — kindly cross-check this against the official statement on the SIH portal before your team commits to it.