Individual Risk Scoring and Targeting

Individual risk scoring predicts what a named person will do. It is distinct from place-based prediction, which forecasts where crime will occur, and the difference matters: a place cannot be stigmatised, arrested or refused bail.

The category covers police force models scoring individuals for likelihood of offending or of becoming a victim, multi-agency systems combining police, council and social data, and central government research programmes.

WHAT IS ACTUALLY DEPLOYED

This site's tracker records several variants. Avon and Somerset Police operate predictive analytics models scoring individuals. Bristol's Think Family Database combined data across agencies to identify families for intervention and was discontinued as not fit for purpose. New South Wales operated the Suspect Target Management Plan, which scored individuals for proactive policing attention and was discontinued after sustained criticism. Durham's HART, covered on its own page, classified arrestees for diversion.

The UK Ministry of Justice, with the Home Office, Greater Manchester Police and the Metropolitan Police, developed a model originally titled the Homicide Prediction Project and since renamed. A data sharing agreement covered between 100,000 and 500,000 people including names, dates of birth, ethnicity and gender. Documents obtained by Statewatch record it as completed in December 2024 but not deployed.

Where sources conflict, both positions are recorded: Statewatch states sensitive data including domestic abuse and self-harm information was used, covering people never convicted; officials deny this, stating only data from people with at least one conviction was used.

THE PATTERN WORTH NOTICING

Of the individual-scoring systems documented on this site, a striking proportion have been discontinued: the Bristol database, NSW STMP, HART. Others were developed and never deployed.

That is a different trajectory from facial recognition or plate reading, which have expanded. It suggests individual risk scoring is harder to sustain in operation than in prospect — because the outputs are contestable, because the consequences attach to named people, and because a score is difficult to defend when the person asks why they are being visited.

THE CASE FOR IT

Police already make judgements about which individuals warrant attention, and those judgements are currently unstructured, undocumented and shaped by whoever happens to be in the room. A model at least applies consistent criteria and leaves a record.

Some applications are protective rather than punitive. Scoring for risk of victimisation — in domestic abuse, child exploitation or serious youth violence — directs support rather than enforcement, and the tools on this site with the clearest social case are of that type.

Multi-agency data combination also addresses a real failure. Serious case reviews repeatedly find that information held separately by police, health and social services would, if combined, have identified a child at risk.

THE CASE AGAINST

The feedback problem is more acute here than for place-based prediction. A model trained on prior police contact identifies people who have been policed before, and directing attention to them generates further contact, which confirms the score. The person cannot escape the classification by behaving lawfully, because the input is police attention rather than offending.

Consequences attach without process. A person on a targeting list is not charged with anything, has no hearing, is generally not told, and has no route to challenge the classification.

The Ministry of Justice's own finding that OASys algorithms had greater predictive validity for white offenders than for offenders of Asian, Black and Mixed ethnicity applies to this category directly.

And the protective framing does not neutralise the objection. A family identified for intervention by an algorithm experiences state attention it did not seek, on the basis of a calculation it cannot see.

WHAT IS NOT ESTABLISHED

How many UK forces currently operate individual risk scoring, and under what models, is not centrally recorded.

Whether individuals are ever informed they have been scored, in any deployment reviewed, is not established.

No independent evaluation comparing outcomes for scored individuals against comparable unscored individuals has been identified.

The reasons for discontinuation of several of these systems are documented only partially, which limits what can be learned from them.