Family violence is a harsh reality in Australia. Figures show that one in four children is exposed to Family violence, and two in five assaults reported to police in 2016 were family violence-related.

Peter Ship, Senior Industry Consultant of SAS Public Security, South Asia, notes that “one of the reasons Australia has such high statistics is that there is a will by the authorities to address this issue, and the first step is to solving this problem is to ensure that incidents are reported”.

Ship argues that unrealistically low numbers of reports are not a sign that all is well – on the contrary, it shows that authorities are not taking the problem seriously, he tells GovInsider.

Once the scale of a problem is understood, the next step is to reduce the actual instances of offences. This can only happen through the sharing of data by all government agencies. There can be “a very big reluctance” among agencies to share information until a significant incident is identified, and an investigation is commenced. But by then, “we’ve let it happen and some one has been hurt”, says Ship, who previously spent 30 years with London’s Metropolitan Police Service.

How can governments protect the most vulnerable among us? Pulling information from across education, health and social services agencies can help governments spot signs of violence earlier, he explains.

Spot the red flags

There are always tell-tale signs to physical abuse and violence in the home, he says. Some of the red flags include: children turning up at school dirty and tired; mothers showing up at the doctor’s with unexplained injuries; and fathers being caught for drink driving and related offences.

Each of these incidents, taken separately, may not be identified as an indicator of a much bigger problem. “Many of these ‘soft’ alerts will not be shared with the police,” Ship points out, “until they accumulate, and by then, it is too late for the family.”

Data analytics can help by scoring each incident as a “soft alert”, which can be coloured or numbered. “These are things that on their own would not necessarily trigger a response, but when they are put together they highlight significant risk which means we can start spotting these issues much earlier,” Ship explains. “Hopefully, we will eventually be able to spot those at risk and intervene before people are hurt.”

Insights into individual families

SAS analytics platform is able to pull together data from various systems – education, healthcare, police, social welfare and schools – to provide insights into individual families and any reported incidents. Such insights could go a long way towards boosting early detection and intervention, Ship believes. It would allow governments to “investigate something at a much earlier stage than would have happened before, and not waiting until there is serious violence before taking action,” he explains.

This way, agencies can cut costs, and more importantly, there will be better outcomes for all involved. With early action, there may not be a need for an expensive prosecution, imprisonment, or removing a child from their home, Ship continues.

Sometimes, it is difficult to tell what happens behind closed doors, but there are ways to detect wrongdoing. The police now have another tool to help them identify vulnerable individuals in need – before it is too late.

Peter Ship has headed the SAS Public Security Unit for South Asia since September 2013. Over the past ten years, Ship has worked with a number of police forces and intelligence agencies across the world, helping them to develop and maximise their intelligence systems.

Ship had a notable 30-year career with London’s Metropolitan Police Service, most of which was spent within New Scotland Yard’s Specialist Crime Directorate managing intelligence-led operations. During his last three years as a police officer, Ship led the strategic review of intelligence systems in MPF, which led to major changes including the development and implementation of the SAS ‘intelligence platform’ across the force.

Image by allenran 917CC BY 2.0