The fashion industry coined the term “the new black” during the 1980s. Every wardrobe contains something black to suit all occasions, but with each new season changing tastes throw up a new item just as versatile and must-have – the new black. Social services systems, while somewhat more stable than the fashion industry, are based on established social norms. While social programs are largely enduring, they must adapt to changing social economic times such as increased participation in the labour market, ageing populations and digital disruption.
For example, the Australian social services system provides a comprehensive safety net for people when they experience social risk. In an ideal world the system would prevent people falling into the net in the first place, or failing that, from falling deeper into the net once they experience the need to use it. Social services systems around the world have been slow to adapt from a reactive to a proactive business model. An adaptive system needs a preventative component in its wardrobe – the new black for social services.
The labour accident arm of social protection on the other hand, has long recognised the tangible benefits of a preventative model. Insurers and employers collectively realised that to stop rising premiums they had to stop work injuries from happening. Regulated occupational health and safety had arrived.
In 1974, Vladimir Rys who went on to become the Secretary General of the International Social Security Association (ISSA) argued “a forward-looking social policy must … include the prevention of social risks as one of its major preoccupations, for it is obvious that through investment in prevention, society is gaining from the social as well as from the economic point of view.”
Forty years later in 2007, the ISSA released the dynamic social security framework, suggesting social security organisations be “better providing forward-looking responses, including activating, proactive, preventive and integrated ones, to emerging challenges.”
Prevention is not about removing all social risk (which would be socially and economically undesirable). Rather, it is to ensure that when people touch the safety net, they can spring out quickly and not fall deeper and/or through the net entirely into a cycle of long-term disadvantage. Unfortunately for some people, once in the system they are at increased risk of getting caught in a vicious cycle of disadvantage. While most systems are designed to never abandon people, it can be very hard for some to find a way out.
An important aspect of prevention is identifying people with increased risk of getting caught in a vicious cycle and acting early with interventions and supports. When people touch the social security system, they can be identified as either low-risk and offered minimal support/low touch or medium-to high-risk where interventions are required.
Many social services agencies, however, treat people the same way irrespective of individual risk. Digital data and predictive analytics is changing how service offers can be tailored in real-time according to risk, giving rise to a differential service response. A differential service response provides the opportunity for organisations to hit both program efficiency and effectiveness targets.
Digital data and predictive analytics enables automated decisions for empowering a majority of people to self-manage or flagging cases requiring intervention. This differentiated service response provides capacity to support investment in prevention, such as social investment initiatives, social impact bonds/pay for success and a universal basic income.
When social risk is identified, dynamic social security systems determine “what actions to take (policy regarding appropriate benefits and services), who takes the action (social security organisations, other state entities, individuals or other stakeholders), how the intervention occurs (which touched on the administration of benefit delivery and management implications) and when the action should be taken – i.e. such intervention should occur at the ‘right time’.”
Predictive analytics provide the foundation for a prevention based approach, the new black for social services. As business processes are further digitalised and automated, social services organisations can dynamically measure outcomes and adjust social programs to improve efficacy. This combination of outcome measurement and dynamic programs can enable policy makers to focus their attention on preventative interventions designed to keep people from falling deeper into the safety net.