How predictive modelling can future-proof the public sector

Spiralling inflation, rising energy bills and an escalating cost of living crisis have left millions of people struggling to afford basic necessities; could future predictive modelling stop this from happening again

These circumstances have fostered a greater dependency on public sector services, with demand for service provision continuing to rise.

With the public sector under significant strain, it’s clear that organisations need to be able to respond to ever-increasing demand, while also meeting citizen expectations for fast and efficient service delivery.

Public sector organisations typically function on more of a reactive basis, where they respond to citizens when they are usually already in, or on the verge of, crisis. However, the past two years have shown that the sector needs to adopt a more proactive approach, whereby organisations can anticipate changes in the market and adapt their services accordingly. But how can this be achieved?

Predictive modelling could be the key to transforming public sector organisations to become more responsive and resilient to change in the future. By enabling organisations to identify and predict potential future changes in the market, predictive modelling and simulation allow organisations to prepare their services accordingly while putting the citizen at the heart of the decision-making process. This will enable the public sector to become more foresighted, data-driven and proactive in the months and years ahead.

Predictive modelling explained

Put simply, predictive modelling is a technique that analyses historical and current data to predict future outcomes. It works by identifying specific correlations or patterns in the data and using this information to forecast the likelihood of future events.

While the use of predictive modelling in the public sector has remained slightly behind that of its deployment in the private realm, its wider implementation could enable public sector organisations to assess potential risks and future crunch points for their services. This would mean that they could prepare their resources and employee capacity in advance of an event occurring, allowing them to respond to citizens faster and before they reach the point of crisis.

Public sector organisations are sitting on an abundance of historic and real-time data, so how can they leverage it to ensure the efficient delivery of citizen services and thus become more adaptable and resilient to change?

Helping the most vulnerable

While the economy has slowly begun to show signs of recovery in the aftermath of the Covid-19 pandemic, certain socioeconomic pressures, such as the cost of living crisis, have led to more and more citizens becoming reliant on the public sector and the services it provides. 

Applying predictive modelling techniques such as machine learning algorithms can enable organisations to reliably predict when and which citizens will fall into hardship, and offer appropriate assistance to prevent this from happening. A key example of this was the deployment of machine learning technology in the State of Queensland, Australia.

This has been able to predict with more than 70% accuracy when a land taxpayer may default on their debt, allowing the organisation to remind citizens when their fine needs to be paid. This has not only enabled the government to allocate more revenue to other essential services, but also proactively offer assistance to vulnerable citizens before reaching the point of crisis.

Predictive modelling can be used to simplify the benefits claimant process in the UK, enabling public sector organisations to predict the likelihood of a new claimant reaching long-term unemployment, and provide necessary support early on to those deemed high risk. This reflects how predictive modelling can allow public sector organisations to prepare for future challenges before they arise, while also placing citizen welfare at the heart of long-term decision-making.

Improving resource allocation

Prioritising the effective allocation of resources will also improve the public sector’s ability to respond to increased demand for services. This is particularly important given the pressure the public sector has come under over the past couple of years, with the Covid-19 pandemic exacerbating the strain on service provision for countries worldwide.

By using data to forecast when a future event might occur, predictive modelling and simulation can enable public sector organisations to distribute and allocate resources where they are needed well in advance of a crisis arising. This will improve the public sector’s ability to respond to future changes in the market by reducing the long-term impact of crunch points on service provision. An important example of this was the development of QCovid, an evidence-based risk protection model, to predict demand for NHS services during the Covid-19 pandemic. This was able to estimate a patient’s combined risk of catching Covid-19 and becoming seriously ill, allowing hospitals to reliably predict and prepare for the number of patients being admitted to the hospital ahead of periods of high demand.

In a rapidly evolving socio-economic landscape, the ability to react and respond to change will form a crucial part of future-proofing the public sector in the months and years ahead. Wider implementation of technology like predictive modelling will allow public sector services to improve capacity and prioritise resources well in advance of a crisis occurring, while also placing citizens at the centre of the services they provide.

Information provided by Satpal Biant, Head of Public Sector at SAP UK&I