Using predictive modelling to enhance public service delivery

A digital face in profile against a digital background.
(Image credit: Shutterstock / Ryzhi)

In recent years, funding cuts, staff shortages and a lack of end-to-end digital transformation have placed an inordinate amount of pressure on public sector organizations and the services they provide. However, over the past few months, spiraling inflation, rising energy bills and an escalating cost of living crisis have fostered a rising dependency on the public sector. With the public sector now under significant strain, it’s imperative that organizations are able to respond to growing demand for their services, while also meeting changing citizen expectations for fast and effective service delivery.

About the author

Satpal Biant is Head of Public Sector at SAP UK&I.

Around the world, public sector organizations typically respond to citizens when they are already in, or on the verge of, crisis. However, the last few months have shown that the ability to prepare for ongoing changes in the market will form a crucial part of public service delivery going forwards. But how can this be achieved?

Predictive modelling could be the key to public sector organizations becoming more responsive and resilient to change in the months and years ahead. By allowing organizations to identify potential changes in the market, organizations will be able to prepare and adapt their services in advance of an event occurring. This will enable the public sector to respond to growing demand for its services, while also placing citizen welfare at the heart of long-term decision making.

Defining predictive modelling

In its simplest form, predictive modelling is a commonly deployed technique that analyses historical and current data in order to predict future outcomes. It works by identifying specific correlations, patterns and trends in the data, and using this information to determine 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 in the public sector could enable organizations to predict future crunch-points for their services. This would mean that they could prepare their resources and employee capacity prior to an event occurring, allowing them to respond to citizens faster and before they reach the point of crisis.

This presents a real opportunity for public sector organizations to utilize the vast amounts of historic and real-time data they hold, and leverage this to become more responsive and resilient to change. But how?

Helping vulnerable citizens

While the public sector has been under significant strain for some time, certain socio-economic pressures, such as the rising cost of living, have led to a rise in the number of citizens becoming reliant on public sector services.

Deploying predictive modelling technology such as machine learning algorithms can enable organizations to identify which citizens will fall into hardship, and offer assistance to prevent this from happening. For example, machine learning could be used to predict when a land taxpayer may fall behind on debt repayments, allowing organizations to remind citizens when their fine needs to be paid. This would reduce the likelihood of citizens falling into difficulty by proactively helping those in need.

Furthermore, predictive modelling can also be used to simplify the benefits claimant process. This can help vulnerable citizens by estimating the likelihood of a new claimant reaching long-term unemployment, enabling public sector organizations to provide early support to those categorized as high risk. This reflects how predictive modelling can allow public sector organizations to adopt a more preventative approach, whereby they proactively prepare for future challenges before they arise.

Increasing long-term capacity

Prioritizing the effective allocation and distribution of resources will also increase the public sector’s capacity to respond to growing demand for its services. This is particularly pertinent given the pressure the public sector has come under in recent years, with the Covid-19 pandemic compounding the strain on healthcare services in particular.

By using data to predict when a future event might occur, predictive modelling can allow public sector organizations to allocate resources and employee capacity where they are needed in advance of a crisis arising. This will help make the public sector more resilient to changes in the market by reducing the long-term impact of crunch-points on service provision.

For example, technology such as evidence-based risk protection models can help alleviate pressure on healthcare services by estimating the number of patients at risk of becoming seriously ill and needing further medical treatment. This will enable healthcare professionals to reliably predict the number of patients being admitted to hospital ahead of periods of high demand, and prepare their services accordingly.

With public sector organizations increasingly having to adapt their operations in line with a fast-changing socioeconomic environment, the ability to react and respond to change will prove vital to future service provision. However, thanks to technology like predictive modelling, public sector services can increase capacity and allocate resources well in advance of a crisis occurring, helping to meet rising demand for years to come.

We've featured the best CX tools.

Satpal Biant is Head of Public Sector at SAP UK&I.