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Predictive analytics: Identifying risk factors and targeting resources in community corrections

By identifying patterns and making data-driven predictions, technology can help in assessing the risk factors associated with each offender

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Predictive analytics offers a powerful tool for community corrections professionals to make more informed decisions about supervision, treatment and resource allocation.

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Editor’s note: This feature is part of Corrections1’s digital edition, “Advancing community corrections: Using technology to improve case management.” Click here to download.

Community corrections, an integral part of the criminal justice system, faces the challenge of managing limited resources while ensuring public safety and offender rehabilitation. Predictive analytics can play a transformative role in addressing these challenges.

By identifying patterns and making data-driven predictions, the technology can help in assessing the risk factors associated with each offender, enabling case managers to tailor supervision strategies and allocate resources effectively.

Brian Lovins, Ph.D., is a principal for Justice System Partners. He earned his Ph.D. in Criminology from the University of Cincinnati and is the past president of the American Probation and Parole Association (APPA). Dr. Lovins routinely helps jurisdictions understand their local systems, helps stakeholders analyze and interpret their data and provides practical, real-world solutions to addressing today’s justice system challenges.

“Predictive analytics is one mechanism that we could utilize to drive better behavior at the individual level as well as at the organizational level,” Lovins says.

Predictive analytics offers a powerful tool for community corrections professionals to make more informed decisions about supervision, treatment and resource allocation. By leveraging historical data and cutting-edge algorithms to anticipate future events and trends, it ultimately can lead to more effective outcomes.

“Predictive analytics allows us to see patterns and then start to allow us to predict those patterns so that we can disrupt them or improve them,” Lovin notes.

Risk assessment tools: A critical component

Risk assessment tools, powered by predictive analytics, are at the forefront of this data-driven revolution in community corrections. These tools analyze vast amounts of data, including an offender’s criminal history, demographics, behavior and other relevant factors, to predict potential risks and outcomes.

“We collect a lot of information,” Lovins notes. “We see the number of people put on probation and revocation rates and technical violations. There’s so much more power in predictive analytics to truly dive into our data and see across trends, to start to see patterns of behavior and risk in ways human beings can’t see patterns.”

These risk assessment tools enable case managers to identify patterns and trends that may not be apparent through traditional methods. By harnessing the power of predictive analytics, community corrections professionals can gain valuable insights into offender behavior and make more targeted interventions.

“We can start to garner risk factors or identify key factors that we can’t necessarily obtain through a conversation but could be obtained more through a behavioral analysis. For example, we built a tablet app to predict the risk of reoffending by playing games that focused on risk-taking and empathy. We would then use the results of the games to correlate with risk factors.”

Improving supervision and treatment strategies

The insights derived from predictive analytics can significantly enhance the supervision and treatment strategies used in community corrections. Case managers can use these insights to make data-driven decisions about the level of supervision required for each offender and the most appropriate treatment programs.

For agencies large enough, predictive analytics could be used to match people on supervision with the staff person best suited to handle the needs of that case, notes Lovins: “Imagine if we took the series of assessments people on supervision take when they come in, then have an analysis of staff characteristics, and then we use predictive analytics to say staff who work with this type of person have considerably more success than another type of person.

“Then we can start asking questions like, ‘What type of officer do you want? Do you want someone more supportive who will high-five you or do you want someone who’s going to hold you accountable and keep you on your toes? Which one do you need more successful?’”

Predictive analytics can empower community corrections professionals to allocate resources more efficiently. For high-risk offenders, it can inform decisions about more intensive supervision and targeted interventions, while low-risk offenders may benefit from less restrictive community-based programs. This not only ensures that resources are used where they are most needed but also reduces the chances of over-supervision, which can be counterproductive and lead to higher recidivism rates.

Lovins emphasizes the potential impact: “We have access to all this information. We should be looking at what are the things that we can learn about people. For me, that’s the one area where I think predictive analytics could be used in community corrections that would transcend what we do and improve our practice.”

The future of predictive analytics in community corrections

Predictive analytics in community corrections is not without its challenges. Concerns about data privacy, the accuracy of predictions and the potential for bias in machine learning algorithms are valid and require careful consideration and ongoing oversight. However, the benefits it offers in terms of resource allocation and risk management make it a promising development that could reshape the future of community corrections.

“I would look at the NFL,” Lovins states. “That organization has tons of money, but they use analytics to predict the likelihood that someone catches a touchdown pass based on speed and other factors. You have to expand your box to look outside of corrections to understand capabilities. Then you do a gap analysis within your organization to see where predictive analytics could assist and start small.”

Predictive analytics and risk assessment tools offer a new way forward for community corrections. By allowing for the identification of risk factors and the efficient allocation of resources, these tools can improve supervision strategies, promote effective treatment, and ultimately contribute to safer communities. As we continue to navigate the digital revolution, the integration of these advanced analytical tools into community corrections is an exciting development that promises to bring about a more effective and efficient justice system.

Challenges to the use of predictive analytics in community corrections

While the potential of predictive analytics in community corrections is clear, its journey to full integration and acceptance faces several challenges. Lovins points out some of the key obstacles.

1. Data privacy concerns. In an era marked by growing concerns about data privacy and security, using predictive analytics in community corrections raises valid questions about how personal data is collected, stored, and used. Ensuring compliance with privacy regulations and building trust among stakeholders are critical challenges. “I think one of the problems that we’ve got with predictive analytics is that we have more of a suspicious country these days,” Lovins notes. “There’s a big fear of exposing the data, which is a key barrier to using this technology.”

2. Accuracy and bias. Predictive analytics relies on historical data, and biases present in historical records can lead to biased predictions. Ensuring that the algorithms used are fair and do not perpetuate existing biases is a significant challenge in the field.

3. Technological limitations. Many community corrections agencies still rely on outdated technology and manual record-keeping systems. Transitioning to modern, data-driven approaches can be a substantial challenge, requiring investment in technology infrastructure and training.

4. Resistance to change. Human behavior is not always predictable, and some professionals in the field may be resistant to relying on data-driven predictions. Overcoming skepticism and ensuring that predictive analytics complements, rather than replaces, the judgment of experienced professionals is a balancing act.

The path forward

Despite potential challenges, the benefits of predictive analytics in community corrections are too significant to ignore. To navigate these challenges and harness the power of predictive analytics effectively, several strategies can be employed:

1. Data governance. Establish robust data governance practices to ensure data accuracy, integrity, and compliance with privacy regulations. Transparent data handling processes can build trust among stakeholders.

2. Algorithm fairness. Continuously evaluate and refine predictive algorithms to minimize biases. Collaborate with experts in machine learning ethics to ensure fairness in predictions.

3. Technology investment. Invest in modern technology infrastructure that supports data collection, analysis and reporting. Training programs can help staff adapt to new technology effectively.

4. Change management. Implement change management strategies to address resistance to adopting data-driven approaches. Engage with staff, provide education and emphasize that predictive analytics is a tool to enhance decision-making, not replace it.

5. Collaboration and research. Foster collaboration between community corrections agencies, researchers, and technology providers to advance the field’s understanding of predictive analytics. Research can help identify best practices and areas for improvement.

6. Community engagement. Involve the community in discussions about the use of predictive analytics in community corrections. Transparency and open dialogue can address concerns and build support.

7. Policy development. Work with policymakers to establish clear guidelines and regulations for the ethical use of predictive analytics in the criminal justice system. Policy frameworks can provide a foundation for responsible implementation.

Examples of data-driven decision-making with predictive analytics

Here are some examples of how predictive analytics can inform data-driven decisions in community corrections:

1. Matching staff and offenders. Predictive analytics can be used to match the right probation or parole officers with the right offenders based on assessments and characteristics. By doing so, community corrections agencies can improve the chances of successful rehabilitation and supervision.

2. Risk assessment and technical violations. Predictive analytics can analyze data on technical violations and conditions of supervision to identify trends and correlations. This information can help probation and parole officers make data-driven decisions about which conditions are most effective for different types of offenders.

3. Violence prevention. Predictive analytics can be used to predict the likelihood of violent behavior among offenders. By identifying early warning signs or patterns of violence, community corrections agencies can intervene proactively to prevent violent incidents.

4. Home visits optimization. Predictive analytics can assess the safety of home visits for probation and parole officers. It can determine under what circumstances a visit should be conducted with a partner or additional precautions, enhancing the safety of fieldwork.

5. System utilization analysis. By analyzing the utilization of various social services and systems by individuals, predictive analytics can help identify high-risk individuals who are frequently engaged with multiple services, such as hospitals, jails, mental health units, and social services. This insight can guide targeted interventions.

These examples illustrate how predictive analytics can be applied in community corrections to make informed decisions that enhance public safety, improve offender outcomes, and optimize resource allocation. While challenges exist, the potential benefits of adopting predictive analytics in this field are substantial.

Nancy Perry is Editor-in-Chief of Police1 and Corrections1, responsible for defining original editorial content, tracking industry trends, managing expert contributors and leading the execution of special coverage efforts.

Prior to joining Lexipol in 2017, Nancy served as an editor for emergency medical services publications and communities for 22 years, during which she received a Jesse H. Neal award. She has a bachelor’s degree in English Literature from the University of Sussex in England and a master’s degree in Professional Writing from the University of Southern California. Ask questions or submit ideas to Nancy by e-mailing