Reducing Criminal Recidivism Through Data Analytics

Of approximately 27,000 offenders in the Indiana Department of Correction system, roughly 17,000 are released every year, and around 37 percent of those released return to prison within three years. Recidivism is devastating for the individuals and families affected, and it’s a drain on society in myriad ways, including a hefty tax burden.

These factors heighten the need for effective rehabilitative services for incarcerated offenders. Reducing criminal recidivism by successfully transitioning offenders provides a positive social and fiscal outcome.

Advanced data analytics techniques make it possible to target the right programs, at the right time, to the right offenders.

As the model of best correctional practices, the Indiana Department of Correction strives to return productive citizens to our communities and inspire a culture of accountability, integrity, and professionalism. IDOC promotes public safety by providing meaningful, effective opportunities for successful re-entry.

The department works with public and private partners from around the state to provide programs that enable educational and vocational opportunities as a means to reduce recidivism.

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The Problem

Studies investigating causes and solutions to recidivism have established that although rates in the United States vary, all states suffer from similar social and economic consequences. While these studies can be helpful in reforming policy by providing insight into the general effectiveness of programs and services, they rely on assumptions. As a result, the studies lack statistical rigor, which inhibits the ability to make a substantive impact on offenders.

Reducing recidivism requires an in-depth look into when specific subgroups of offenders have the greatest likelihood to recidivate, which suite of programs will optimally reduce their risk of returning to prison, and how to equip policymakers with the information they need to make informed decisions and investments.

The State of Indiana asked Resultant to work alongside it in the effort to reduce recidivism in Indiana by:

  • Understanding for which subgroup of offenders the problem was most pervasive;
  • Evaluating the effectiveness of programming; and,
  • Providing actionable guidance on specific steps to reduce recidivism.

The Resultant team established a plan to apply advanced analytical techniques to cross-agency data, which would in turn provide actionable insights for the State.

The Solution

The Resultant Data Analytics team leveraged cross-agency data from the State to help tackle the issue of recidivism in a new and innovative way.

Data Discovery and Analysis

Resultant aggregated data from disparate systems including the courts, criminal justice institute, and the offender management system. The team worked alongside the State’s subject matter experts to analyze the data and understand the insights by applying a generalizable, proprietary algorithm suite, deemed the “Criminal Risk Indicator” tool to highlight relevant information and eliminate less actionable factors. With a full understanding of the data, the team was able to evaluate program effectiveness in reducing recidivism.

Effective vs. Ineffective Questions

When attempting to reduce recidivism, it is important to start by asking the right
questions. The team refrained from focusing on questions that were simplistic, drew their own conclusions, or allowed external factors to influence answers. Instead, questions were tailored to specific offenders.

Ineffective Questions

  • Which programs are effective?
  • Which facility rehabilitates offenders best?

Effective Questions

  • How can we best rehabilitate this offender?
  • If this program is applied at this time to this offender, how much of a decrease in his/her probability of recidivism can we expect?
  • How can we best reduce recidivism for the offender population by spending $XX?

Program Participation Optimization
The State was interested in identifying specific programs that were effective in reducing recidivism. Of the programs evaluated, the team was able to identify not only which programs were effective, but for which offender a program would be most effective, given the individual’s unique characteristics, background, and criminal history.

Upon determining the optimal program for each offender, the team analyzed the marginal impact of completing a secondary program.

Through the development of this tool, case managers can supplement existing practices to identify optimal programming for specific offenders, based on their characteristics such as age or offense. The tool will better inform case managers on what combination of programs are most effective and provide the greatest likelihood of rehabilitative success.

The Outcome

The “Criminal Risk Indicator” algorithm tool developed by the Resultant team on top of the SAP HANA® platform allows data to be effectively seen in a new way by:

  • Projecting the future risk of recidivism;
  • Enabling the creation of individualized and optimized programming for offenders; and
  • Better informing policymakers’ decisions on important issues like sentencing reform.

With the tool, Resultant and the State are able to specify program participation for specific offenders, which will lead to a data-driven understanding of the most effective programs for each inmate to combat recidivating.

Faced with a limited amount of funding, the State can more effectively align programs and individual offenders to provide the greatest potential for success.

Using Resultant’s recidivism tool, the State is using data to reexamine the eligibility requirements of each program for offenders. In addition, the State is able to make data-driven decisions about its allocation of programming and funding.

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