Next-Gen child support: improving outcomes for families.

Author:White, John
Position:Technology speaks
 
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Arguably one of the most effective federal programs of all time--child support--now faces new challenges arising from dramatic changes in our society that threaten its future success.

Changing family structures and circumstances, rising incarceration rates, and a challenging economy all make it harder than ever for child support programs to improve performance. Many state programs rely on outdated mainframe computer systems to support their operations, lack the resources for costly upgrades, and struggle to attract and retain high-performing employees--especially technical employees--needed to bring the program into the future. However, there are solutions to these challenges that can ultimately improve outcomes for families.

To succeed in the coming years, the child support program will need to embrace new ways of operating (see Figure 1), including:

* New technologies that tap into data within agencies, maximizing the outcomes of communication and enforcement strategies

* A renewed purpose that ensures the program meets parents and families where they are today, recognizing the changing social landscape

* New approaches to workforce optimization to help create a better employee experience for the child support workforce

Child Support 2.0: The Data Revolution

America's child support agencies possess a treasure trove of historical data on the cases they manage.

Typically, the data are accessed only after a parent has fallen significantly behind, often on an ad hoc basis to retroactively determine what went wrong and why. If truly put to use, however, data can be proactively leveraged to unleash significant value.

Thanks to advances in data analytics, caseworkers can make use of the data to craft solutions tailored to the unique needs of cases that also have the best chance of succeeding. Consider Florida's Child Support Program.

The Florida Child Support Program uses data to guide team members; its goal is to select the compliance actions that will result in the greatest return on investment (ROI) for the program. This is accomplished by using a predictive model based upon two specific parameter groups--the financial compliance levels of cases and the indicators of the parents' ability to pay (e.g., criminal history, employment, institutionalization status, and disabilities).

Based on the outcome of the predictive analytics model and the ROI of each potential remedy, Florida's system identifies the best course of action for a case. The model prioritizes remedial actions that have the largest ROI--bringing in the most collection money when...

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