Predictive Analytics in Action

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Cathleen Cole
8 years ago
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1 Predictive Analytics in Action Steven Golightly Ph.D. Director Los Angles County Child Support Services

2 Who Are We?

3 Los Angeles County LA County is 24% of the State s child support caseload. Over 1500 employees Caseload sizes averages 800 per case caseworker

4 Analyzing Data Program Support Division CSTATs Meeting Data Sharing

5 Why Do We Need It?

6 Business Need for Analytics Child Support Agencies Challenges Less resources staff (budget cuts, hiring freezes) money (budgets flat lined) Increased caseloads and workloads Increased performance expectations and goals

freezes) money (budgets flat lined) Increased

7 Integration of Predictive Analytics within Child Support Agencies Predictive Analytics Objectives Improved decision-making at case on-set Identify payment behavior Actuarial decisions based on statistical relationships between variables

decision-making at case on-set Identify payment behavior

8 Predictive Analytics Will Predict a cases payment behavior Caseworker will be able to: Make data-driven decisions Think strategically Reduce inefficiency Identify and confront under-performing cases Prioritize work Overturn conventional case management thinking

strategically Reduce inefficiency Identify and confront

9 How We Began?

10 Predictive Analytics Encompasses a variety of techniques that analyze current and historical facts to make predictions about future, or otherwise unknown, events. Statistics Modeling Data mining

11 Model Development Procurement of Software Staffing Concerns Training of Software

12 Procurement of Software LA County CSSD chose IBM s SPSS Modeler Professional as our predictive analytic tool since we were already working with SPSS Statistics. Client license vs. Desktop license o Modeler could be used by a number of users from their own computers. Cost o Purchased one license, which will require users to schedule when the software can be used.

13 Staffing Concerns Staffing Concerns o Skill set needed for analytics o Internal staff (building up skills in analytics) o Management Fellows Program o Hiring staff with appropriate background o Unions Training of Software o Trial and error

analytics) o Management Fellows Program o Hiring staff with

14 Where Are We?

15 Goal Predictive Analytics Goal: By June 30, 2014, develop an initial analytical model which can be used to predict the probability that payments will be received in a child support case. Score all active enforcement cases based on those predictions and distribute them in a format that will allow Child Support officers to prioritize their work.

16 Scoring Project Project Task: Build an analytical model via the IBM SPSS Modeler software that will score our enforcement caseload based on our prediction that Non-Custodial Parent (NCP) will regularly pay monthly order in full.

enforcement caseload based on our prediction that

17 Data Mining Identifying the right data Data Age Available Data Elements Accessibility 15% 5% 5% 5% 5% Validating Data 65% Normalizing data for software Business Understanding Data Preparation Deployment Data Understanding Modeling Evaluation

65% Normalizing data for software Business Understanding

18 Data Management Multiple tools to manage data Microsoft Access, Excel IBM SPSS Statistics, Modeler Data transformed multiple times to get data into a format understandable to Modeler

19 Scoring Project Run basic statistics Identify Target Variable Sample data View data relationships (Association Model) Score data (Classification Model) IBM SPSS Modeler

20 Scoring Project IBM SPSS Modeler - Scores

21 Data Elements Scored NCP Data Age Address Cell Phone address Employer Information UIB, DIB Prison Indicator Bankruptcy Flag Case Information Arrears Amount Current Support Order Amount Early Intervention Participation Youngest Dependent Age

22 Model Analysis Model Validation Scored all enforcement cases, monthly, starting October 2012 through September Compared a years worth of predictions to actual payment history Year 1 Prediction Accuracy Payer Non-Payer Predicted Correctly 30.2% 54.8% 85% Predicted Incorrectly 6.8% 8.2% 15%

23 Next Steps

24 Upcoming Task Develop a deployment plan Regular re-evaluations of model Identify other uses of predictive analytics within department Automate modeling process

25 Challenges Data Clean Up Staffing Civil Service Rules Unions Areas to address: Model needs constant monitoring and modifications. Improve data accessibility. Additional training opportunities for staff. Case ownership model

26 Scored caseload will lead to a balanced workload for our caseworkers. Allocate cases based on case complexity (workload) vs. caseload Strategize customized action plans based on cases need Caseworkers perform efficiently; decreases collection cost and improves case performance

27 Thank You Dr. Steven Golightly

PREDICTIVE ANALYTICS LOS ANGELES COUNTY CSSD

PREDICTIVE ANALYTICS LOS ANGELES COUNTY CSSD DR. STEVEN GOLIGHTLY DIRECTOR LOS ANGLES COUNTY CHILD SUPPORT SERVICES Los Angeles County LA County is 24% of the State s child support caseload. Over 1500