Predictive Analytics in Action

Predictive

Analytics in Action

San Diego County

• 4,083 sq. miles – 8th largest county by size • 3 million residents – 5th largest by population • 725,000 children

• 13.9% living below poverty level

• Unemployment rate 5.9% (as of 10/14)

Our mission is to enhance our children’s futures by obtaining support for families today

• 73,000 cases

• Over 113,000 children served

• FFY 2013 - $178.7M

• 6% of California’s total caseload

• FPM Performance FFY14 : #1 – 99.8%, #2 – 88.2%,

#3 – 70%, #4 – 69.8%, #5 - $3.85 (FFY2013)

County of San Diego

•

Precision

Consistency

Agility

Speed

Cost

Predictive Analytics

Child Support Payment Predictor

 Scope of Study:

 1,033 cases opened 10/2011 through 5/2013

 No prior support order

 Objective:

 To establish a “payer” classification predictive tool that

prognosticates the NCPs who are potentially at risk to fall behind on their child support payments.

 Provide staff with a tool to more effectively perform

Child Support Payment Predictor

 466 data elements from the case and participants  _{68 field variables were created as candidates for}

exploratory data analysis

 29 variables were selected for model building  _{8 variables remain in the final model}

Child Support Payment Predictor

 Model data points

 NCP’s Employment stability

 Incarceration history

 Unemployment in previous year

 Living status in previous year  Default history on first order  NCP’s email information

 NCP’s employment status at initial order establishment

Child Support Payment Predictor

 Payment Predictor Details

 Multinomial logistics regression

used

 4 payer groups created:

 Extremely rare payer (0%-30%)

 Rare payer (31% to 50%)

 Attempt payer (51 to 80%)

 Constant payer (81% or greater)

Distribution of 6 month average payment rate (N=1033)

Child Support Payment Predictor

 By payment class

 Extremely rare payor (0%-30%)

 Rare payor (31% to 50%)

 Attempt payor (51 to 80%)

 Constant payor (81% or greater)

Total due vs. Total payment for each payor class

Child Support Payment Predictor

 _{Shortest - 2months}  _{Longest- 19 months}

Child Support Payment Predictor

 _{54% had e-mail addresses}  _{Strong impact on payer class}

Child Support Payment Predictor

 Prediction accuracy of the model  _62.5%

 _{Percentage of the caseload more accurately monitored}  14%

 Able to target/focus on at risk cases before they

What’s Next or in the works?

 Business Intelligence – “Smarter Planning”  New analytics tools

 _{Greater flexibility and detailed analysis}

 Review “payment predictor” for areas of further

analysis

 Legal Paperless System – customer behavior  Performance-based dashboard

Overview

 _{What is Business Intelligence?}

 _{Set of theories, methodologies, processes and technologies that}

transform raw data into meaningful and useful information.

 _{Why do we need Business Intelligence?}

 Reaching limit of producing NEW reporting and analytical results.  _{Paradigm shift from reactive reporting (the past) to proactive}

reporting (looking into the future).

 _{Who is likely to participate in the process?}  _{Who is likely to pay their child support?}  _{What will our caseload look like in 5 years?}

Smart

er

Planning – A new way to plan

 Smarter Strategic Planning

 _{Strategic Planning Tool – predict future case loads, staffing needs, budget and}

performance.

 Smarter Case/Knowledge Worker

 Case Stratification Tool - Based on certain case data predictors, will be able to predict case outcomes with high probability.

 “Reduce” Case Loads: Focus on cases that need ‘personal touch’ – automate others.

 _{Change Individual Case and Family Outcomes}  Proactive Case Management

 Targeted Early Intervention

Smart

er

Planning – How do we get there?

 4 Phases

 Phase I: DCSS Executive Dashboard

 Phase II: DCSS Smarter Planning

 Phase III: County-wide Data Sharing

Smart

er

Planning – Phase I

 The Past and Present

 Data Visualization of Current Performance Measures using Key Performance Indicators (KPI)*

 Summary of Case Load Performance  General Health of Department

 Central Reporting Portal

*A set of quantifiable measures that a company or industry uses to gauge or compare performance in terms of meeting their strategic and operational goals.

Smart

er

Planning – Phase II

 Phase II: DCSS Data Mining and Predictive Analytics  Looking at the Future

 Predict future case performance, participant and staff behavior

 Correlate Staff Demographics to Case Data  Smarter Planning Tool

 Case Stratification Tool  Alerts

Smart

er

Planning – Phase III

 _{Current limits to Business Intelligence based on only} Child Support Data (silo effect)

 _{Partner with other Departments including IV-A to Pilot}  Richer Business Intelligence and Predictive Analytics

Smart

er

Planning – Phase IV

 Phase IV: SmarterGoverning

 Executive Dashboard, KPIs and Reporting Portal Rollup for PSG

Departments

 Smarter, Integrated Knowledge Workers

 Probation Officer helping with Child Support Case

Management

 Early Case Intervention

 Reduce recurring public sector demands

 Central Case Management Customer Service Portal  Predicting Future Budget, Facilities and Staffing

Credit Score

Jeff Grissom Director

San Diego County

[email protected]