Predictive Analytics in Action

Transcription

1 Predictive Analytics in Action San Diego County

2 San Diego County 4,083 sq. miles 8 th largest county by size 3 million residents 5 th largest by population 725,000 children 13.9% living below poverty level Unemployment rate 5.9% (as of 10/14)

3 County of San Diego Department of Child Support Services Our mission is to enhance our children s futures by obtaining support for families today 73,000 cases Over 113,000 children served FFY $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)

4 Predictive Analytics Precision Consistency Agility Speed Cost

5 Overview

6 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 intervention on cases.

7 Child Support Payment Predictor Data Elements 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

8 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 information NCP s employment status at initial order establishment Other NCP Demographic data

9 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)

10 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

11 Child Support Payment Predictor Time to order or waiting time Shortest - 2months Longest- 19 months

12 Child Support Payment Predictor Variable NCP address 54% had addresses Strong impact on payer class

13 Child Support Payment Predictor Results: 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 become delinquent

14 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 Additional data point research

15 A Smarter WAY TO PLAN

16 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?

17 Smarter 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 Keep Families Intact

18 Smarter Planning How do we get there? 4 Phases Phase I: DCSS Executive Dashboard Phase II: DCSS Smarter Planning Phase III: County-wide Data Sharing Phase IV: County Smarter Government

19 Smarter Planning Phase I Phase I: DCSS Executive Dashboard 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.

20 Smarter 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

21 Smarter Planning Phase III Phase III: County-wide Data Sharing 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

22 Smarter Planning Phase IV Phase IV: Smarter Governing 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

23 Credit Score FICO.com

24 DRAFT Executive Dashboard

25 Predictive Analytics Google Now

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27 Questions? Jeff Grissom Director San Diego County