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P 01 paw_methods_2017_10_30_v4

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It is and it is not about the model. In this presentation, learn about a framework and some case studies that apply the framework on.

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P 01 paw_methods_2017_10_30_v4

  1. 1. A METHOD TO AI MADNESS Vishwa Kolla Head, Advanced Analytics John Hancock Insurance
  2. 2. TOPICS Background Framework Case Studies 2
  3. 3. BUILD ME A MODEL TO … 3 REDUCE COMPLAINTS GROW WALLET- SHARE GROW CSAT REDUCE CHURN REDUCE COST TO TARGET GROW BOTTOM-LINE GROW TOP-LINE REDUCE COST TO ACQUIRE
  4. 4. MODEL BUILD IS THE PATH OF LEAST RESISTANCE 4 Platforms R P H20 SPARK TENSOR FLOW TBD SUPERVISED UN SUPERVISED NLTK NUM PY PAN DAS PLOT LY PLATFORMS ALGORITHMS PACKAGES PYO DBC CRY PTO PYPD F SCI KIT TOR NAD O ZICT BAB EL BLA ZE
  5. 5. A THOUGHTFUL APPROACH CAN YIELD BETTER OUTCOMES BUSINESS USE CASES DATA MATH TECHNICAL IMPL. BUSINESS IMPL. FEED BACK 5
  6. 6. TOPICS Background Framework Case Studies 6
  7. 7. “A” MODEL BUILD FRAMEWORK 7 DATA TARGET CONSTRUCTION EVALUATION PERFORMANCE SOURCES DISTANCE FROM SIGNAL SAMPLING METHOD SAMPLE SIZE SIGNAL SIZE PREDICTION HORIZON UNIT OF ANALYSIS ONE MODEL vs. STRATIFIED ONE MODEL vs. SEVERAL MODELS TARGET DEFINITION PRESENCE OR ABSENCE BLACK BOX vs. CLEAR BOX RECENCY FREQUENCY SEVERITY FEATURE SELECTION MODELING STRATEGY MODEL STRENGTH EXPLANATORY vs. IMPORTANCE ACCURACY vs. SENSITIVITY vs. SPECIFICITY ECONOMIES OF SCOPE MODEL FIT BAGGING ENSEMBLE SINGLE vs. MULTIPLE STAGES PREDICTION & OPTIMIZATION BOOSTING
  8. 8. TOPICS Background Framework Case Studies 8
  9. 9. Business 9
  10. 10. IT (ALWAYS) STARTS WITH A BUSINESS PROBLEM PROSPECTING NURTUREACQUISITION MARKET SEGMENTS CUSTOMER SEGMENTS LIKELY TO [*] MEDIA MIX CHANNEL SURVEY ANALYTICS CROSS / UP- SELL OCR MISREP LIKELIHOOD MORTALITY APS SUMMARY FLUIDLESS SMOKER LIKELIHOOD MORBIDITY CHURN NEXT BEST OFFER CLAIM LIKELI- HOOD JOURNEY CLAIM SEVERITY NEXT BEST ACTION FRAUD >> TEXT ANALYTICS OPTIMIZE NEXT LIKELY ACTION WELLNESS IOT ANALYTICS NPS ANOMALY >> 10
  11. 11. FOCUS ON INCREMENTAL VALUE KEPT US GROUNDED BUSINESS CASE OPTICAL REALIZABLE SHARED INCREMENTAL
  12. 12. IN PROSPECTING, TARGET OPTIMIZAITON IS A JOURNEY 12 … LOWER CUSTOMER TARGETING COSTSA SERIES OF OPTIMIZATION TARGETS … Prospects Leads Apps Issued Placed CPL CPA CPP CP[*] CHANNEL MIX
  13. 13. Data 13
  14. 14. PLANS ARE NOTHING ; PLANNING IS EVERYTHING 14 EDA USEABLE USEFUL DERIVATIVES BI-VARIATE CROSSTAB PRINCOMP JOURNEY
  15. 15. A DATA STITCH IN TIME SAVES NINE 15 CLAIM TERMIN ATION CLAIM ACTV. DEMOS CALLS INTERA CTION CLAIM INIT. CUSTOMER MONTH FRAUD DETECTION
  16. 16. UNDERSTANDING DATA SAVES (NOT WASTES) TIME 16 Signal Distribution Pop. Incidence Rate Skews Model Inclusion
  17. 17. Math 17
  18. 18. FLEXIBILITY IN TARGET DEFINITION IMPROVED ACTIONABILITY 18 20172014 Predict incidence In next 3 years 2007 20172014 Predict incidence 3 years out 2007 Vs.
  19. 19. RIGHT SIZING SIGNAL CAN YIELD BETTER OUTCOMES 19 SIGNAL DILUTION SIGNAL AMPLIFICATION 1% 99% 40% 60%
  20. 20. SIMPLE MODELS CAN HELP US EXPLAIN BIG DRIVERS 20 PREDICTORS PRESENCE RECENT FREQUENT SEVERE
  21. 21. QUANTIFICATION OF INFORMATION GAP IS A GOOD FIRST STEP © Andrew Ng INFORMATION GAP
  22. 22. WINNING MODEL CHALLENGING CHAMPIONS HELPS US UP THE ANTE 22 DATA TARGET SOURCES DISTANCE FROM SIGNAL SAMPLING METHOD SAMPLE SIZE SIGNAL SIZE PREDICTION HORIZON UNIT OF ANALYSIS ONE MODEL vs. STRATIFIED ONE MODEL vs. SEVERAL MODELS TARGET DEFINITION METHODS LINEAR TREES DEEP- LEARNING EVALUATION MODEL STRENGTH EXPLANATORY vs. IMPORTANCE ACCURACY vs. SENSITIVITY vs. SPECIFICITY ECONOMIES OF SCOPE MODEL FIT
  23. 23. Technical Implementation 23
  24. 24. MULTI-STAGED MODELS PROVIDED IMPLEMENTATION FLEXIBILITY 24 9-101-8 1-7 8-10 Likely to Qualify Likely to Respond Sweet Spot DESIRED SIGNAL MODEL MIS- CLASSIFICATION MODEL EXCLUDE NOISE1 INCLUDE MIS-CLASSIFIERS2 STAGES
  25. 25. Business Implementation 25
  26. 26. A CULTURE OF MEASUREMENT, TEST AND LEARN IMPROVES VALUE 26 Measure TestLearn Build CONTINUOUS IMPROVEMENT

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