Matt Edmonds Predictive Analytics

Transcription

1 Matt Edmonds Predictive Analytics

2 Agenda Introduction to PA Guidewire s Approach to PA Case Studies Software Conclusion Page 2

3 Introduction to Predictive Analytics

4 What is Predictive Analytics Predictive analytics is the branch of the advanced analytics which is used to make predictions about unknown future events. PredictiveAnalyticsToday.com Page 4

5 Predictive Analytics in Insurance Basic Advanced Pricing & Underwriting Ratemaking Adverse selection monitoring UW risk scoring External data cost reduction Marketing & Distribution Channel performance Expansion opportunities Channel risk selection Customer Lifetime Value models Claims Fraud detection Triage & assignment Subrogation Specialist referral Text mining Litigation Expert Lifetime value pricing Portfolio optimization Channel profitability monitoring Straight-through processing Program effectiveness Intervention prediction

6 PROGRAMMING & DATABASE Computer science fundamentals Scripting language (e.g., Python) Statistical computing package (e.g., R) Databases (e.g., SQL and NoSQL) Relational algebra Parallel databases and parallel query processing MapReduce concepts Hadoop and Hive/Pig Custom reducers Experience with XaaS like AWS THE PERFECT ELUSIVE DATA SCIENTIST MATH & STATISTICS Machine learning Statistical modeling Experimental design Bayesian inference Supervised learning, decision trees, Random Forest, Logistic Regression Unsupervised learning, clustering, dimensionality reduction Optimization DOMAIN KNOWLEDGE & SOFT SKILLS Passionate about business/ industry Curious about data Influence without authority Hacker mindset Problem solver Strategic, proactive, creative, innovative, and collaborative COMMUNICATION & VISUALIZATION Able to engage with senior business leaders Storytelling skills Translate data driven insights into decisions and actions Visual art design Knowledge of visualization tools (e.g., D3.js, Tableau)

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8 Guidewire Predictive Analytics

9 Guidewire Predictive Analytics Guidewire Predictive Analytics enables insurance organizations to drive better business results and empower a data and analytics-driven culture. A scalable turnkey solution, Guidewire Predictive Analytics includes tools for data preparation, integration, deployment, and reporting. The only machine-learning analytics system built for insurance, by a team with deep insurance expertise.

10 Spam Filters Video Streaming Driverless Cars Face Recognition Consumer Marketing Medical Diagnostics Machine learning predictive analytics is well-established in many industries. Insurance is a late adopter.

11 The Machine Learning Difference Without Machine Learning: Reliance on human hypotheses Slow and iterative Linear interactions only With Machine Learning: More accurate and stable No human hypotheses, so not limited by human creativity Models built 10x faster than usual Non-linear interactions become clear

12 PREDICTIVE ANALYTICS JOURNEY DATA MOBILIZE Monitor model & integration performance Select models for deployment MOBILIZE IT implementation requirements Define business rules & Cleanse data MODEL actions Find variable DATA signal Select & methodology correlations Define algorithm Select variables parameters MODEL Bucket variables Validate models Integrate multiple models DEPLOY Integrate with existing systems DEPLOY Train and communicate to front line Rating Sophistication Claims Management Expense Reduction Data Expertise DATA-DRIVEN ORGANIZATION Business Sponsor: Real-time dashboard reporting Modeling Team: Real-time analysis-level information Front line, Claims/ Underwriting: Real-time evaluations of quotes, policies, claims with reason codes Technology Staff: Analytic model control panel, automated error checking, release manager, infrastructure

13 Case Studies

14 Loss Ratio Relativity Exposure Distribution Case Study: Auto Pricing Challenge Large auto carrier looking for opportunities for pricing improvement % 20% 15% Solution Machine-learning algorithms found that 60% of the exposures in the carrier s technical premiums had pricing errors greater than 10%: o Underpricing errors of up to 54% % 5% 0% o Overpricing errors of up to 34% Score Band Machine Learning results delivered in 2 months compared to GLM at 18 months. Exposure Distribution Actual Relativity

15 Case Study: P&C Underwriting Challenge Personal Property: $308M premium/ 5 years of data* Target specific customers identified by scoring (loss ratio and retention) in batch mode and produce policy listing to take immediate actions. Solution Find high-profit, lower-retention business, allowing retention activities that improve profit by $700,000 over three years. Exclude low-profit, high-retention business (6.5% of total exposures) * Total: $155M DWP, single-state writer

16 Case Study: Workers Compensation Claims Challenge Workers Comp program costs were increasing due to jumper claims, low auto-adjudication rates, and increased readjudication. Solution A severity model built by applying machine learning algorithms to closed claims data showed that they could identify 3 times more jumper claims, and that 50% of claims could be auto-adjudicated. Results Jumper Claims: The new model has resulted in early identification of as many as 60% of jumper claims compared to 20% the organization had previously been able to predict. Straight-through processing: The new model identified that more than 50% could be straight-through processed, compared to the 20% they had previously handled in that way. Where they had been re-adjudicating about 20% of claims, with the model in place they now readjudicate less than 5%.

17 Claims Triage EARLY IDENTIFICATION CAN REDUCE PAYOUT 4 8% FAST TRACK PROCESS MAY INCLUDE AUTOMATION 3-10% OVERALL REDUCTION IN CLAIM HANDLING EXPENSES

18 Software

19 The Guidewire Predictive Analytics Way

20 Build

21 Analyze

22 Deploy

23 Monitor Claim Track: Estimated Value: Managerial Review 15% Probability over $100,000 Page 23

24 Conclusion

25 Supports the full Analytics Journey Machine-learning algorithms developed specifically for insurance data Build and assemble more quality models, faster and easier True transparency to the model, factors, and influence Limited IT resource requirements via pre-packaged API generation for model deployment Single-source responsibility with a full-service system

26 For additional information or to schedule a demo please come visit us at the Guidewire booth