Building the analytics Poly Analyst empowered enterprise Web

Standard Outline. Functional Areas Company Marketing Customers Loss Prevention Customer Service Quality Assurance Risk Management Engineering Finance Human HR Resources Competitive Intelligence Competitors Suppliers Public Relations General Public Audit Legal Regulators

Typical Tasks - examples Outline • Social Media Data Analysis • Call Center Data Analysis • Survey Analysis • Incident Report Analysis • Fraud Detection • Subrogation/Litigation Prediction • Database Marketing • Sales Data Analysis

Social Media Data Analysis Outline Implemented in: § Marketing Dept Thousands of communications Key topics, sentiment & trends v Customer-driven insights Immediate & intelligent action v

Call Center Data Analysis Outline Implemented in: § Call Center Millions of phone call transcripts (text) Results of categorization offered in OLAP cubes v Objective, consistent results v Corporate awareness

Survey Analysis Outline Implemented in: § Vo. C § Vo. E Thousands of free text survey responses Easy to comprehend reports v Analysis of 100% of data v Responsive company

Incident Report Analysis Outline Implemented in: § Safety Department Thousands mixed incident reports Key issues and root causes v Improved Safety v Shortened response time

Fraud Detection Outline Implemented in: § SIU Millions of financial transactions Anomalies and potential fraud cases + Investigation v. Prevention/recovery v. Enhanced of losses system integrity

Subrogation Outline Prediction Implemented in: § Subrogation Dept Millions of free text claims notes Extracted key patterns + Subrogation potential v Predicting probable subros v Real time and retrospective

Database Outline. Marketing Implemented in: §Marketing Dept Millions of random prospects Increased response rate through better targeting v Increased response rate v Better allocation of resources

Cross-Sell Outline. Analysis Implemented in: § Call Center § Internet Store Real-time recommendations Millions of historical transactions v Significant increase in sales v Better customer experience

Benefits for the Enterprise Outline • Dramatic cost reduction • Increase in quality and speed of the analysis • Objective and uniform data-driven analysis • Discovery of even unexpected issues suggested by data • Automated monitoring of known problems • Timely discovery of newly developing issues • Utilization of 100% of available data: structured and text • Up-to-date reports for executives • Easy to use and maintain solution

Example: Warranty Fraud Detection • Dealer level • Identify and rank Dealers that have a large number of anomalies in submitted claims • Claim level • Determine and store for application efficient business rules automating payment decision on individual new claims

Finding Anomaly Thresholds and Suspect Dealers Machine Learning & Statistics New claim Claims Data Determine Nature of Distributions Business Rules for Claim Scoring Payment Decision Detect Thresholds & Anomalies Identify Systematic Anomalies Group Anomalies by Dealer Claim level List of Anomaly Ranked Dealers Dealer level

Analytics for Individual Claim Processing Submitted Claims Business Rules Discoveries Technology Assisted Audit Data & Text Analysis, Modeling and Scoring Pay Reject

Detecting Anomalies in Data

Finding Anomaly Thresholds Lower Threshold Upper Threshold

Distribution Analysis (Di. An) script

Tail Thresholds – for each Causal Part Tail Thresholds

Overall Anomalies in ESP claims

Dealer Total Loss by Anomaly

Dealers by Total Loss due to Anomalies

Share of Anomalies to Full Operation

Total Loss by Share of Anomalies

Dealers by Anomaly Type

Total Loss by # of Different Anomalies

Strongest Correlations: Dealers to Anomalies

Drilling down to Dealer 00601 Favorite Causal Parts

New Inputs for Predictive Modeling Add Extra Metrics through Text Mining • Feature Extraction • Entity & Event Extraction • Search for Patterns • Clustering • Categorization • Summary Creation • Sentiment Analysis

Key Steps of Text Mining Graphical Reports 1 Textual Data 2 3 4 Data Cleansing Data-driven Analysis Analyst-driven Analysis Enriching Structured Data OLAP cubes