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Building the Foundation for Analytics
A Data Management Perspective Tracy A. Spadola CPCU, CIDM, FIDM V.P. Business Development – Insurance Data Management Association Practice Lead – Insurance – Teradata Corporation
Persistent Economic Turmoil, Market Uncertainty
Increased Frequency & Severity of Cat Events
Regulatory Environment Growing more Onerous
Tech Savvy, Demanding Customers Expensive & Hard to Manage
Distribution Channels
Increased Customer & Market Competition
Major Challenges in the Business Environment
Major Challenges in the IT Environment
Legacy Systems Upgrades Cyber Security
Big Data New Technologies
* Data Management Challenges
Insurers are Often Flying Blind
Each Discipline has its Own Data
No Common Understanding
No Complete View of Customer, Agent Decisions Based on Incomplete Information
BABEL
Why Data Management in Claims is Important
• Data Silos – Claims data can include many information silos including subrogation,
litigation management, adjusting, financial, case management, vendor management and more.
• Data Management – assures better claims data integration for more accurate analytics and
information as well as faster claims investigations. – allows for more automation of processes including fraud detection. – enables better regulatory claims compliance and reporting – enables product development, reduction of costs and more.
7
Types of Multi-structured Data Outside the Enterprise Data Warehouse
†Source: Analytics Platforms – Beyond the Traditional Data Warehouse, Survey of 223 companies. BeyeNetwork 2012
Data Types Outside of the Enterprise Data Warehouse
53% of Companies Struggle Analyzing Data Types Not in the Traditional Data Warehouse
Big Data, Analytics & Its Challenges
• “Non-Traditional” data sources – Web activity, telematics, weather, social media, etc. – 3rd Party data – Limited data standards
• Beyond structured – Unstructured and Multi-Structured data requiring new
technologies (hardware and software)
• Highly iterative analysis
Big Data Requires multiple Information Management strategies and new technologies.
Integration across the Analytic Ecosystem is critical
Beyond the Traditional Data Warehouse
TERADATA UNIFIED DATA ARCHITECTURE
Security, Workload Management ERP
SCM
CRM
Images
Audio and Video
Machine Logs
Text
Web and Social
SOURCES
Marketing Executives
Operational Systems
Frontline Workers
Customers Partners
Engineers
Data Scientists
Business Analysts Math
and Stats
Data Mining
Business Intelligence
Applications
Languages
Marketing
USERS
ANALYTIC TOOLS & APPS
Search
Marketing Executives
Operational Systems
Knowledge Workers
Customers Partners
Engineers
Data Scientists
Business Analysts
USERS
INTEGRATED DATA WAREHOUSE
DATA PLATFORM
INTEGRATED DISCOVERY PLATFORM
Security, Workload Management REAL TIME PROCESSING
Final Thoughts
• More and More Big Data is not traditional Insurance Data – Understand the Data Quality implications
• Identify the right platform for the particular data source
– Reporting, Analytics, File Storage, Discovery
• Implement solid Data Management practices for increased Business Value
• Creating a Data Centric Culture – Begin with the
Executives – Meet with the claims
handlers – Remember that not
everyone realizes how sexy data is!
– Apply standard change management techniques
• What’s in it for me • Provide examples of
current data gaps • Walk the team through
the possibilities in a world of accurate data – automation, ease of use, predictive claims handling
• Follow Up is Key – Bring success stories to
the team – Share pitfalls or gaps
during audits – Always tie data integrity
to new reports, features, or automation
• Keep reminding the team that data and measurement matters
© Assurant, Inc. 2015 - This document is the property of Assurant, Inc. All information contained herein and any related material thereto is considered proprietary and confidential. The content cannot be duplicated, distributed, replaced, shared, or conveyed either in whole or in part, electronically or in hard copy to any third parties without the prior written consent of Assurant, Inc.
Fraud Management Vision Leverage our extensive experience in underwriting, administration, logistics, repair, automated systems, and data-driven analytics to deliver an exceptional customer experience while preventing, detecting, and deterring fraud with automated tools and systems.
Dynamic Scrutiny (Analytics) Utilizes 7 distinct risk models to compute a score which determines the claim review process. The Dynamic Scrutiny Model conducts a detailed risk assessment of each claim to assign a score which determines the next actions required. Dynamic Fulfillment (Business Roles) Provides various methods to fulfill a claim based on the life-time value of a customer. System Dynamic Claims Management (DCM) processes are incorporated into our operational system to drive the entire claims process.
Models and Systems -
Pre-Claim Review of enrollment
data to identify potential fraud prior to a claim
being filed.
At-Claim Review of customer, claim, account, device and shipping information at
the time of the claim.
Post – Claim Review of information
associated with deductible payments and claimed device
returns to validate the policy terms are met.
Determine Probability of Fraud The pre-score, at-claim and post-score models are consolidated and weighted to calculate the fraud risk for the claim.
Analyze Claim Data Create a post-score matrix that contains: 1. Quantify deviation from standard
claimant profile such as: • shipping address • device tier • rate plan mismatch
2. Analysis of non-returned devices and bounced deductible payments.
Pre-scoring Model 1. Variations from normal for various metrics
such as # of enrollments from agents, locations, etc.
2. Past enrollments, plan details, device details, etc.
Use the probability of fraud to make a decision If probability is low then claim is approved. If high the action to be taken is driven by pre-determined business policies.
Customer Information • Insured Name • Insured Address • Make / Model • Insurance Start date • Device Activation date • Customer fraud flags
Claim Information • Loss Type • Incident Date • Equipment Claimed • Shipping Address • Claims History • Agent ID • Other customer provided information
Account Information • Total devices on account • Total claims on account
Pre-Claim Inputs At-Claim Inputs
Dealer Information • Enrollments • Device type enrollments • Take rate percentage • Claims frequency • Loss type • Cancellation rates
Enrollment Information • Geographic area • Multiple accounts • Device activation date • Coverage activation date • Rate plan mismatch
Risk Assessment Post-Claim Inputs
Payment Information • Payment address mismatch • Credit card verification
Shipment Information • Ship-to address mismatch • Ship-to known “bad address” • Multiple shipments, same address
Risk Assessment
Engine
Data Elements Drive the Risk Assessment Engine
Validation data secured and reviewed by Assurant.
Claim Adjudication
Risk Assessment
Engine
No Review Required
Validation Required
Claim Investigation Required
Validation data required from claimant and reviewed by Assurant. • Claim Review Board (internal review of claim) • Claim Investigation Unit (internal review of entire account)
Lost/Stolen Database
Address Verification
Fraud Lists
ID Verification
BIG DATA sources
Risk Assessment Engine Drives the Claim Adjudication Path
Other Uses of Big Data “How do we validate the information provided by the customer during the claim interview is correct?”
• External address and identification databases. • External bad address and fraud lists.
“How do we predict the likelihood that a claim will result in a complaint to the DOI?”
• Use a web scraper for the claim files for key word matches to populate the Corrective and Preventive Action (CAPA) database.
• Use a web scraper on social media sites to collect customer comments.
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