FICO Credit-Based Insurance Scores

Transcripción

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© 2011 Fair Isaac Corporation.
Use of Credit Scores in the Property &
Casualty Insurance Industry
Boost Operating Efficiency and Underwriting Profit
with Predictive Analytics
Lamont D. Boyd, CPCU, AIM
Insurance Industry Director, Scores and Analytics
FICO (Fair Isaac Corporation)
This presentation is provided for the recipient only and cannot be
reproduced or shared without Fair Isaac Corporation's express consent.
2
Agenda
» FICO Analytics in Insurance
» FICO Insurance Scores
» Background
» Industry Usage
» Discussion
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© 2008 Fair Isaac Corporation. Confidential.
FICO is the leader in Predictive
Analytics and Decision Management.
Thousands of businesses worldwide,
including nine of the top ten
Fortune 500, rely on FICO to
Make Every Decision Count.
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FICO Overview:
Committed to the Insurance Industry
We have provided services and technologies to companies in multiple industry
sectors – property & casualty (personal and commercial lines), life & health, and
brokerage.
Plus over 350 other insurers worldwide, including:
Aetna
Amerigroup Corporation
Amica Mutual Insurance Co.
Atlantic Mutual Insurance
American Family Insurance
Aviva
Chubb
Co-Op Network
Farmers Insurance Group
Fireman's Fund Insurance Co.
Great American Insurance
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Guardian Life Insurance
Kemper National Insurance
Liberty Mutual Insurance
Liberty Northwest Insurance
Mercury Insurance
Missouri Employers Mutual
Mutual Of Enumclaw Insurance
Nationwide Mutual Insurance
Norwich Union
OH Bureau Of Workers' Comp
One Beacon Insurance
Peerless Insurance Company
Prudential Insurance
Safeco
Travelers
Unitrin
VHI
Westfield Insurance
Zurich Insurance Company
FICO’s Solutions for Insurance
Power profitable decisions
across core insurance
processes while increasing
precision in the numerous
customer decisions
» Build a profitable book-of-business
» Bring right products to market, faster
» Faster, more efficient claims
processing and management
» Acquire and grow profitable
customers
» Increase customer loyalty and
retention
» Fight fraud from beginning to end
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Two-thirds
of the Top US P&C Insurers
FICO Insurance Analytic Solutions
Insurance Industry
Property & Casualty
Life
Health
Rules Mgmt, UW
Profit, Claims
P&C Personal
P&C Commercial
Private Payors
Auto
CBIS Scores
Custom Risk
Models
Fraud Detection
UW Decision
Rules Mgmt.
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Homeowners
CBIS Scores
Custom Risk
Models
UW Decision
Rules Mgmt.
Workers’ Comp
Fraud Detection
Loss Reserving
Subrogation
Custom Risk
Models
Rules Mgmt.
Property & Liability
Rules Mgmt.
Fraud Detection
Government
Federal
State
Medicare
Medicaid
Fraud Detection
Fraud Detection
Rules Mgmt.
FICO’s view:
Analytics in operation
Data
Data
External
Data
DATA
MANAGEMENT
Prodn./
Ops.
Env.
Internal
Data
Data
Modeling
Analytic
Data Mart
Cleansing
Exp. Design
Reactions
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ANALYTICS
(Models, Scores,
Strategies)
Predictive
Modeling
Exploratory
Analysis/
Data Mining
Prospects &
Decision
Modeling
and
Strategy
Design
Customers
STRATEGY
DEPLOYMENT
Decision
Engine
Rules
Mgmt.
Case
Mgmt.
Actions
Prodn./
Ops.
Env.
Agenda
» FICO Credit-Based Insurance Scores
» Background
» Industry Usage
» Discussion
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» CBIS introduced in the US in 1993, Canada in 1995
» Current generation, commercially-available FICO CBIS
» Equifax US and Equifax Canada
» Experian US
» TransUnion US
» Custom FICO CBIS Models (FICO Model Builder)
» Developed for individual clients, based on underwriting
strategies, past performance, and future needs
» US industry estimates
» ~90-95% of personal lines automobile and homeowner
insurance underwriting and pricing decisions are based in part
on CBIS
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» Global FICO Scores for Insurance
» Available in countries
» Robust consumer credit data collection and reporting practices
» Allow the insurance industry’s use of consumer credit
information
» FICO Scores Partner in Mexico
» Circulo de Credito
» Juan Manuel Ruiz Palmieri
Director Comercial
» Ricardo Moreno Contreras
Gerente de Productos de Riesgo
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» FICO CBIS Benefits
» Greater predictability of future loss
» More accurate risk decisions for greater profitability
» Facilitate efficient, consistent and objective decisionmaking for swifter processing
» New and renewal underwriting, pricing, fraud detection
» Account and book management
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» Consumer benefits
» Relationship between how people manage their credit and how they
manage their risk is significant
» Conscientious credit managers are conscientious risk managers
IMPORTANT TO NOTE: Majority of consumers manage
their credit well and benefit from the use of insurance
scoring
» Up to 75% of US auto and home insurance applicants pay lower
premiums due to insurance scores
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Insurance Scoring Background
» Statistical correlation proven repeatedly
»
»
»
»
»
»
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FICO studies
FICO CBIS client studies
Tillinghast Towers-Perrin study
University of Texas study
Epic Actuaries study
Texas Department of Insurance study
FICO & Círculo de Crédito study in México
Insurance Scoring Background
» US Federal Trade Commission – FTC
» FTC Auto Study – July, 2007
» CBIS scores are objective tools for more accurate risk evaluation
» Use benefits most consumers
» CBIS scores cannot be used to identify demographic groups – so no
effect as proxy for race or ethnicity
» CBIS scores not correlated to income levels but to an individual’s
management of credit obligations
» Restricting CBIS scores would result in higher rates for better risks –
without regard to race and ethnicity
» FTC Homeowner study – 2013/2014 ETA
» Precisely the same findings are anticipated
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Insurance Scoring Background Study
Tillinghast Towers-Perrin
» Study Title: CBIS and Loss Ratio Relativities.
» Study Date: Dec 1996
» Sponsors:
» 8 Auto, Homeowners Companies in US market.
» Findings
» The relationship between the CBIS
and the loss ratio relativities is
highly statistically significant, or
» Is very unlikely CBIS and loss
relativities are not correlated.
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EPIC Actuaries
» Study Title: The Relationship of CBIS to Private Passenger Automobile
Insurance Loss Propensity.
» Study Date: Jun 2003
» Sponsors:
•
•
Alliance of American Insurers
The American Insurance Association
•
•
The National Association of Mutual Insurance Companies
The National Association of Independent Insurers
» Findings
» CBIS correlated with propensity for loss,
primarily with claim frequency, rather than
with average claim severities.
» CBIS do overlap with other risk
characteristics, but finally CBIS increase the
accuracy of the risk assessment process.
» CBIS are among three primary risk factors for
six automobile coverage's studied.
- BI Liability
- Pers.Inj.Prot
- Comprehensive
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- PD Liability
- Med Pay
- Collision
33%
9%
-19%
EPIC Actuaries
» Sponsors:
•
•
•
•
» Findings
» CBIS correlated with propensity for loss,
primarily with claim frequency, rather than with
average claim severities.
» CBIS do overlap with other risk
characteristics, but finally CBIS increase the
accuracy of the risk assessment process.
- BI Liability
- Pers.Inj.Prot
- Comprehensive
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- PD Liability
- Med Pay
- Collision
33%
9%
-19%
EPIC Actuaries
» Sponsors:
•
•
•
•
» Findings
» CBIS correlated with propensity for loss, primarily
with claim frequency, rather than with average
claim severities.
» CBIS do overlap with other risk characteristics,
but finally CBIS increase the accuracy of the risk
assessment process.
- BI Liability
- Pers. Inj. Prot.
- Comprehensive
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- PD Liability
- Med Pay
- Collision
Círculo de Crédito
» Study Title: CBIS and Loss Ratio Relativities in México.
» Sponsors:
» Auto Insurance Companies in Mexican market.
» Findings
» The conclusion is that it is very high that CBIS and loss ratio relativities, considering the average
claim severities, are correlated.
LOSS RATIO RELATIVITY - RANK ORDER
150%
140%
% LRR
130%
120%
110%
100%
90%
80%
70%
LOW
450
600
MEDIUM
690
Score
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740
HIGH
790
FICO CBIS Scorecard Development
» Using depersonalized data, over 100 credit characteristics
are analyzed against insurance policy premium and loss
records
» Adjusted for relationship of overlapping characteristics
» Statistical attribute weighting
» Insurance scoring models
» 10-15 most predictive characteristics
» No single factor drives the FICO CBIS score
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FICO Analytics:
Multivariate Modeling for Underwriting
» Determine value of predictive information available
» Calculate statistically accurate weights for most predictive
information
» Produce statistical model(s) which allow for greater policy
processing efficiency and predict future performance
Outstanding
Debt
Outstanding
Debt
Payment
History
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Oldest
Account
VS.
Payment
History
Oldest
Account
FICO Model Development:
Initial data analysis
Fine Binning
Coarse Binning
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» Common data issues
» Incomplete and/or inaccurate data
» Outlier anomalies can skew statistics
» Highly correlated items blur the
incremental value contributed by each
predictive variable
» FICO’s approach improves results
» Binning preserves patterns while reducing
noise
» No information bin effectively handles
outliers
» Tools automatically assess correlation
impact & information value contributed for
each variable
FICO CBIS Models –
Rank-Order Example
Loss Ratio Relativity
Automobile Insurance
1.8
1.6
1.4
1.2
1.0
0.8
0.6
0.4
0.2
0.0
low
high
Score Range
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FICO CBIS
Five General Areas of Predictive Information
Credit History
Length
15%
Outstanding
Debt
30%
Pursuit of New
Credit
10%
Credit Mix
5%
Payment History
40%
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Information NOT Considered
by FICO CBIS Models
» Race, color, national origin
» Religion
» Gender
» Marital status
» Age
» Income, occupation or
employment history
» Location of residence
» Any interest rate being charged
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» Child/family support obligations
or rental agreements
» Certain types of inquiries
» Whether or not a consumer is
participating in credit counseling
of any kind
» Any information that is not
proven to be predictive of future
performance
» Any information not found in the
credit report
FICO CBIS Usage
Lifecycle: Customer Acquisition
» New Business Underwriting, Pricing, and POS Fraud
Strategy
» Earliest CBIS application
» Consistent and objective new business decisions
» Quickly accepting applicants with greatest profit potential
» Dedicating resources to appropriate risks
» Minimizing exposure to riskier applicants
» Highlighting potential fraud at point-of-sale
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New Business Underwriting, Pricing,
and POS Fraud Strategy
Surcharged
Max
140 Surcharge Pricing
Discounted Pricing
Levels
130
120
Loss Ratio
110
100
90
80
Average
70
60
50
40
0
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1
2
3
5
6
7
4
Population by deciles
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FICO CBIS Usage
Lifecycle: Customer Relationship Management
» Renewal underwriting/pricing strategy
» Secondary CBIS application
» Updating CBIS scores at renewal
» Identifying renewal policyholders for swift processing
» Highlighting renewal policyholders requiring greater attention
» Renewal tier-placement and pricing relative to exposure
» Consistent and objective renewal underwriting decisions
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Renewal Underwriting and Pricing Strategy
N/R
Refer to
Underwriting
Discounted Pricing Levels
140
130
120
Loss Ratio
110
100
90
80
Average
70
60
50
40
0
30
1
2
3
5
6
7
4
8
9
10
Profitable Customer Retention Strategy
140
No Cross-Selling or
Contact
Regular Cross-selling
and Contact
130
120
Loss Ratio
110
100
90
80
Average
70
60
50
40
0
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1
2
3
5
6
7
4
8
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FICO CBIS Usage
Lifecycle: Book Management
» Book management
» Managing books of business at variety of levels across the enterprise
with a focus on production and profitability goals
» Underwriting territory management for greater book understanding
» Production source management for more effectively managed and
strengthened relationships
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Profitable Book Management Strategy
Monitor closely
for action
140
Reward production
source
130
120
Loss Ratio
110
100
90
80
Average
70
60
50
40
0
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2
3
5
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Key Takeaways
» FICO Analytics are uniquely positioned for the insurance
industry
» 400+ clients over 30+ years
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»
»
»
»
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Predictive scoring and analytics market leader for past two decades
Commercially-available or custom models
Enhanced decision-making at every point in lifecycle
Proven predictive solutions to meet client strategies and needs
Focused on regulatory compliance, operational efficiency, and
greater profitability
Agenda
» Background
» Industry Usage
» Discussion
3535
Thank You
In USA
In Mexico
Lamont D. Boyd, CPCU, AIM
Insurance Market Director,
Scores and Analytics
[email protected]
602-485-9858
Juan Manuel Ruiz Palmieri
Sales Director
[email protected]
(55) 1720-9977
Ricardo Moreno Contreras
Product Risk Manager
[email protected]
(55) 1720-9969
To learn more visit:
FICO.com > Industry > Insurance
This presentation is provided for the recipient only and cannot be
reproduced or shared without Fair Isaac Corporation's express consent.
36

FICO Credit-Based Insurance Scores

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