Implementation of Predictive Models – Making Models Come Alive
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Implementation of Predictive Models – Making Models Come Alive. John Lucker – Principal – Deloitte Consulting LLP Michele Yeagley, ACAS, MAAA – Asst. Vice President - Harleysville Insurance. Casualty Actuarial Society - Predictive Modeling Special Interest Seminar September 2005.

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Casualty actuarial society predictive modeling special interest seminar september 2005

Implementation of Predictive Models – Making Models Come Alive

  • John Lucker – Principal – Deloitte Consulting LLP

  • Michele Yeagley, ACAS, MAAA – Asst. Vice President - Harleysville Insurance

Casualty Actuarial Society - Predictive Modeling Special Interest Seminar

September 2005


Discussion themes

Discussion Themes

  • Predictive Modeling provides the toolset to assist with a variety of critical business operations

  • The market is softening and better risk assessment capabilities are needed to avoid inappropriate soft market dynamics and naïve reactions

  • Predictive Models can be implemented in ways that do not require complex, expensive efforts

  • The financial benefits that can be realized from predictive models are very significant – phased and rapid implementation can help fund additional future analytics and more complex implementations

  • The three most important parts of any predictive modeling project are (1) implementation; (2) implementation; and (3) implementation – building a great model should be a given


Casualty actuarial society predictive modeling special interest seminar september 2005

Some Predictive Modeling Basics

2


Some areas that insurance predictive modeling can address

Some Areas that Insurance Predictive Modeling Can Address

  • Customer Management

    • Cross-Sell Success Rates

    • Client View

    • Geo / Market Expansion

    • Book Rollover/Transfer

    • Proactive Call Centers

    • Audit & Billing Options

  • Reinsurance

    • Assumed Biz Scoring

    • Retro Placement/Retention

  • M&A

    • Pre-Deal Assessment

    • Post-Deal Remediation

  • Risk Profitability Assessment

    • Risk Attraction

    • Risk Retention

    • Risk Avoidance

    • Non-Renewals

    • Right-Pricing

  • Claims Management

    • Claim Triage

    • Duration Reduction

    • Fraud Propensity

  • Producer Management

    • Profitable Production

    • Production Retention

    • Recruitment


Before insurance predictive modeling class underwriting

Before Insurance Predictive Modeling – Class Underwriting

Numbers are Illustrative

Workers’ Compensation

Commercial Auto

CMP / BOP

Property

General Liability

Private Passenger Auto

Homeowners

Roofers

Youthful Drivers

140%

90%

135%

87%

Overall

Loss Ratio

of 75%

125%

82%

115%

Florists

Middle Aged Drivers

78%

110%

75%

100%

72%

90%

68%

80%

65%

70%

63%

60%

Below average

Average

Above average


A predictive modeling approach

Building and deploying predictive models requires a specialized combination of skills covering data management, data cleansing, data mart construction, actuarial and statistical analysis, data mining and modeling, and insurance operational and business processing and technologies

A Predictive Modeling Approach

Data Aggregation

&

Data Cleansing

Score For Each Policy

180%

160%

External

Data

140%

120%

110%

100%

90%

80%

70%

60%

Predicted loss ratio

Internal

Data

Evaluate and Create Variables

Business Rules Engine

Synthetic Variables

Develop Predictive Model

Score Driven Business Applications


With insurance predictive modeling individual risk scoring

Y = A + B(Var 1) + C(Var 2) + D(Var 3) + E(Var 4) + F(Var 5) …

With Insurance Predictive Modeling – Individual Risk Scoring

Internal / External Data

Predicted Loss Ratio

120

Bob’s Flower Shop = 821

Numbers are Illustrative

90

Linda’s Flower Shop = 324

82

PredictedLoss Ratio

Overall

L.R. 75%

78

74

70

66

62

58

50


Casualty actuarial society predictive modeling special interest seminar september 2005

Laying the Groundwork for an End-To-End Implementation

7


What the process is not what it is

What The Process IS NOT – What it IS

  • What it IS NOT

  • A Black Box approach

  • Stock delivery

  • Replacement for underwriters

  • Score used to communicate decision

  • Score drives results

  • A single variable magic bullet

  • Actuarial and/or systems project

  • Class plan underwriting

  • What it IS

  • Scoring drivers are known / understood

  • Collaborative throughout the business

  • Additional underwriting toolset

  • Reason codes / messages are developed

  • Implementation drives results

  • Relationship among variables is power

  • Business initiative

  • Efficient segmentation of policyholders


Using the lift curve for business applications renewal business

Using the Lift Curve for Business Applications – Renewal Business

  • Highly Profitable

    • Risk Attraction

    • Retention Priority

    • Less Loss Control

    • Less Premium Audit

    • More Pricing Flexibility

Best 25%

  • Renewal Business

  • Underwriter

  • Workflow

  • Highly Unprofitable

    • Risk Avoidance

    • Repricing Priority

    • Non-Renewals

    • Loss Control

    • Premium Audit

Worst 25%


Using the lift curve for business applications new business

Using the Lift Curve for Business Applications – New Business

  • Highly Profitable

    • Risk Attraction

    • Retention Priority

    • Less Loss Control

    • Less Premium Audit

    • More Pricing Flexibility

Best 25%

  • New Business

  • Underwriter

  • Workflow

  • Highly Unprofitable

    • Risk Avoidance

    • Repricing Priority

    • Non-Renewals

    • Loss Control

    • Premium Audit

Worst 25%


End to end implementation making models come alive

Biz & Technical

Planning

Predictive Modeling

Technical Developmt

Business Process Redesign

Biz & Systems Integration

Organize

Change Mgmt

Perform Metrics & Reporting

End-To-End Implementation – Making Models Come Alive

  • Predictive Models must be effectively implemented to derive their benefit potential

  • The financial benefits can be so significant that urgency should drive the pace of the project

  • Create a benefit analysis and use the benefits to drive the project – a complex process (PIF counts, LR management, retention, not written, etc)

  • Competitive jockeying should also drive project pace – first adopter advantages

  • A best practice is to create a continuum of implementation solutions and phases

  • Initial implementation should focus on extracting value from models before automation

  • Tactical implementation can be achieved in 2-4 months

  • Planning, planning, and then some more planning


End to end implementation making models come alive1

Biz & Technical

Planning

Predictive Modeling

Technical Developmt

Business Process Redesign

Biz & Systems Integration

Organize

Change Mgmt

Perform Metrics & Reporting

End-To-End Implementation – Making Models Come Alive

  • Steering Committee and Project Committee Structure

  • Phased structure and focus on 80:20 Rule

  • Development of End-State-Vision & Project Planning Document – some key questions are:

    • How will predictive modeling guide decision making, pricing, and tier placement?

    • How will predictive models impact existing business processes (e.g. by line / account)?

    • How will predictive models be blended into the field and agency management process?

    • What key performance measures must be achieved?

    • How will underwriters/raters/other personnel’s compliance be measured?

    • What level of automation is desired for various business processes?


End to end implementation making models come alive2

Biz & Technical

Planning

Predictive Modeling

Technical Developmt

Business Process Redesign

Biz & Systems Integration

Organize

Change Mgmt

Perform Metrics & Reporting

End-To-End Implementation – Making Models Come Alive

  • Extract, Transform, Load process for Internal, External, and Synthetic Data

  • Management of external data vendors and external data acquisition

  • Data quality and cleansing issues

  • Construction of Scoring Engine(s) (real time, batch, manual, etc)

  • Construction of Business Rules Engine or similar process

  • Construction of operational data mart

  • Design of technical architecture, data flow, messaging, data management, etc.

  • Model maintenance


End to end implementation making models come alive3

Biz & Technical

Planning

Predictive Modeling

Technical Developmt

Business Process Redesign

Biz & Systems Integration

Organize

Change Mgmt

Perform Metrics & Reporting

End-To-End Implementation – Making Models Come Alive

  • Underwriting workflow (renewal business, new business, touch level)

  • How model scores will be used in the U/W process (scores, reason codes, action thresholds, risk avoidance, risk acquisition, retention management, account vs. monoline, etc)

  • How will models be used as risks proceed from new to renewals (disruption issues)?

  • Use of model scores for downstream processes

  • Relationship of model usage to field and producer management

  • Business rule creation, optimization and maintenance


End to end implementation making models come alive4

Biz & Technical

Planning

Predictive Modeling

Technical Developmt

Business Process Redesign

Biz & Systems Integration

Organize

Change Mgmt

Perform Metrics & Reporting

End-To-End Implementation – Making Models Come Alive

  • What systems modifications are required to accommodate the process?

  • What will different people in different roles see throughout the process?


End to end implementation making models come alive5

Biz & Technical

Planning

Predictive Modeling

Technical Developmt

Business Process Redesign

Biz & Systems Integration

Organize

Change Mgmt

Perform Metrics & Reporting

End-To-End Implementation – Making Models Come Alive

  • Identification of all new process stakeholders (Underwriting, actuarial, systems, executive, legal/regulatory, claims, field, agency, training, project management, etc)

  • How will predictive modeling be described internally and externally to all stakeholders?

  • Manage any legal/regulatory issues and concerns

  • Once communicated, how to deal with questions, concerns, issues, etc. from Underwriters, field personnel, agents, market analysts, etc.

  • Development of change management, communication, and training activities and materials

  • Development of necessary implementation materials for all stakeholders

  • Development of feedback mechanisms using objective and subjective criteria


End to end implementation making models come alive6

Biz & Technical

Planning

Predictive Modeling

Technical Developmt

Business Process Redesign

Biz & Systems Integration

Organize

Change Mgmt

Perform Metrics & Reporting

End-To-End Implementation – Making Models Come Alive

  • Creation of management reports and metrics measurement processes including dashboards

  • Communication of model usage, results tracking, and management metrics at all process points to all constituencies

  • Loop back processes to manage compliance or deviation of model usage business plan


Contact information

Contact Information

John Lucker

Principal

Deloitte Consulting

860-543-7322

[email protected]

Michele Yeagley

Asst. Vice President

Harleysville Insurance

215-256-5403

[email protected]


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