The Use of Data in Commercial Lines P&C Insurance

Toronto Data Mining Forum Stéphane McGee, BSc, FCIA, FCASMay 7, 2014 Maki Dahchour, Ph.D., ACIA, ACAS

Adam Scarth, B.Comm., FCIA, FCAS

Agenda

An introduction to P&C Insurance

Specific examples in Commercial Automobile Insurance

Use of Data in Commercial Lines Insurance

Questions

Construction & Contracting

Manufacturing & Resources

Consumer & Business Services

Health, Education & Social Services

Transportation & Logistics

Actuaries use:

Statistics

Probability

Economics

Business Knowledge

To solve business problems.

Some Examples of Actuarial Problems:

Pricing New Products

Price Classification

Modeling Catastrophes

Graphing Size of Loss Distributions

Establishing Loss Reserves

Solvency Monitoring

Rate Adequacy Studies

Trending and Development of Losses

Projecting Future Rate Needs

Interaction with Other Departments

Underwriters

Actuarial

Finance Claims

Frequency: # Claims per Risk Insured

Severity : Average Cost of each Claim

Gl / TPL: General Liability / Third Party Liability

BI: Liability for Bodily Injuries

PD: Liability for Physical Damages

IBC Code: 4-digit code representing an industry

FSA: Forward Sortation Area: First 3 digits of a postal Code

IBNR: Incurred But Not Reported

Glossary

What are we insuring?

• Injuries to drivers, passengers, and third parties

• Damage to vehicles

• Damage to physical structures

• Loss to contents inside the vehicle (cargo)

• Liability incurred by the operation of the company

Commercial Lines Auto

What drives risk/premiums?

• Characteristics of the driver(s)

• Types of vehicles

• Distance and locations travelled

• Cargo carried

• Characteristics of the fleet/operations

• Coverage options

Commercial Lines Auto

The Good

• Mandatory data reporting

• Lots of event data

• Non-fleet follows filed rates

The Bad

• Unstructured data collected for fleets

• Subjective rating variables

• Results are volatile

The Ugly

• Larger fleets self-insure lower layers

• Fraud!

Commercial Lines Auto

What does our team do?

• Pricing analytics

• Rate filings

• Large account pricing

• Financial planning

• Communication

Commercial Lines Auto

• 2013 Towers Watson predictive modeling survey

Industry trend

Source: Towers Watson

- Why Analytics is lagging in P&C commercial lines?

• Focus on personal lines (urgent priority for some companies, more volume, better data quality)

• Data issues (see next slide)

• Lack of trust: P&C commercial lines pricing mostly UW driven

- P&C commercial lines characteristics allow for more analytics?

• Almost no regulation (in Canada)

• Derived prices are advisory prices

• Impact of soft markets (Analytics can help growing in such environment)

Current State

• Volume

• Quality

• Dimensionality

• Loss development

• Exposure base

Commercial lines Data and Modelling challenges

Volume: e.g., GL, low frequency and high severity business

Difficult to fit a distribution

Some solutions:

• Use more years of data

• Group sub-coverages/perils (e.g., analyze TPL combined instead of analyzing BI and PD separately)

• Incorporate competitors/industry rates (as an offset where not enough exposure)

• Model validation: avoid using a holdout sample and use consistency over time and judgment instead

Data volume

Quality: examples

• Null records, especially for attributes not used in the pricing

• Difficulty in linking losses with policy attributes

• Change in variables definition over time (e.g., IBC codes)

Some solutions:

• Replace Null records (imputation), various techniques exist

• Make a difference between null and zero values (e.g., credit)

• Focus more on variables used in rating and underwriting

• Work with IT and Claims departments to improve losses data files

Data quality

Dimensionality: a variable with a large number of levels (e.g., IBC codes, FSA, car codes)

Difficult to use as is in as predictor in a model

Solutions:

• Grouping: can rely on UW’s knowledge

• Dimension reduction: use proxies of the variable in question (e.g., for IBC codes, could use Sector, major class, commercial credit, Average size of businesses in the sector, Hazard grade, whether the business is B2B or B2C, Relative risk ranking of the IBC code by the company,…etc)

Dimensionality

Loss development: can be important for long-tailed lines of business (e.g., GL)

Some solutions:

• Use an existing IBNR allocation to bring losses to their ultimate values

• When dealing with undeveloped losses, use “year” as a predictor in the model and an acceptable number of years of data

Loss development

Two issues:

• Multiple exposure bases (e.g., GL: sales/payroll/area), which exposure base to use as a weight in the model?

• A linear relationship is assumed between the weight in a model and the response variable:

– Auto: car-year (true)

– GL: payroll/sales (questionable)

Some solutions:

• Model each component separately

• Use dominant exposure base and some conversion formula

• Use earned year as a weight in the model and the exposure base as predictor

Exposure base