non-traditional actuarial work

Non-Traditional Actuarial WorkSCCAC - 2016/11/30Frank Chang

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Traditional Actuaries (P&C)


● Pricing / Product Actuaries: Estimate insurance premiums, may engage in some research, tool-building, or product development

● Reserving / Corporate / Financial Actuaries: Estimate loss reserves, reserve uncertainty, may be involved in reinsurance, capital allocation, statutory accounting

● Research / Modeling Actuaries:Build cat models, class plans, claims analytics, credit models, gain competitive advantage / efficiency

Employed by Insurance Companies, Consultancies, Regulators

Non-Traditional Actuaries (P&C)


● Pricing Actuaries: Estimate cost of risk, optimal retention (ERM), impact of operational decisions, insurance purchasing decisions

● Reserving Actuaries: Estimate self-insured or captive loss accruals and expense, may model VaR, tVaR

● Research Actuaries:Construct predictive models to mitigate or allocate loss, increase operational efficiency

Employed by Other Companies

Non-Traditional Actuaries (P&C)


● Product Management: Estimate pricing / structure / design for company’s insurance-related product offering

● Risk Management: Decide on insurance purchasing, risk allocation, distribution within risk-bearing entities, manage risk profile

● Data Science / Statistician:Conduct experiments, provide data-based results to guide decision-making, may be embedded or centralized

Employed by Other Companies

I. Bootstrapping:Overview

Simple Generalized Linear Model (ODP)


1. Set i = accident year, j = development age2. Inflation factor, going down accident years: xi3. Development factor, going across maturities: yj4. k, a constant

A few math slides to get us to the bootstrap!

Set Up Model


1. Set mij = incremental loss for year i at age j

2. Find xi and yj to best fit the data: mij = kxiyj

3. Take the log of mij = kxiyj to make it easier to fit

ln(mij) = c + αi + βj

where αi = ln(xi) and βj = ln(yj)


Analyze Results


Use generalized Pearson residuals to judge fit:- uniquely defined for negative incremental loss- calculated consistent with scale parameter

where

95% of residuals should lie inside (-2, 2)


Reduce, Reuse, Recycle


Use residuals to bootstrap triangle

Bootstrap Results


Output from “Chain Ladder” package

Gives range for estimates for

Pricing: Ultimate cost

Reserving: “IBNR”

II. Application ofBootstrapping

Operational Planning : Handsets


Unit Sales Forecast Process

Demand

1. Weekly Forecast

Demand

Demand

By unitBy region

2. Incorporate Customer Input

Carriers

3. Global Shifting

MOTO

4. Supply-Demand Management

Adjustment AdjustmentDemantra

or equivalent

Operational Planning : Bootstrap


Organize Data



Organize Data - Triangular Form

● What patterns do we see?

● What questions can we ask?



Calculate Model Parameters, Residuals

● Construct Forecast / Actual Ratios● Calculate residuals by vintage, segment



Results

● 2013 YE financial forecast of 3,050 (75th %ile)● 2013q1 actual: 2,850 units (close to mean)

III. Overview of Big Data

Tools Used in Actuarial Research


Overview

Non-Distributed Computing:● R

● Python

Distributed Computing:● Hive

● Spark (Scala / Java / Python / R)



R / R Studio / R Studio Server

● Software with stats-focused libraries

● Handles more data than Excel (up to 5 GB*)

● Great visualization libraries (e.g. Shiny)

● Not really a programming language

* Revolution R (now owned by Microsoft) can handle more



Python / iPython (juPyter) Notebook

● Well-supported programming language (2.x)

● Fast, great for ETL (pandas)

● Handles bigger datasets (up to 40 GB)

● Not stats or visualization focused

● Easy-to-learn, easy to share work (notebooks)



Apache Hive

● SQL-like interface to Hadoop

● Distributed Data (limited only by hardware) ● ETL Tool to get data into manageable form

● Bad max speed (set up mappers and reducers)

● Min speed is determined by worse executor



Apache Spark

● Tool with 4-language interface

● Distributed Data (limited only by hardware) ● Can write libraries to be incorporated as jars

● Better max speed (all work done in memory)

● Can handle ETL as well as statistical modeling

non-traditional actuarial work

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