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Decision Support Systems Final PaperMIS-648-101 Professor Jerry Fjermestad

Presented By

Andrey Skroznikov

Kevin Matos

Edwin Zankang

Onyedikachi Achilike

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Table of contents

Introduction.........................................................................…............................3

Define the problem.......................................................................................…...4

Develop a proposal......................................................................................…....4

Justification…………………………………………………………………..…………..……….…4 Benefits………………………………………….…………………………………..…………..…..…5

SPSS data model………………………………………………………………………..……….…….….…6

Step 1 Excel. csv database……………………………….....………………..………..…..….7 Step 2 Import into SPSS…………………………………………………………..………..…....7 Step 3 Cleaning the data………………………………………………………….……..……...8

Summary.......................................................................................................…..9

Research results................................................................................................10

Multiple Linear Regression in R………………………………………………..…..……..10 Descriptive analyses in Tableau 8.2…………………………….………….………..…..10 Ordinal Linear Regression in SPSS………………………………………………….…….12 Descriptive analyses in IBM Cognos Insight………………………………………..….13

Contribution…………………………….……………………………….…………………………………….16

Attributes, Independent and dependent variables…………………………………………17

Dimensions of the model..............................................................................…..18

Ordinal linear……………………………………………………………………………………....18 Multiple linear…………………………………………………………..……………..…..…...18

Building the model………………………………………………….……………………………..….……19

References…………………………………………………………………………..…………………………25

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Introduction

It is becoming increasingly apparent that we all compete from within information

businesses. At some level, all businesses, whether they manufacture goods or provide services,

compete in the global economy as information-driven enterprises. This has fueled the rapid

development and deployment over the past few decades of various forms of Information

Technology (IT). Examples include information systems such as Business Intelligence, IBM

COGNOS, SPSS, tableau, and many more. Each state has its own laws that outline the types and

amount of auto insurance that one is required to have. Your coverage requirements might be

different depending on where you live and what your personal insurance needs are. An agent

can help you understand your state's insurance requirements so you can make an educated

decision about the coverage levels and deductibles you want.

The objective of this paper is to first of all examine All States’ auto insurance premium

pricing and the risk factors involved in the insurance problem, and secondly to develop a

proposal on how to solve the problem. The benefits of this project will be outlined, a software

system that is not complicated will be used, so as to predict and analyze efficiently. After

identifying the data, it shall be imported into SPSS for a series of analysis. The result of these

analysis will give us a better idea on how to strategize, allocate and identify resources as a

growing business.

Decision Support Systems is an automated information system used to support decision-

making within an organization or business. A DSS enables users to filter through and analyze

massive reams of big data and gather information that can be used to solve problems and make

better decisions. The benefits of decision support systems comprise more informed decision-

making, opportune problem solving, superior efficiency and better learning. (11) A DSS can

compile and present information for many aspects of a business, including sales trends, actual

versus projected sales, worker productivity, profitability mix and so on. Decision Support

Systems (DSS) goes a long way to enhance a business operation in so many different ways.

First, it saves time as research has demonstrated and substantiated reduced decision cycle

time, increased employee productivity and more timely information for decision making.

Secondly, it enhances effectiveness and improves interpersonal communication. Thirdly,

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competitive advantage is being realized, and cost reduction is evident from saving from labor

savings in making decisions and from lower infrastructure or technology costs.

Define the problem

Insurance business is one of the leading elements in financial industry. ”While the use of

big data and business intelligence will matter across sectors, some sectors are set for greater

gains, the computer and electronic products and information sectors, as well as finance and

insurance, are poised to gain substantially from the use of BI”. Major global and national

insurance companies have a rich and longtime serving history, which is only superseded by

huge market share of insurance customers. Among many types of insurance products, car

insurance is perhaps the most common. Different insurance companies utilize various models

for quoting customers their car insurance premiums. As a startup insurance company our

primary goal is to investigate how car age, duration of previous policies with the company and

average age of the customer, affect quoted insurance premium on the policy. Our secondary

goal is to examine how risk is attributed to every policy. We will investigate the influence on risk

categories by the following factors: car age, duration of previous policies, premium cost, and

average customer age on the policy, home ownership and marital status.

Develop a proposal

Justification

As a startup insurance company we realize that car insurance products can be a

significant source of revenue. We also realize that insurance business is extremely competitive

and the only way we could possibly grab a market share of potential customers as if we

properly identify how other insurance companies price their products. Initially as an insurance

startup we do not feel the need to reinvent the wheel. Before we come up with our own unique

pricing strategies we need to understand robust models that are being used by others in the

market. To execute our investigative queries we acquired ALL STATE car insurance database,

which contained all our variables of interest and had almost 420000 observations related to

unique car insurance policies. To provide a sound analysis on premium pricing and factors

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attributed to composite risk categories we decided to get acquainted with trending BI

platforms. For descriptive parts of our analyses we decided to use IBM COGNOS and Tableau,

because both of these currently trending platforms provide strong visualization tools, based on

similar constructs of dimensions and measures. For the predictive parts of our analyses we

decided to use R and SPSS, because both of these platforms are popular in academic and

business environments for their competency in providing reliable tools for regression analyses.

We justify our BI platform selections as means to reconstruct a quantitative picture of variable

relationships responsible for the core processes of premium pricing and evaluation of risk

categories.

Benefits

“We can measure and therefore manage more precisely than ever before. We can make

better predictions and smarter decisions. We can target more-effective interventions, and can

do so in areas that so far have been dominated by gut and intuition rather than by data and

rigor.” There are a number of direct benefits that we hope to acquire after we conduct our

investigations. By determining the influence of composite factors on quoted premium prices of

our competition we could potentially form a sound strategy to undercut and to optimize future

customer portfolio based on customer car age, average age on the policy and duration of

previous policies. We also hope to attain a profile of customers who currently pay large

premiums as a potential target for our future direct marketing. Understanding the mechanics

behind risk categories, will give us a guideline as to what metrics should be used in determining

whether or not potential customers represent high or low risks. Knowledge of risk factor

categories will also help us to optimize the future portfolio of customers. Indirect benefits to

our investigations revolve around experimentations with four different types of BI platforms.

After we complete our analyses we should have a clear picture as to which specific BI tools suit

our needs the most. As a startup insurance company we need reliable BI tools that provide

intuitive high quality charts at short notice, we also need BI tools that are not complicated in

use for predictive analytics.

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SPSS data model

Original ALL STATE insurance database was downloaded from www.kaggle.com.

Insurance data base was obtained as an Excel .csv file. Original data contained many errors and

a great number of variables that were not needed for the intended analyses, thus we imported

the original excel data into SPSS for further cleaning and consolidation.

Step 1 Excel . csv database

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Step 2 Import into SPSS

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Step 3 Cleaning the data

Significant part of our SPSS data model creation was spent on removing observations with

missing values and removing variables that insurance company provided no detailed

explanation for. Some of the observations were also removed because depicted values did not

fall in the range which was defined by variable details. Original data base contained 665250

unique observations and 25 variables. Cleaned database contained 418439 observations and 11

variables. Here is the list of some of the changes to the original database:

Removed observations relevant to policy group sizes of more than 2 people

Removed the following variables due to irrelevance: customer ID, shopping pt, record

type, day of the purchase, time of the purchase, location of the purchase, car value, etc.

Removed Age oldest and age youngest on the policy and created an “average age on

the policy” variable

Removed noise data as NA for risk factors, NA for duration previous

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Summary

Research questions:

1. How quoted insurance premium is affected by car age, duration of previous coverage's and

customer average age on the policy.

2. How policy risk category gets affected by car age, duration of previous policies, premium

cost, average customer age on the policy, home ownership and marital status.

To investigate our first research question we imported SPSS data base file into R. We

further conducted multiple linear regression analysis which allowed us to determine which

variables were significant drivers in predicting quoted insurance premium cost to a customer.

To validate regression results we imported the SPSS data base into Tableau 8.2 and conducted

descriptive analysis for each of the drivers versus quoted insurance premium.

To investigate our second research question we conducted an ordinal linear regression

in SPSS. We chose to do ordinal linear regression because we were trying to predict risk

category variable (which is a categorical variable measured on the ordinal scale). With the help

of the ordinal regression we were able to see how risk categories of the insurance policies were

affected by demographic variables of the customers and by insurance construct variables. To

validate ordinal regression results we imported SPSS data base into IBM Cognos Insight and

conducted descriptive analysis for each of the driver variables versus risk category variable.

For both of the analyses our methodology was to compare and to contrast regression

results to matching descriptive inquires of relationships between variables. Visual

representations of relationships between variables helped us to determine anomalies that

questioned validity of results for both of the regression analyses. Discovered anomalies helped

us to understand hidden mechanics for the pricing of the insurance premium and the

evaluation of ranks for risk categories.

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Research results

1. Multiple Linear Regression in R (Illustrations are in building the model)

Multiple Regression

Estimate Std. Error T value Sig

Intercept 681.95 0.213 3199.7 0.00**

Car_Age -2.179 0.011 -184.2 0.00**

Duration_Previous -1.17 0.014 -81.68 0.00**

Average_age -0.525 0.003 -133.78 0.00**

Adjusted R-squared for our linear multiple regression model was 0.1389. This finding indicates

that the model is poor and only explains for approximately 14% of the variance in quoted cost

premiums. Even though the model does not explain for the majority of the variance, it provided

us with driver loadings. Car_age was the strongest driver in the model (Beta -2.179), this

indicated that the lower is the age of the car, the higher is the quoted premium.

Duration_Previous was the second strongest driver in the model (Beta -1.17), which indicated

that the lower is the duration of previously coverage the higher is the quoted premium. Finally,

average customer age on the policy was the weakest driver (Beta -0.525), which indicated that

the lower is the average age on the policy, the higher is the quoted premium.

Descriptive analyses in Tableau 8.2 (Illustrations are in building the model)

A. To further examine the variable relationships in our multiple regression model, we

conducted a series of descriptive analyses, between driver variables and the target variable

(quoted cost of the insurance premium). The first relationship that we looked at was the cost of

insurance premium versus car age. Our regression model indicated that the lower is the car age

the higher is the premium. However, when plotted against each other we saw that the most

significant jump in the price of premium happens from the newest car to the oldest (age 0 to

age 1). From the period of 3 to 9 years (car age) the premium stays approximately the same.

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The Tableau model visually pointed out that in year five; there was also a significant drop in

premium which was leveled off by premium increase in the next year. After year 9 premium

gradually declines to the car age of 29 years.

Possible explanations: The premium cost of the insured in year zero is much lower than year one

because most likely the insured purchased a new car and just started paying premium. Another

possible explanation is that insured receive a major discount from the insurer for being new

customer. The reason for the drop in premium for year five is because in general most insured

individuals finish paying off their vehicles and also most manufacturing warranties expire during

this time frame which leads to people maintaining their vehicles before the warranty expires and

after year six people start showing neglect for their vehicles. After warranties expire in general,

vehicles start to display functional issues.

B. The second relationship that we looked at was the cost of the insurance premium versus

duration of previous policies. Our regression model suggested that the lower is the duration,

the higher is the quoted premium. However, when cost is plotted against duration we see that

the cost of premium increases from policy duration of zero to year one. The initial increase in

the cost of premium for the first three years steadily declines up to year fifteen, where it jumps

up surpassing costs of premium for any of the duration years.

Possible explanations: tableau model visually illustrated that after the insured customer has been

with All State for at least three years, those customers graduated into the loyal customer list and

started to receive loyalty discounts, thus paying less premium to the insurer All State. These

loyalty discounts continued until loyalty year fifteen. At year fifteen the major spike in premium

was attributed to loyal senior customers. Possible reason for such an increase was because most

of the customers who have been loyal for long periods of time were over sixty years old and

were much more prone to having accidents, which increased the risk and impacted the amount of

premium All State charged the insured.

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C. The third relationship that we looked at was the cost of insurance premium versus average

insured age on the policy. Our regression model indicated that the lower is the average age on the

policy, the higher is the quoted premium. However, when cost was plotted against the average

age, we saw a steadily increase in the cost of premium from the age of 17.5 to the age of 25,

which was followed by a leveling decrease up to the age group of 75. The category of policies

with an average age of 75 years old showed the highest premium costs, this showed to be an

anomaly.

Possible explanations: our graph illustrated that younger individuals pay less premium. The

reason for this was because we were provided with less data for new customers and younger

teenagers were insured through their parents. According to the model in Tableau, once a young

adult becomes an adult at the age of twenty six the insurance company considers this age group

to be less risky of suffering a loss. Therefore, the insurer All State lowers the premium charged

to the insured and keeps that rate relatively flat for the duration of a individuals policy life,

provided that they don't get into car accidents or incur any speeding tickets. Once an individual

reached an excessive age of seventy five years old there was a tremendous spike of premium the

insured had to pay to the insurer. The reason for the premium increase is because most

individuals no longer have the same reactions, or mentality as when they were younger. Anyone

over the age of seventy five was considered as much more prone of getting into a car accident

and therefore was in the higher risk category which directly impacted the amount of premium for

the age group. An insurance company will not make money if they keep suffering losses

because they did not charge enough premium to cover for losses.

2. Ordinal Linear Regression in SPSS (Illustrations are in building the model)

Parameter Estimates

Estimate Std. Error Sig

Car_age 0.032 0.001 .000

Duration_previous -0.033 0.001 .000

Cost 0.003 0 .000

Average_age -0.26 0 .000

Homeowner = 0 0.117 0.006 .000

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Married_couple=0 0.055 0.007 .000

If Beta > 0 High score more likely

= 0 Equally likely scores

< 0 Lower Scores more likely

Pseudo R squared for our ordinal regression was 0.11. This finding indicates that the model is

poor and only explains for 11% of the variance in determining rank categories for risk factors.

However, this model provided us with driver loadings which we can use to determine

relationships. Home ownership was the strongest driver in the model (Beta 0.117), no home

ownership results in probability of high risk category. Average age on the policy was the second

best driver (Beta -0.26), the higher is the average age on the policy, and the lower is the risk

category. Marital status (Beta 0.055) indicated that being single results in high risk category. Car

age (Beta 0.032) indicated that the older is the car on the policy, the higher is the risk category.

Duration previous (Beta -0.033) indicated that the longer is the history of previous policies with

the company, the lower is the risk category. Finally, Cost (Beta 0.003) does not influence risk

categories because beta is close to 0.

Descriptive analyses in IBM COGNOS Insight (Illustrations are in building the model)

A. To examine the relationships in our ordinal regression model we conducted descriptive

analyses between the driver variable and the target variable (risk rank). Our ordinal regression

model indicated that the older is the car the higher is the risk rank for the policy. However,

plotting risk categories against the car age illustrated that when cars age from zero to one year

the risk of the policy actually increased substantially, descriptive model showed that only after

year one the risk categories started to decrease consistently with the progression of the car age.

Possible explanations: we believe that the reason for the initial risk increase was related to new

car defects that require recalls for corrections. For example, the Toyota Camry accelerator brake

had issues a few years ago when drivers would try to brake and instead the car would accelerate

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causing major accidents. Therefore, our COGNOS descriptive showed that, after the one year

trial period the risk factors lowered because by majority of factory defect issues have been

corrected. We believe that risk increase in year five resulted from the average lifespan of the car

parts which usually start to show wear and tear during the period, All State factored wear and

tear into the construct of risk. After year seven, all levels of the risk factor showed a gradual

decrease because on the average people trade in older vehicles for new models. In general it is

logical to assume that cars do not last much longer than twelve years and end up in junkyards

after reaching the age of twenty one years.

B. We further examined relationship between risk categories and the duration of previous

coverage under the same policy. Basically, we investigated how the loyalty duration of a

customer affects risk. Ordinal regression results indicated that the longer is the previous duration

history the lower is the risk category. However, between year 0 and year 1 we noticed an

increase in the amount risk, categorized by highest risk categories (3, 4) switching places. But

apart from this anomaly, our plot confirmed regression expectations up to the period of year 8-9,

during which all of the customers were deemed to be in the same risk category.

Possible explanations: for the period of 0 to 1 years of policy duration, customers present the

highest turnover risk. All State gradually lowered the turn over policy risk by providing

continual discounts for people who continue to use the services of the company. We think that

the period between 8-9 years of continual coverage indicates a cross over between risk

categories, because the amount of people in highest risk categories (3,4) decline, while the

amount of people in lowest risk categories (1,2) increase.

C. The next relationship that we investigated was average age on the policy versus risk factor.

Our ordinal regression results indicated that the higher is the average age on the policy the lower

is the risk factor. When we plotted age versus risk factor, we saw that regression results are true

but mostly for the highest risk categories (3, 4). Third risk factor category represented itself as

the highest category granted from the age of 17 to the age of 23. Our plot also showed us that

lower risk factor categories (3, 4) actually increase throughout the age progression.

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Possible explanations:

We attributed the initial decrease (from the age of 17-23) of highest risk factors to increasing

maturity level of younger individuals. COGNOS descriptive also illustrated that after individuals

are twenty five years old the risk factor lowered and remained consistent. We think that after

individuals turn twenty five, in general they are out of college and do not party as much. In other

words people become even more mature, thus even less prone to risky accidents. For the most

part this descriptive analysis confirmed our regression results.

D. Our ordinal regression results indicated that home ownership was the strongest driver in the

model. No home ownership resulted in high risk categories. To further examine this relationship

we plotted home ownership versus risk. The descriptive for this relationship between the two

variables did not show any anomalies, it supported the regression results.

Possible explanations: The reason why individuals that own a home are less risky is because

they have much more to lose such as their home and have more responsibilities than the

individuals who do not own a home in the event of a lawsuit. Another reason why individuals

that own a home are less risky is because they most likely have a car garage that will safely

secure their vehicles.

E. One of our last analyses was to examine the relationship between marital status and risk

categories. Regression results indicated that being single results in higher risk categories. We

plotted marital status versus risk categories, only to find the confirmation of the regression

findings.

Possible explanations: we visually determined that the married individuals are less risky than

individuals who are not married; a possible reason for this is that individuals who are not married

do not have as many responsibilities as people that are married. Another viable reason is that

married people usually share a policy which drives the cost of premium down.

Contribution

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“Successful companies, defined as those that outperform their peers in profitability, have leaders

who support the use of data’’ (6). Every company’s goal is to grow and expand and the need to

integrate relevant big data will be imperative to this objective. However, big data will not be

enough, this data has to be mined, and analyzed with the use of BI platforms such as the ones

presented in this report. BI tools enable firms to glean all kinds of information such as customer

to business relationship and also aid in business process optimization. Business intelligence

systems combine operational data with analytical tools to present complex and competitive

information to planners and decision makers, in order to improve the timeliness and quality of

the decision-making process (7).

Consumers are always in search of a combination of quality and reasonable pricing; any

company that is able to offer such will gain significant competitive advantage. As a startup

company looking to offer attractive products, the above analysis gave some insight into

consumer base and how to potentially formulate and hyper target our products. It is impossible to

offer great product packages without the right knowledge of customers, and this knowledge is

enabled by the use of BI platforms.

In order to create mutually beneficially premium price points, firms must account for and

mitigate certain risk factors. It is advised that companies put customers in categories reflective of

such risks. Some of the risk factors we examined were home ownership, marital status, and

candidate age. In setting a price with those factors, a family bundle pricing strategy could be a

likely solution in a scenario where an individual lives with family and is not married. The

aforementioned individual poses as a risk because of marital and home ownership status, but

what if he/she met the criteria of “having long duration with previous insurer”, older in age

(26+), and has an accident-free driving record, these factors can also be examined and aid in

marginalizing risk and potentially offering such candidate(s) a better price.

Another scenario can be seen in examining the correlation of car age and premium, new car (0)

paid less premium but according to the ordinal analysis, the newest car posed the highest risk. As

mentioned before this could be as a result of young drivers being under parents insurance,

however the risk is still there, just masked. To relieve this factor, companies can have a flat start

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out rate for all new cars and add to or subtract from that amount based on other risk factors

present. Also for individuals that pose a lot of risk, loyalty programs can be offered whereby they

get points for good driving (going for a long period of time without an accident: 2+years). This

will diminish risk and also create a relationship whereby customers take accountability for

premium pricing. It also fosters a trusting relationship between business and client, as it shows

that insurer is considerate

Using these descriptive and predictive analytical platforms helps a firm see the big picture of its

business and look for more relationships similar to the above examples. It shows that “one size

does not fit all”, and from such analysis effective customer segmentation can occur for tailor

made pricing.

In summation, Business Intelligence is essential in organization. “A sustainable business model

in today’s market is one that strategizes in congruence with effective knowledge management,

and business intelligence analytics is key for such management of knowledge.

Attributes, Independent and dependent variables

Home_ownership – variable that was described by policy holder having an ownership of a home (0 – no home ownership, 1- owned a home)

Car_age – variable that describe the age of the car on the policy

Married_couple – variable that was used to describe policy holder’s marital status (0 – not married, 1 – married)

Duration_previous – variable that was used to describe the previous longitude of policy with the same insurance company (All State)

Cost – variable that was used to describe cost of the quoted premium

Average_Age – variable that was used to indicate average age of the people on the same insurance policy.

1. Multiple regression had one dependent variable-cost; the analysis had 3 independent variables: Car_age, Duration_previous and Average_Age.2. Ordinal regression had one dependent variable-risk; the analysis had six independent variables: Cost, Home_ownership, Car_age, Married_couple, Duration_previous, Average_age.

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Dimensions of the model

1. Multiple linear

2. Ordinal linear

Building the model (multiple linear regression in R)

Average Age

Car Age

Duration_previous

Cost

Homeownership

CostCar Age

Married_couple

Duration previous

Average Age

Risk (1-4)

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Cost versus Car Age

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Cost Versus Duration Previous

Cost Versus Average Age

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Ordinal Linear Regression in SPSS

Ordinal Linear Regression in SPSS

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Risk versus Car_age

Risk versus Duration Previous

Risk versus Average Age

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Risk versus Home ownership

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Risk versus Marital Status

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References.

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(3) McAfee, A., and Brynjolfsson, E. 2012. “Big data: the management revolution,” Harvard Business Review, (November 2014), p. 62.

(4) www.Kaggle.com

(5) "Do Low-Income Households Pay More For Auto Insurance?" Online Wholesale Insurance, Superior Access Insurance Services (SAIS). Web. 15 Nov. 2014. http://www1.superioraccess.com/news/insurance-industry-news/do-low-income-households-pay-more-for-auto-insurance.

(6) Cukier, Kenneth. “Ideas economy: Finding Value in Big Data”, Oracle. June 2013. http://www.oracle.com/us/technologies/big-data/finding-value-in-big-data-1991047.pdf. Viewed 16 November 2014

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http://practicalanalytics.wordpress.com/2011/04/24/gartner-says-bi-and-analyticsa-10-5-bln-market/ 2011.

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