2007 Actuarial Software Now

Transcription

A
S U P P L E M E N T
T O
Winter 2007
How Actuaries Make IT Work
for Business
Benefits of Using Scatter Plots
Discovering Office Treasures
Data Unlocks the Door of Opportunity
for Insurers
PolySytems Ad
A SUPPLEMENT TO
Winter 2007
CONTENTS
4
How Actuaries Can Make IT Work for Business
Actuaries can play an important role in implementing IT strategies.
By Jeff Carmeli
8
A good scatter plot is worth a thousand words.
By Samik Raychaudhuri
18 Discovering Office Treasures
Buried gems can make an actuary’s job easier.
By George De Graaf
28 Data Unlocks the Door of Opportunity for Insurers
Clean data in predictive models can assure improved profitability.
By Richard G. Vlasimsky
Cover image: Stockxpert
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Actuarial Software Now 1
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How Actuaries Can Make
IT Work for Business
Information technology isn’t usually part of the actuarial curriculum.
But that doesn’t mean actuaries can’t play an important role
in implementing IT strategies.
Photo: Stockxpert and Cindy Johns
By Jeff Carmeli
L
ike the financial services industry itself, the work of actuaries
has always been characterized
by a high level of information intensity. Financial services both use information as a production input and
generate information as a finished
product for customers. This combination makes the industry particularly dependent on the productivity of its information technology
(IT) investments and, consequently,
turns IT investment management
into a mission-critical activity.
Actuaries have developed a great
deal of expertise in quantitative
analysis methods, but they require
new capabilities to succeed in today’s business organizations, which,
invariably, are highly focused on
aligning their IT investments with
business strategy. Financial services
organizations, in particular, spend a
significant portion of their revenue
not only on maintaining but on
constantly upgrading their overall
enterprise architecture. This results
from several factors, such as:
■ Increasingly complex regulatory
requirements.
■
Relentless pressure from contract holders, who expect 24/7,
on-demand service and superior
investment management, coupled with access to their funds,
and customized products. These
pressures, in fact, will get more
severe in the future as the baby
boomer generation continues to
retire while attempting to retain
a very high standard of living
through sophisticated management of its retirement assets.
■
The continued need for cutting
the cost of services while raising
their quality and availability, a feat
that can be achieved only through
rapidly improving technology.
Pressure from the marketplace to
introduce and modify highly customized and competitively priced
products quickly.
In order to successfully confront
these challenges, financial institutions are forced to utilize two key
sets of process improvement tools:
IT investment governance and
alignment of architecture with business goals.
■
Keys to Business Value
Governance is a set of organizational policies and procedures intended
to control the entire IT investment
life cycle. This includes identification of IT investments, development
of business cases to evaluate a given
set of investments, and comparison
of actual to expected results. Governance is intended to guide the organization through the optimal selection of investment decisions that
are interrelated and can’t all be supported by a limited budget.
Investment governance is also required because of the real options
Armed with a better understanding of the IT
role, actuaries may be able to make a
significant contribution to the rapidly growing
field of IT investment management by helping
develop better models of risk, return, and
performance measurement.
embedded in IT investments. For
example, initial investments can be
limited and then expanded if demand for their services increases.
Similarly, IT investments can be
scaled back or terminated if they
don’t meet return expectations or if
market conditions and a particular
investment no longer match corporate value objectives. From an analytical perspective, these are call and
put options that need to be evaluated in order to objectively assess the
potential value of individual, as well
as a portfolio of, IT investments.
Conceptually, architecture denotes the overall structure of IT
(hardware, software, and services)
intended to enable the achievement
of corporate goals. Architecture has
to be evaluated periodically and
realigned to ensure that it will continue to support business objectives.
Owing their critical importance to
corporate success, architectures
are broadly defined by the organization’s chief information officer
(CIO). Once that broad definition
has been completed, however, architectures are primarily handled
by program management offices
(PMOs) that are in charge of the IT
governance process at a local level.
Within organizations that are large
enough, individual lines of business
may have their own PMO responsible for designing an investment
program that’s consistent with the
architecture defined by the CIO.
Opportunities for Actuaries
As is often the case, periods of
change create both challenges and
opportunities. The challenge to the
actuarial profession is represented
by the need to rapidly acquire capabilities that aren’t covered by the
examination process. (Because of
the ongoing evolution of these capabilities, and because they’re outside of the traditional scope of actuarial training, they probably never
will be.)
The opportunities, however, appear to far exceed the challenges.
Here are some examples of how actuaries could play a valuable role in
managing IT investments:
■ To compete in the marketplace,
today’s business organizations are
required to make large and risky
IT investments. IT investments
are notoriously difficult not only
to manage but also to evaluate
both prospectively (in order to assess their risk/return relationship)
and retrospectively (to estimate
their actual performance against a
benchmark). Armed with a better
understanding of the IT role, actuaries may be able to make a sigActuarial Software Now 5
nificant contribution to the rapidly growing field of IT investment
management by helping develop
better models of risk, return, and
performance measurement. The
analytical capabilities clearly fall
within the professions’ domain
and need only to be adapted to
the IT environment and its specific terminology.
■
To implement business strategy
portunities. In the case of IT investment management, however, the
actuarial profession may not be able
to afford the luxury of not addressing this opportunity in a direct and
structured fashion. This stems from
several reasons:
First, IT investment is a key conduit used by financial institutions to
create shareholder value. If actuaries want to continue playing a crucial
role in creating value for financial
Actuaries already play a major role on the
business side in developing those business
requirements, but they could strengthen their
position by participating in program
management offices, which are in charge of
the IT governance process.
successfully, business organizations routinely gather and revise
large volumes of requirements.
Actuaries already play a major
role on the business side in developing those business requirements.
However, they could strengthen
their position by participating in
PMOs, which are in charge of the
IT governance process.
Should Actuaries Take This
Opportunity?
As in all other professions, there are
business opportunities that may be
considered only by some members
who may feel they have a distinct
advantage in pursuing those op6 Actuarial Software Now
institutions, they’ll need to learn to
leverage IT investment activities and
align them with the actuarial projects
they’re already managing.
Second, the financial analysis
of IT investments still represents a
“white space” into which no professional group has been able to step.
This is because an unusual combination of skills is required in order
to succeed in this arena. First, despite the vast amount of academic
and professional research that has
been done in the past two decades,
the economics of IT are still not well
understood, largely because of the
turbulent technological change that
continues to confound risk/return
relationships of IT investments. Actuaries, however, may have a significant advantage in this arena through
their deep understanding of risk/
return and portfolio management
and may even be able to obtain the
support of IT senior management
they routinely work with, given the
difficulties encountered by CIOs in
assessing the potential value of IT
investments.
Finally, actuarial projects within
financial institutions are still run using less-than-rigorous methodologies. Through the development of
new IT capabilities and adoption
of appropriate methodologies and
documents, the quality and consistency of actuarial projects could be
dramatically raised. For example,
development and maintenance of
actuarial models (which can easily span multiple systems) could be
based on structured gathering of requirements and formal system specifications. Just adding this type of
documentation could significantly
reduce the level of effort and complexity required to modify actuarial
models, as business requirements
change over time.
What Capabilities Should Actuaries
Develop?
The capabilities actuaries require to
successfully address this opportunity
may be best developed outside the
exam framework. Although it may
be possible (and perhaps desirable)
to add some exam content related
to real options analysis and activitybased costing (both of which are
fundamental to successfully modeling IT investment performance),
this content may not be sufficient in
providing actuaries with a clear advantage. Actuaries who are serious
about pursuing these opportunities
will need to develop more mature
capabilities in the area of business
requirements gathering and analysis, as well as a solid understanding of enterprise architecture. Developing these new skills may even
be possible through a joint training
effort with IT personnel within the
organization.
IT investments will continue to
play a critical role in the creation
of shareholder value for financial
institutions. Analyzing, defining,
Analyzing, defining, managing, and
evaluating the performance of those investments creates an arena that is still wide open
and could significantly benefit from actuaries’
highly regarded analytical capabilities.
managing, and evaluating the performance of those investments creates an arena that is still wide open
and could significantly benefit from
actuaries’ highly regarded analytical capabilities. The actuarial profession, however, must also take the
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lead in adapting and appropriately
expanding its arsenal of tools in order to leverage this extraordinary
opportunity. n
Jeff Carmeli is an actuarial consultant in
Edison, N.J.
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If a picture is worth a thousand words, a scatter plot can graphically
show the nature of the data before an actuary decides to embark on more
complicated quantitative methods.
By Samik Raychaudhuri
A
ctuaries have various tools at
their disposal for analyzing
the impact of risk and uncertainty. One tool, however, is often
overlooked, and it can be very useful for attacking certain problems.
Scatter plots can help save time and
can be very effective at deriving
preliminary conclusions quickly, enabling the user to decide if further
analysis is needed. What are scatter
plots, and why should actuaries use
them? Here are some common uses
of these plots, common plot types,
and best practices.
Fig. 1 Example of a Scatter Plot.
What Is a Scatter Plot?
A scatter plot (also known as a scatter chart or scatter diagram) is a
kind of graph used to visually display and compare two sets of related quantitative or numeric data.
Each pair of values is displayed as
a coordinate on a horizontal and a
vertical axis. Once a collection of
points has been plotted, it’s known
as a scatter plot. Fig. 1 shows an example of a scatter plot.
What Can a Scatter Plot Tell Us?
Let’s assume we’re performing a
study about patients who have had
a heart attack. We
want to find out if
there is a correlation between the
patients’ weight
and the number of heart attacks they’ve had.
We’ve gathered
data from 1,000
patients; Fig. 1
shows our results
on a scatter plot.
The number of
past heart attacks
has been plotted
along the X, or
horizontal, axis,
and the weight of our patients has
been plotted along the Y, or vertical, axis. This scatter plot can be
used to quickly derive important
conclusions, saving us the time and
trouble it might take with other
alternatives.
A scatter plot is often used to reveal relationships or associations between two variables. The pattern of
the plots, or lack thereof, serves as a
guide to gain insight into the relationship. This plot is typically used
as an important guide before using
other advanced analytical methods
to further probe into the relationships in the data. In the aforementioned plot, we can clearly see there
is a strong correlation between patients’ weight and the number of
heart attacks they’ve experienced.
It’s important to note that a scatter plot can show someone only the
possible relationship between the
variables. It can’t offer insight into
the cause or effect of a relationship.
It’s up to the actuary using the scatter plot to investigate further and
uncover more information about
the relationship.
Uses of Scatter Plots
Uncover relationships: As mentioned above, scatter plots can be
A scatter plot is often used to reveal relationships or associations between two variables. The
pattern of the plots, or lack thereof, is used as
a guide to gain insight into the relationship.
Fig. 2 Positive Correlation.
used to uncover relationships between two variables. Specifically,
scatter plots can be used to answer
questions such as:
■ Are the variables related?
■
If they are related, what kind of
relationship exists (positive/negative/or no relationship)?
Which points are outliers
and which may need further
investigation?
We can calculate the mathematical correlation between the two variables to give us an understanding of
■
Fig. 3 Negative Correlation.
the strength or weakness of the relationship. A strong positive correlation can be identified if the pattern
of dots slopes from the lower left
corner to the upper right corner (Fig.
2). A strong negative correlation can
be identified if the pattern of dots
slopes from the upper left corner to
the lower right corner (Fig. 3). If no
such pattern is visible, the relationship between the variables can be
said to be random (no correlation)
or to have low correlation. Correlation between the two variables is an
important mathematical validation
of a scatter plot.
Scatter plots can also be used to
investigate independence (or correlation) in a single series of data. To
accomplish this, we construct two
series from a single data series by
choosing a certain lag in the data.
For example, if we have 10 data
points, we can construct two data
series of nine data points each. The
first data series consists of the first
nine points, and the second one
consists of the last nine points (implying a lag of one period). Once
these points are plotted on a scatter
Scatter plots are also used by professionals
in the insurance and financial industries
to analyze risks associated with
insurance premiums. They can quickly show
how their company compares against the
competition for premiums related to
the same risk event.
plot, we may be able to ascertain
that there is an autocorrelation (interdependence) between the points.
Guiding further mathematical analysis: Scatter plots can help
guide an actuary into the necessity
of using other statistical methods to
gain more insight into the data. If
the plot indicates a high degree of
positive or negative correlation (as in
Figures 2 and 3, where the points are
shaped like a band), then the actuary
might want to perform linear regression to arrive at a mathematical relationship between the variables.
Likewise, if the data indicate little
positive or negative correlation, the
actuary might want to include nonlinear regression to find out a precise
mathematical equation of the relationship between the variables.
If the plot doesn’t show any specific pattern, other statistical methods might be needed to analyze the
data. Specifically, when the scatter
plot can be easily partitioned into
two or more regions (Fig. 4), clus10 Actuarial Software Now
tering algorithms can be used to
subdivide the data into regions and
then draw conclusions specific to
each region. Patterns can be identified visually, giving the scatter plot
its power. Otherwise, we could scan
through data for hours or even days
and not be able to recognize obvious
patterns.
Quality control and Six Sigma:
Scatter plots are often used in quality control and Six Sigma projects
for a variety of reasons, one of
which is their ability to find outliers.
In these projects, quality practitioners often try to determine the percentage of “defects,” or points that
fall outside certain limits. A scatter
plot can provide these individuals
with obvious visual cues, indicating
when something has moved beyond
the control limits.
Multiple scatter plots: Scatter
plots are very insightful, even with
projects that involve more than two
variables. A scatter plot can be constructed for each pair of variables.
When these plots are arranged on
a grid, the collection of plots is
called a scatter matrix (or a scatter-
Fig. 4 Indication of Data Clusters.
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Interpretation of a scatter plot can be
subjective, especially when it’s used to make
important decisions. It is, after all, a tool to
be used for visionary analysis. It should not be
used as the only tool for making decisions.
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plot matrix). A scatter matrix allows the actuary to compare the
different correlations between variables.
For example, in a project involving multiple factors and a response
variable, we can look at the scatter
matrix and decide which variables
are driving the response and which
are not. A scatter matrix is useful for
visualizing how data change across
different variables. By putting all
the scatter plots in the matrix the
same way, you can see how the correlation between variables changes
from one scatter plot to another.
Industries using scatter plots:
Scatter plots are routinely used in
many different industries for many
different tasks. The medical profession regularly uses scatter plots to
identify patterns in the occurrence
of medical problems, and manufacturers use them for quality control.
In addition, scatter plots are used
by professionals in the insurance
and financial industries. These professionals often analyze risks associated with insurance premiums and
can compare prevailing insurance
premiums in the industry by using
a scatter plot. The plots can quickly
show how their company compares
against the competition for premiums related to the same risk event.
Weaknesses and Best Practice
Just as with any other tool, users of
scatter plots need to be aware of
certain limitations that can help
avoid pitfalls.
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■
■
Interpretation of a scatter plot can
be subjective, especially when it’s
used to make important decisions.
It is, after all, a tool to be used for
visionary analysis. It should not be
used as the only tool for making
decisions. This limitation can be
overcome by conducting further
quantitative analysis of the data.
At the very least, one should look
at the basic statistics of the data
(such as mean, median, standard
deviation, coefficient of variance
etc.) along with the scatter plot to
make meaningful judgments.
Scatter plots with too few or too
many samples aren’t very useful.
Too few points on the scatter plot
may not be enough to arrive at
a proper meaningful conclusion.
On the other hand, too many
points on a scatter plot might distort or hide trends in the chart.
A best practice is to play around
with the number of samples in
the plot and see if the same conclusions can be made.
As with other quantitative methods for analysis, a scatter plot is
only as good and reliable as the
data being used. If there is substantial variation in the quality of
the data or in the data collection
process, it might lead to incorrect
plot characteristics and wrong
conclusions. A best practice is to
look closely at the source of the
data and the methods used for
data collection, and ensure that
everything has been done correctly.
■
Although a scatter plot yields
quantitative data, it provides no
means for identifying the specific
cause or effect of certain behaviors seen in the plot. Practitioners
using a scatter plot must be vigilant about what can or can’t be
deduced in a plot.
Software Using Scatter Plots
Most statistical software packages
and spreadsheet software currently
have the ability to create scatter
plots. Microsoft Excel has a reasonably strong charting engine that
also includes scatter plots. Generalpurpose statistical software packages such as Matlab, SAS, Mathematica, and Minitab also include
the ability to draw these plots. Special-purpose risk modeling software
such as Crystal Ball, which can run
Monte Carlo simulations, also has
the capability of creating scatter
plots and scatter-plot matrices that
can show the relationship among
various probabilistic assumptions
and model forecasts.
Many software packages now
support three-dimensional scatter
plots, where users can simultaneously visualize the relationships
among three variables. By changing the color of the plot points,
this software provides the ability to
include an additional dimension.
These plots can help visualize some
of the non-intuitive relationships
between variables, which are typically only uncovered by advanced
quantitative techniques.
Conclusion
While scatter plots won’t provide
the answer to all questions, they’ll
quickly and easily provide insight
and show cases that need further
analysis, which otherwise might take
hours or even days to determine. As
is often said, a picture can be more
powerful than a thousand words;
likewise, a scatter plot can graphically show the nature of the data
before an actuary decides to embark
on more complicated quantitative
methods. n
References
Temporal Distributions of Problem Behavior Based on Scatter Plot
Analysis. Kahng et al. Journal
of Applied Behavior Analysis.
Vol 31, pp 593-604, 1998.
Scatter Plot. Engineering Statistics Handbook. Maintained online at: http://www.itl.nist.gov/
div898/handbook/.
Samik Raychaudhuri is a senior member of
the technical staff of Oracle’s Crystal Ball
Global Business Unit in Denver.
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Discovering Office Treasures
Everybody knows and uses Microsoft Office. But did you know it’s
full of buried gems that can make an actuary’s job easier?
M
icrosoft
Office
continues to be one of
the most popular
collections
of software
programs in
use today. Excel
spreadsheets,
Word documents,
Access databases, and
PowerPoint presentations have become such
an integral part of business
that it’s difficult to imagine working
without them. Of all the Office
applications, Visual Basic for Applications (VBA) may be the hidden gem in this treasure chest of
software.
Macros
All Microsoft Office programs come
with VBA, a powerful application
development language. Some of
you have been using VBA without
assign a macro
to a button, keystroke, toolbar, or
menu. As a result,
one click of the
mouse or keyboard runs a series of commands.
even
knowing it.
Consider macros, those collections
of repetitive tasks. When you record a macro in Excel, the underlying macro code is programmed in
VBA. The recorder translates each
keystroke or mouse click into the
appropriate sequence of VBA commands needed to carry out the task.
When you run a macro, you’re telling Excel to run a VBA program,
also known as a subroutine. You can
VBA
Once you’ve mastered
macros, the next step is to
write your own code in VBA.
VBA is just like Visual Basic, except that VBA depends on Office
and can’t run alone. VBA programs
are stored in modules and become
part of the file (spreadsheet, database, etc.). Writing your own programs opens up the full potential of
Office and is the secret to uncovering real treasure in your applications.
Consider an example of VBA in
Excel. Let’s say you want to read information from an Excel worksheet,
perform calculations in VBA, and
Photo: Stockxpert
By George De Graaf
VBA programs are stored in modules and
become part of the file (spreadsheet, database,
etc.). Writing your own programs opens up the
full potential of Office and is the secret to
uncovering real treasure in your applications.
return results to the worksheet. A
sample subroutine (program) consists of the following code:
Sub Example1()
Variable1 = Range(“Input1”).
Value
Variable2 = Range(“Input2”).
Value
Result = 100*Variable1 +
Variable2
Range(“C15”).Value = Result
End Sub
The two range names (Input1 and
Input2) identify input cells in Excel.
The calculation may be as simple or
complex as needed and can involve
other variables as well. The final result is stored in the variable (Result)
and returned to cell C15 in Excel.
Any cell in the workbook is available
to the VBA program.
Why bother with VBA? The above
result can be calculated directly in
Excel. However, VBA can handle
extremely complex calculations that
may be cumbersome or impossible
to manage in a worksheet environment. Also, the input variables can
be set to different range names, depending on some condition. Flexibility is the advantage.
Now let’s look at a similar process in Access. In a database, data
is arranged in tables or queries by
records (rows) and fields (columns).
You can use VBA to read data from
a record set (table or query), perform calculations, and return results
to another table. A sample program
follows:
Sub Example2()
Set rstTable = Currentdb.
OpenRecordset(“Table”)
Variable1 =
rstTable(“Input1”)
Variable2 =
rstTable(“Input2”)
rstTable.Close
Result = 100*Variable1 +
Variable2
Set rstResult = Currentdb.
OpenRecordset(“Result”)
rstResult.AddNew
rstResult(“Answer”) = Result
rstResult.Update
rstResult.Close
End Sub
In this case, the table [Table]
contains two fields, [Input1] and
[Input2]. The program reads these
values into VBA, calculates a result,
and stores the result as a new record
in the table [Result], which contains
a single field [Answer]. You can enhance the program to loop through
all records in [Table] and perform
the calculation each time, storing all
of the results in [Result].
Transferring Data
You can use VBA to control not
only your current application but
also any other Office application.
For example, you can use VBA in
Access to send data to Excel, or you
can write a VBA program in Excel
that prints a Word document (containing an Excel chart). Through
VBA, all of the Office applications
are available for any project.
You can use VBA to control not only your
current application but also any other Office
application. For example, you can use VBA in
Access to send data to Excel, or you can write a
VBA program in Excel that prints a Word
document (containing an Excel chart).
In Access, for example, the TransferSpreadsheet VBA command
provides an efficient way to import
or export data between Access and
Excel. This gives you the power of
both applications in one VBA program. The primary benefit is that
you can manage the data in Access
and perform spreadsheet analysis
in Excel. Using each application
for the purpose for which it was designed produces greater efficiencies
overall.
To illustrate, consider an Excel
workbook named Newdata.xls that
contains data arranged in a table
from A1 to C100, with table headings in row A. The following line of
VBA code in Access will import the
data into a table named [Sales]:
DoCmd.TransferSpreadsheet
acImport,,”Sales”,”C:\Newdata.xls”, True, “A1:C100”
The reverse process (exporting
data to Excel) consists of similar
code:
DoCmd.TransferSpreadsheet
acExport,,”Sales”,”C:\Newdata.xls”, True, “A1:C100”
Alternatively, the VBA command
CopyFromRecordset can be used
to copy data from a table or query
in an Access database to an Excel
workbook. This command can be
used within a VBA program in either Excel or Access.
Set rstTable = Currentdb.
OpenRecordset(“Table”)
Range(“A1”).CopyFromRecordset
rstTable
The first line opens an Access table (called [Table]); the second line
copies all of the data to Excel, starting in cell A1.
Queries
Data is a precious jewel, especially
to businesses in the financial services
industry. Significant resources have
been devoted to developing data
warehouses, query tools, data mining units, analysis programs, and
knowledge management systems.
The ability to obtain results from
a database gives you the power to
make informed decisions. A fundamental understanding of structured
query language (SQL)—the standard language used to query most
databases—yields big dividends. An
SQL statement tells the database
which tables to consider, how to join
related tables, what calculations to
perform, and how to display results.
In addition to the tools that are available to query databases, you can use
VBA to connect to a database, build
and execute an SQL statement, and
display results.
Below is an example of a VBA
program in Access that builds and
runs a simple query. The table
[Sales] contains sales revenue data
by year, state, and product. Assume that we want to find the total
revenue for 2007 for all states and
products.
Sub Example4()
Dim strSQL As String
Dim rstResult As Recordset
strSQL = “SELECT SUM(Revenue)
AS Total FROM [Sales] WHERE
Year = 2007”
Set rstResult = CurrentDb.
OpenRecordset(strSQL)
MsgBox (“Total Revenue for 2007 is: “ &
rstResult(“Total”))
rstResult.Close
Set rstResult = Nothing
End Sub
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Significant resources have been devoted to
developing data warehouses, query tools, data
mining units, analysis programs, and knowledge
managementBysystems.
TheHerzog
ability to obtain
Thomas N.
results from a database gives you the power
to make informed decisions.
There are several new parts to
this program. The Dim statements
declare variables and classify them
as specific data types so that VBA
knows how to work with them.
The strSQL variable is a text string
containing the SQL statement.
The rstResult variable is the result
of running the query through the
OpenRecordset command. A message box displays the correct answer
after the total is calculated.
Open Database Connectivity
Most seasoned veterans know that
Access can query data that exists
not only in Access but also in any
other database through Microsoft’s
gem of a technology called open
database connectivity (ODBC). An
ODBC connection allows Access
to communicate with any other
ODBC-compliant database, including DB2, Oracle, SQL Server, and
many others. The ODBC standard
acts as a translator between databases, allowing them to pass data
and commands back and forth.
You can write VBA programs that
dynamically build SQL statements
that retrieve data from a corporate
data warehouse (which is just a big
database) and send results to Excel.
This gives you the power to write ad
hoc queries and generate reports,
all from within the familiar Office
environment.
With Access, the following scenario is possible. A company stores
monthly financial results in a corporate data warehouse. Once the
results are available, Access can be
used to query the database and calculate subtotals by product or state.
This data can be stored in an Access
table and used to determine trends
over time. Analysts can study profitability, compare actual results to
plan, and perform ad hoc queries
that may not be available through
the company’s normal reports generated from the data warehouse.
Component Object Model
VBA can also call other programs
outside of Office. Microsoft developed another gem called component
object model (COM), which enables
one application to call another application. VBA and Office are built
upon COM. If you have an existing
application that supports COM, you
can integrate it with your VBA program. Essentially, this extends the
power of your Office application to
include the other application.
Conclusion
I’ve touched briefly on several technologies present in Microsoft Office.
More information on any of these
topics may be found on the Internet,
especially Microsoft.com or MSDN.
com. There are several other websites, including discussion forums,
newsgroups, and communities, that
provide a wealth of knowledge about
VBA and Office. The treasure is as
close as your computer. n
George De Graaf is a property/casualty
reserving actuary for Nationwide Insurance
in Des Moines, Iowa. He also serves as
the webmaster for the Iowa Actuaries Club.
He is a past chairperson of the Society of
Actuaries Computer Science Section Council. He may be reached at DeGraafConsulting@mchsi.com.
N`eb\cmfjj
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Data Unlocks the Door of
Opportunity for Insurers
Insurance carriers must not only develop strong predictive models
but view data as an internal asset and a competitive advantage.
By cleansing dense and cumbersome internal data and considering
additional external factors, carriers can assure improved profitability.
T
he hard market cycle of property/casualty rate increases
we saw from 2001 to 2004
is over. While industry indicators show that rates increased
for both property and general
liability lines in the first quarter of 2006, overall, the
property/casualty insurance market is soft. The
carriers we’ve talked
to are concerned about a
wholesale return to a soft market, with the reduction in underwriting discipline and the marketfollowing behavior it implies. They
also recognize a fundamental shift
is taking place in the market, and
they are concerned about being left
behind in the search for competitive
advantage.
One of the key success factors
emerging for insurance companies
today lies in a carrier’s ability to assess risk at least as accurately as and,
where possible, more accurately
than its competitors — and then to
price that risk appropriately. But beyond this focus on pricing precision
is an increased emphasis on ease of
doing business for agents, a desire
for quality growth that maintains
current profitability guidelines, and
an increase in automated processing
of insurance transactions, whether
new business, policy changes, or
renewals.
As a result, carriers are looking
closer at analytics and business intelligence (BI) technologies, specifically the use of predictive analytics,
in order to develop fact-based business models, streamline manual
operations, and drive sound
business decisions.
While many carriers
already have a predictive
analytic solution in place,
others are just beginning
to embrace the power
of predictive analytics to
propel sound, data-driven
decision-making processes. Regardless of where
they are in adopting predictive analytics, however,
the organizations we talk
to all ask the same fundamental question. Carriers
continually want to know the
critical factors they must address to
ensure successful utilization of their
predictive analytic environment not
only initially but over time as well.
And while our answers often focus on recommendations unique to
each firm based on their objectives,
current capabilities, and resources,
one recommendation continually
emerges on a topic that is all too frequently ignored. For advanced users
as well as beginners, data is the key
Photo: Stockxpert
By Richard G. Vlasimsky
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One of the key success factors emerging for
insurance companies today lies in a carrier’s
ability to assess risk at least as accurately as
and, where possible, more accurately than its
competitors—and then to price that risk
appropriately.
element in revealing business opportunities. After all, a predictive model
is only as good as the data that it is
based upon.
Clean Your Data
While many carriers may believe
they already have the elements in
place to build a predictive model,
the first step in maximizing opportunity should always be to review
the in-house data being used. Carriers trying to base risk assessment
on data that has not been cleaned
need to beware. All too frequently,
the data is of poor quality and may
not be easily accessible for analysis.
The process begins by examining
existing data—policy, claims, and
demographic information—and executing a rigorous data validation,
data hygiene, and data enrichment
process to create a clean, statisticalgrade data set that can be used for
predictive modeling. While carriers might understand the importance of attention to fundamentals
in the predictive model being built,
in the beginning, they often fail to
recognize the importance of data
integrity. Data integrity needs to
be checked to ensure that the data
set does not contain keying errors,
referential integrity errors, or nonstationary issues—if these occur,
a poorly performing model will
result.
Once the issue is brought to their
attention, the challenge becomes:
How can a carrier execute the data
cleansing process? Manually analyzing thousands of pieces of data
takes months of dedication and
time—time most market-driven
companies do not have. Further, by
the time this entire process is completed manually, much of the data
may already be out of date, defeating the purpose of the exercise.
In hopes of overcoming these
lofty challenges, some providers
of predictive analytic solutions are
addressing this first step and offering automated data validation and
cleansing solutions. The intuitive
technology they utilize scrubs the
data, scanning for inaccuracies and
“outlier” data, assuring each data
element is ready for the modeling
process. By leveraging an automated
tool, the data validation and cleansing process is executed in weeks,
leading to improved and highly accurate models with a much faster
time to market.
Unify Your Data
Not only do carriers have to address
the cleansing of data that’s already
in place, but also they frequently
have to unify disparate databases.
Often this arises from discrepancies
in how data is stored in different databases internally, but a new source
of difficulty in data unification is
emerging. Today’s dynamic organizations are growing and acquiring new companies and divisions—
therefore acquiring more data. Part
of the data hygiene process requires
that all data be unified and standardized into a usable format. This
includes not only assuring data is up
to date but eliminating unique organizational data patterns and creating a standard data format.
Here again, many providers of
predictive analytic solutions offer
pre-determined methodologies for
standardizing data in an analytic
friendly format. They have the processes and procedures in place to
map data from multiple data sources internally and resolve the apparent discrepancies that exist in data
formats, data definitions, and data
capture and storage methodologies
to ensure that the data being analyzed is complete and consistent.
Outsmarting the Competition
When building a model, a carrier
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typically relies
on an ongoing
on data from its
basis.
Some
For advanced users as well as beginners, data
own policy and
predictive
is the key element in revealing business opporclaims adminisanalytics techtration systems.
nologies not
tunities. After all, a predictive model is only as
only prepare
This
means
good
as
the
data
that
it
is
based
upon.
data initially
that most carbut also moniriers are levertor the data set
aging relatively
the same standard information and ogy providers are offering access to continue to check for changes or
are likely to derive the same insights to external data sources as a part inaccuracies, ensuring that human
as the competition. Carriers that are of the service they offer. More ad- or technical errors are not leading to
able to combine internal data with vanced providers are going the next false findings. In order to drive accuexternal data can gain insights into step—aggregating external data rate pricing and long-term growth,
into a single data source to stream- carriers need to recognize the imrisks that competitors will not see.
By accessing additional data from line the data unification and analysis portance of first cleansing data and
continuing the process throughout
external sources, linking the exter- process. This data is already formatthe life of the model.
nal data to internal data, and con- ted for use within a model, allowing
In order to achieve quality
sidering variable interactions from carriers to skip the cleansing process
growth, pricing precision, ease of
among the different sources, a mod- and also streamline the unification doing business with agents, and
el may reveal greater insights, allow- with their internal data. When car- streamlined processing flows, insuring carriers to out-select the compe- riers combine this with improved ance carriers must not only develop
tition and uncover new pockets of data analysis and predictive analytic strong predictive models but also
techniques, they are able to discover view data from a unique perspecopportunity.
While traditional external data performance parameters that are tive—as an internal asset that is a
sources have been utilized for some outside the norm and stay a step source of competitive advantage.
There is no magic trick to unveiling
time in risk analysis, new data sourc- ahead of the competition.
new insights that lead to competies are emerging that can provide The Data Goes On
significant insight when combined The final phase in effectively lev- tive advantage; the key is hidden
with internal data. Information such eraged data—which is often over- within often dense and cumbersome internal data. The good news
as location indicators to the nearest looked by carriers—involves the
is that there is help available in the
hospital or police station, or safety ongoing monitoring and refreshmarketplace. By cleansing data and
information about transportation ing of data. As carriers utilize the
considering additional external
devices can help carriers reveal seg- predictive models to enhance their factors, carriers are able to ensure
ments of opportunity previously book of business, and as competi- improved profitability and leverage
hidden in larger and previously dis- tors respond, the data captured and a source of ongoing competitive
the benefits derived are constantly advantage. n
missed market segments.
Recognizing that it is difficult to changing. Carriers need to factor
obtain external data that is accurate in these transformations to create Richard G. Vlasimsky is chief technology
and highly relevant, some technol- highly effective and accurate models officer for Valen Technologies in Denver.
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