|
 |
| |
| |
| INSURANCE |
The insurance industry is all about risk. Are you confident of the way in which you forecast business and financial risk?
To ignore the effects of uncertainty in risk analysis means to potentially expose your organization to unnecessary risk and potential failure. Your organization's existence is dependent upon being able to successfully
estimate the occurrence of future events and provide policies
that cover those risks at a competitive market price. Your knowledge
and your toolset will make the difference between whether
your work succeeds or fails.
Whether you're calculating
premiums, pricing policies, providing rate calculations, or researching appropriate
ways to invest the company's assets, you need to account for the known uncertainty in your models. No matter what risks you face, Crystal Ball software
can help you find the specific solution for your needs.
Crystal Ball is a Microsoft® Excel®-based suite of analytical tools that includes Monte Carlo simulation, optimization, and forecasting. With little effort, you can apply these advanced analytical techniques to your new or existing spreadsheets to create more accurate cost and financial predictions and better informed business decisions.
Crystal Ball software is for anyone who uses
spreadsheets and needs to forecast uncertain results.
Financial analysts, actuaries,, product managers, and operational managers
all rely on Crystal Ball to improve the quality of their
decision making processes. Common applications include pricing of products, estimating Probable Maximum Loss, determining appropriate loss reserve levels, designing pension plans, modeling life expectancy, and developing reliable loss distributions.
|
|
 Key
risk analysis features include sensitivity
analysis, correlation, historical data fitting and optimization. The sensitivity
analysis helps you to understand which of the uncertain input variables
are most critical and drive the uncertainty of your cost
model. Correlation lets you link uncertain inputs and account
for their positive or negative dependencies. If historical data
does exist, the data fitting feature will compare the data to
the distribution algorithms and calculate the best possible fit
and parameters for your data. Optimization allows you to account for uncertainty and risk in simulations but still select the best possible settings (e.g., staffing levels, investment amounts, product prices) to achieve success.
With Crystal Ball, you can:
- Replace min/max estimates with more accurate range of all possible outcomes
- Reduce the time required to produce estimates,
- Eliminate multiple manual “what if” estimates,
- Mitigate your cost and schedule risks,
- Gain immediate insight to the driving inputs and output variations,
- Make knowledgeable decisions on where to focus resources, and
- Provide decision-makers with factual data that shows the risk associated with each choice.
LEARN MORE ABOUT CRYSTAL BALL FOR INSURANCE APPLICATIONS
This page offers links to a growing number of resources, including recorded Web seminars, articles, white papers, case studies, and example models. Additionally, you can view a list of common uses and examples reported directly from customers using Crystal Ball. You can also download a free trial version of Crystal Ball to see how it can help improve your business forecasts and decisions!
"Wonderful Product, [Crystal Ball] does instantly what
used to take me hours...and more!"
-- Matthew K. Wessel, Actuarial Associate, MetLife |
|
RECORDED WEB SEMINARS
To be added soon.
Top
|
WHITE PAPERS & ARTICLES
 |
Analysis Comparativo
Del Valor Teorico Esperado De Un Fondo De Pension Bajo Contribucion
Definida
By Evaristo Diz Cruz (this is a Spanish-language paper) |
 Download |
|
Análisis Comparativo Del Valor De Una Anualidad Bajo Distintos Modelos De Riesgo Para Un Empleado Que Haya Alcanzado Los 60 Años De Edad
By Evaristo Diz, E. Diz Actuarial Services and Consulting (this is a Spanish-language paper) |
Download |
|
Applied Probability Models in Insurance Frequency and Severity Estimations
By Evaristo Diz, E. Diz Actuarial Services and Consulting (this is a Spanish-language paper) |
Download |
|
Assets
/ Liabilities Modelling for a Pension Plan
By Evaristo Diz, E. Diz Actuarial Services and Consulting (this is a Spanish-language paper) |
Download |
|
Compound Process Simulation and Application to Insurance Modeling
Xin Cao, Senior Statistician, HSB Inspection & Insurance, Co.
 |
Download |
|
An Enterprise Level Approach to Proactive
Performance Engineering
By Michael K. Cook, Technical Specialist I, State Farm Insurance |
Download |
|
Impacto de los cambios de la Mortalidad en el Valor Esperado del pago
de un Plan de Pensiones Tipo Lump – Sum o pago único
By Evaristo Diz, E. Diz Actuarial Services and Consulting (this is a Spanish-language paper) |
Download |
|
Modeling
Certain Annuities Valued in Life - Expectancy
By Evaristo Diz, E. Diz Actuarial Services and Consulting (this is a Spanish-language paper) |
Download |
|
Warranty Cost-Risk
Analysis
By James R. Brennan, Product Assurance Analysts |
Download |
|
Top
|
CASE STUDIES
|
Healthcare Negotiation
Lobbyists Use Crystal Ball To Negotiate Expansion Of Children's
Health Insurance Subsidies |
Download |
|
Insurance Litigation
Crystal Ball Suits Lawyer for Insurance Defense |
Download |
|
Top
|
EXAMPLE MODELS
|
Financial Impact of Process Risk - Medical Claim Payments
From: This model is discussed in the textbook Lean Six Sigma Statistics, written by Dr. Alastair Muir, and is available from Dr. Muir's Web site.
Detail: We are on the project team directed towards reducing the variation in time for processing medical claim payments. As part of the Measure phase, we must assess the financial risk associated with the existing process. We have surveyed the customers and know that errors causing delays in payment are a common complaint and require a great deal of time on the part of the business to identify and correct.
The belief from management is that the error rate is relatively low, on the order of 1-2 percent. Our job is to estimate the span of the problem in financial terms to baseline the process and get buy-in from management. A Monte Carlo simulation of the process is conducted using Crystal Ball. The results show the range of expected cycle times and financial impact on the customer. They also identify the key process steps influencing the wide range of cycle times and financial impact. |
Download
For:
Crystal Ball
Level:
Moderate |
|
Top
|
COMMON USES & EXAMPLES
The following examples were provided by our customers and represent
only some of the potential insurance-related applications for
Crystal Ball.
- Calculate risk premiums for insurance tariffs
- Designing pension plans
- Determining appropriate loss reserve levels
- Developing reliable loss distributions
- Estimate what premiums to charge given business failure rates
- Estimating Probable Maximum Loss
- Generate probability distributions for claims payments for
insurance projects that are subject to different input variables
- Investigate projects/business opportunities and risk
- Modeling life expectancy
- Pricing of products
- Use in creating matrix reflecting our business costs
Top
|
| INSURANCE - Partners and
Toolkits |
| Decisioneering is pleased to partner with companies
that incorporate Crystal Ball into their existing software toolkits. |
Radar2000 and RadarXL |
Radar2000 and RadarXL are forecasting and risk analysis models
designed for non-life insurance companies. Forecasting and risk
analysis within insurance is commonly referred to as 'Dynamic
Financial Analysis' or "DFA". Insurance industry regulators
worldwide are moving towards adopting risk based capital methods
and it is likely that DFA will come to form the underlying framework.
Radar provides a 'Dynamic Financial Analysis' capability to insurance
companies, large and small, in an easy to use and cost effective
application. The application runs within Microsoft Excel, hence
we believe that companies will be able independently to use Radar2000/
XL in-house with the same ease as they now use basic operating
and accounting systems.
Today's reliance on specialist consultancies to conduct costly
DFA reviews can be reduced.
In a nutshell, the Radar modelling process involves:
- Inputting proposed insurance business and estimating gross
losses, including reinsurance cover specific details such as
event losses.
- Using Crystal Ball and Monte Carlo simulation, management
and regulators can attempt to evaluate the expected range of
losses for a class of insurance business and determine a probability,
or certainty level to apply in the reserving process.
- Inputting details of the actual and proposed reinsurance program
and other variables, such as interest rates and investment return.
Crystal Ball is also used to assess the likely outcome of such
variables.
Results are expressed in familiar terms, as Profit & Loss
and Balance Sheet, but also in less familiar ones, as probability
and risk adjusted return.
Modelling is an iterative process. Radar allows for the full
range of expected results to be evaluated and the business plan
refined. More importantly insurance company management and regulation
shifts to expected future results and away from the historic;
to dynamic, not static, management.
Click here to visit the Radar 2000 Product page
 Back to Top
|
|
|
|
| |
|
|
