Insurance is a data-driven industry, and thus employs large numbers of analysts to continuously monitor and analyze data. Analysts in the insurance industry have formal education in a variety of disciplines, including statistics, as well as other quantitative disciplines, such as finance, economics, business, mathematics, and computer science. Entry level analysts require a bachelor's degree in one of these quantitative disciplines. Senior analysts may possess an advanced degree. In addition to technical skills, career success requires good project management skills, and the ability to communicate effectively with both management and information technology specialists. Qualified individuals have opportunities to move into management roles.
There is much overlap between insurance statisticians and insurance actuaries. For instance, in Progressive Insurance Company, statisticians play a major role in setting the insurance rates, which is traditionally done by actuaries. Actuaries consistently rank among the most desirable jobs in ratings of professions.
Functions requiring varying levels of statistical skills include:
There is much overlap between insurance statisticians and insurance actuaries. For instance, in Progressive Insurance Company, statisticians play a major role in setting the insurance rates, which is traditionally done by actuaries. Actuaries consistently rank among the most desirable jobs in ratings of professions.
Functions requiring varying levels of statistical skills include:
Pricing and Product Design
Multivariate statistical models are used to predict average losses versus driver characteristics (e.g., driver age, gender, marital status, driving record, etc.), vehicle characteristics, and geographic location.
Multivariate models are necessary to separate out the individual contribution of each of these inter-related variables. Such models are used to accurately set the relative price to charge particular segments of customers.
In order to accurately forecast trends in losses over time, moving averages, linear regression, log-linear regression methods, or time series analysis may be employed.
Claims
Does fast response have an impact on the magnitude of claims payments? If a claims adjuster can get right out to the scene of an accident, inspect the vehicle and meet with our insured and claimants, does that help control the total amount an insurer will pay? A statistical test is used to answer this question.
A new Claims adjuster needs to figure out how much to pay for a herniated disk and a fractured leg. A number of insurance industry vendors offer models that take into account 20 - 30 variables (including doctor's diagnoses, type of impact, location of impact, attorney-representation, etc.) and generate an estimate of how much is typically paid for a particular type of claim feature. These benchmarks are only guidelines, but they provide additional data for adjusters to consider in making an offer.
Marketing
In Marketing, statistical methods are used to model response to advertising campaigns, in order to target advertising to market segments most likely to respond to the campaigns, for example. Designed experiments may also be used to efficiently test different strategies for increasing sales.
Customer retention
Statistical methods such as logistic regression or survival analysis may be used to identify variables that are predictive of how long a customer stays with the company. For example, such models are used to determine the impact of premium increases on whether or not a customer renews his policy. Designed experiments may also be used to efficiently test different strategies for retaining more customers. The results of such customer retention experiments may be used as the basis for actions implemented to increase customer retention.
Operations
In Operations, computer simulation may be used to model call volume, in order to optimize staffing levels. Quality control statistics may be used to monitor and improve quantitative and qualitative measures of service performance.
Insurance companies invest heavily in Information Technology, to capture and store transactional data in data warehouses in a form suitable for analysis. Thus, analysts have at their disposal massive volumes of data. It is not unusual to work with several hundred thousand or even millions of observations, and several dozen to hundreds of variables. As with any data originating from customer transactions, care must be used to recognize possible data errors and to understand the limitations of the data. One must also understand the dynamic changes in the data over time.
Knowledge of basic statistics is essential to properly analyze and interpret insurance data. Knowledge of advanced statistics is helpful to implement multivariate methods and develop better methodologies of data modeling. For example, advanced methods such as cluster analysis, regression and classification trees, logistic regression, and survival analysis can be useful to more effectively identify segments of customers for pricing or marketing purposes.
Tidak ada komentar:
Posting Komentar