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Roles & Skills for an Analytics Career In Insurance

Roles & Skills for an Analytics Career In Insurance


Mitsui Sumitomo Insurance

Insurance companies are one of the biggest recruiters of analytics talent and their growing needs are unlikely to be met with existing supply.  Here are the three main roles for analysts & data scientists in Insurance companies:



  1. Marketing , Distribution, Product Development: Here is Accenture’s views on how analytics plays a part in these key functions: Customers prefer personalized products and are becoming more price sensitive to product features, information transparency of key product features. Successful marketing and distribution efforts correctly segment customers, channels and markets through predictive analytics. The use of such analytics improves sales by helping insurers identify the right product for the right customers at the right time and delivered in the right ways, improving adoption and boosting cross-selling.
  2. Claims Analytics is the art of analyzing claims by customers to both set underwriting risks & also innovatively reduce future claims. Here is an example by McKinsey on analytics reduces claims in auto insurance- “One UK insurance company using telematics reported that better driving habits resulted in a 30 percent reduction in the number of claims; another UK insurer similarly used telematics to help a large client reduce accident-causing risky driving maneuvers by 53 percent.”
  3. Underwriting Analytics: Historically, insurance companies reviewed less than 1% of policies to study claims patterns and setting up hard coded rules on pricing. However, with advanced analytics and cheaper computing power, these rules are dynamic and set up using advanced machine learning algorithms.

We decided to analyze job postings for analytics & data scientists in the insurance industry to see what skills & capabilities they are hiring for.  We went through about 60 job postings in detail and using text extraction and search criteria here is what we found.

From a statistical skill & capability perspective, the key techniques that insurance firms expect you to be familiar related to predictive modelling are given below:

  1. Bayesian Analysis
  2. Neural Networks
  3. Logistic Regression
  4. SVM
  5. Tree Models
  6. Cluster Analysis
  7. Random Forests

We have shared examples of 5 job postings, their companies, positons & key statistical techniques required for these roles.

Company Position Key Statistical Techniques
Arbella Insurance Manager Predictive Analytics General Knowledge of Bayesian analysis, survival analysis, multi-dimension scaling, decision trees, GAM, neural networks, text mining and geospatial analysis.   GLM, logistic regression, variable selection,  Multivariate analysis, factor analysis, principal components
, Time series
Allstate Senior Data Scientist Expertise in statistical modeling techniques such as linear regression, logistic regression, generalized linear models, tree models (CART, MART), data visualization, cluster analysis, principal components and feature creation, validation.
Progressive Data Scientist Job Experience with NLP or linguistics, representation theory, location preserving hashes, machine vision, social network analysis or recommender systems
American Modern Predictive Analytics Manager Strong analytical abilities including understanding of advanced mathematics and statistical techniques such as GLM/Regression, Classification and Regression Trees, Neural networks, and machine learning.
Verisk Lead Analytic Scientist Knowledge and experience with diverse statistical and data mining methods (e.g. – GLM/Regression, Boosting, Random Forest, Trees, Clustering, PCA, SVM, etc.)

 

So, are you ready to dive into the world of insurance analytics? If yes, here are some resources to get you started:

See Also

  1. Accenture Report on Insurance Analytics
  2. Mckinsey on Insurance & Advanced Analytics
  3. SAS on Insurance & Claims Processing

Author:

Vozag uses data to better understand the world around us. We explore topic & provide commentary on topics using data, numbers & analytics.


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