Back in 1879, Gilbert and Sullivan created an amusing character named Major-General Stanley in the light opera "The Pirates of Penzance." He enters singing a song satirizing the idea of the “modern” educated British Army officer of the latter 19th century:
“I am the very model of a modern major-general, I’ve information vegetable, animal, and mineral... I’m very well acquainted, too, with matters mathematical; I understand equations, both the simple and quadratical; about binomial theorem I’m teeming with a lot o’ news; with many cheerful facts about the square of the hypotenuse.”
What resources enable their efforts?
All the large companies have resources dedicated to predictive modeling. In companies with more than $1 billion GWP, 63% have a team of more than 10 people, and 22% have a team of 5 to 10 people. Sixty-five percent of the companies with less than $1 billion GWP have one to four people dedicated to predictive modeling, and 9% have no resources dedicated to predictive modeling.
Ninety-one percent of survey respondents use external data to supplement their company’s data. Of the companies that use external data, the vast majority (80%) use insurance score or raw credit attributes. Sixty-seven percent use geo-demographical data, and 53% use competitive pricing data. Catastrophe models data and weather data were also mentioned by 46% and 42% of the respondents respectively.
The most common use of predictive modeling is in pricing, where 81% of the respondents use predictive modeling either always or frequently. Another area with substantial use is underwriting, with 52% of respondents using models either always or frequently.
When is it time to deploy or rebuild models?
While some respondents take two to three months (22 percent) or less (10 percent) to deploy new predictive models, as many as 55 percent take more than four months to deploy new models.
Companies that are quicker to deploy new models take advantage of these capabilities with higher frequency of re-estimating and building new models. For example, 53 percent of the companies that take less than one month to deploy new models update their models once a quarter or more frequently, while the majority of companies that take longer only update their models once a year or less frequently.
Profitability is the predominant benefit respondents get from using predictive analytics, mentioned by 85% of the respondents. Additional benefits include risk reduction (55%), revenue growth (52%) and operational efficiency (39%)
Close to half of the respondents use either SAS (47%) or Excel (44%) to build models. Other statistical software used to build models include: R (25%), domain-specific modeling software (12%) and SPSS (6%).
Close to three quarters of the respondents (74%) use GLM/GAM in their model building process. Other algorithms used in the model building process include: regression/classification trees (47%), clustering (34%), principal component analysis / Factor analysis (25%) and text mining (16%).