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Data opportunities in Specialty Insurance Part 1. Introduction
An introduction to a series discussing Specialty Insurance analyticsgoes here
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Data opportunities in Specialty Insurance Part 2. Risk should always equal Appetite
Part 2. Risk should always equal appetite, wherever and however we look
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Data opportunities in Specialty Insurance Part 3. Risk does not equal appetite
Risk should equal appetite, but if we care to look, we find it otherwise
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How to justify more Analysts in Specialty Insurance – the value of explicit targets
There is so much profitable low hanging fruit for risk analytics that a simple dollar centric measure can help focus the allocation of resources to harvesting the fruit.
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Protected classes and analytics in Specialty Insurance
Analysing Risk with new methods will force Insurers to be more proactive in finding inevitable pockets of discrimination.
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Analyzing relationships between risk factors
The 2020 article in Annals of Actuarial Science ‘Home and Motor insurance joined at a household level using multivariate credibility’ shows how realtionships between factors are not revealed by the usual analyses.
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Better risk modelling with neural networks
The application of neural network-based models for conditional quantile estimation in non-life insurance can significantly impact profitability by providing better risk assessment and premium pricing strategies. By leveraging neural network-based models, insurers can enhance their risk assessment and premium pricing, leading to better differentiation between good and bad risks.