Specialty Insurance Analytics: AI Techniques for Niche Market Predictions
Keywords:
Adverse selection, affordability, artificial intelligence, generative model, machine learning, niche market, premium prediction, specialty insuranceAbstract
Industry consolidation and a war for talent has sparked a surge in the development of InsurTech start-up firms. Because established insurance companies take a conservative regulatory stance, specialized insurance is an emerging market for coverage. The rapid accumulation of business data in high-dimensional vector fields opens up an opportunity for machine learning solutions based on huge datasets. However, usage of machine learning in specialized insurance is a frontier market that is mature. Gaps exist in availability of domain knowledge and machine learning guidance on a shared platform for start-ups, generalists and experts. It is an urgent need to construct comprehensive guidance on AI solutions for niche market predictions by making sense of insurance vernacular through the lens of word2vec and the architecture of a suite of supervised and unsupervised machine learning. This modeling framework capitalizes on fast search speed and high numerical efficiency of eigenvalue decomposition to remedy the curse of dimensionality while offering an excellent graphical visualization. The versatile modeling power and generative theory of neural networks built on local learning can set up the next decades of AI development while shaping a product-rate predictor. The proposed approach has been tested on telematics auto, specialty insurance, and exploration insurance to exhibit its applicability in real market phenomena. A path forward for insurers and GTs is to adopt this open-source toolbox of auto machine learning, time series and large network applications to create a huge behavioral database for precise pricing. A case study of spam detection is discussed to illustrate the tool for tens of decimal hypotheses testing which is a nightmare for manual analytics propensities in terms of number of hypotheses and number of linear constraints.
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