The global insurance industry is evolving and has leveraged technology to simplify some of the core operations of insurance to achieve profitability targets. Insurers need to identify the following:
Without this data, the focus may defer to low priority areas or increase the insurance premium without due analysis. Unfortunately, this type of strategy negatively impacts both new prospects and existing customers, bringing decline in their existing GWP. To overcome this, insurance carriers need a thorough analysis of their portfolios and adjust their pricing based on risks projected over time.
Our cognitive technology-driven intelligent solution will help insurers in identifying key segments based on profitability forecast of customers and define optimal price for the risk underwritten. This will increase both insurer revenue and customer base. To arrive at this model, a detailed analysis of records is done before cleaning, correcting, and segmenting into meaningful format. Key attributes are identified and applied on the XGBoost algorithms for predicting outcomes and based on that a Premium Projection Model is created with 91.7% accuracy.
Our intelligent solution is a combination of machine and deep learning integrated with predictive analytics. It analyzes historical data and predicts the likelihood of new users/prospects accepting new business quotes and policies on renewal. This cognitive solution will provide insurers with projections based on the premium forecast (GWP) they plan to write in future (monthly, quarterly or yearly). It will help insurance carriers in optimizing the pricing for an individual customer based on certain attributions, and by supplementing the rate structure. However, the insurance market is unsteady, and insurers should consider future uncertainties to implement impactful business decisions in appropriate market segments. The intelligent solution aims to identify these segments and provide insights to assist in the decision-making process. The model carries out future projections of GWP by running predictive algorithms on historic transaction parameters.