Question

Analytics is now at the forefront of the insurance industry. please describe the applicability of analytics to managing, retaining, and transferring risk. describe briefly the tools that are being deployed: Product Lines Quantified; Total Cost of Risk (TCOR); Global Peril Diagnostic; Stress Tests and; Peer Analytics.

Answer 1

The customer service industry is awash with examples of how consumer data is used effectively to personalize experiences, brands and product offerings. However, practical examples for the risk management and insurance industry are harder to find. Big data should be a key tool for our industry, but it’s become a nightmare. It’s one thing to collect and interpret as much data as you can get your hands on, but to do it in a way that makes a difference to your business goals is the challenge. End-to-end digitization helps. The more the claims process is digitized, the more data you have available, the more insight you will have for risk management and market segmentation.

Data analytics serves many purposes with respect to claims. For instance, loss statistics allow companies to identify repetitive causes. Once these are solved you can reduce their frequency and save money in the process. This requires complete insight into all losses wherever they occur. The same goes for more sophisticated predictive risk models. You need a complete and consolidated data set you can mine for information. The aim is to process data from all incidents across, at scale, in a uniform way. Information ranging from losses to policies and customers’ personal details is logged.

Risk profiles are also used in the insurance industry. This enables insurers to segment consumers based on their desirability and design customized solutions. It involves using enriched customer profiles, which are based on policy details, insurance application information and any claims someone may have had. All this data is used to build up a detailed picture of each customer.

The type of insurance policies each of us takes out tells us a lot about our respective lifestyles. If you combine claims and policy information you can see that there’s a connection between risk appetite, defined by the level of insurance taken out, and the actual risk posed, defined by the number and extent of losses. Profiles can now be enriched further by additional information that’s available from voluntary sharing schemes such as car telematics.

Creating risk profiles for certain customer groups also allows accurate market segmentation and the design of more personalized services and pricing schemes. Take safe-driving and usage-based schemes for motor insurance, these have paved the way for more accurate tariffs for motorists.

In the risk management industry, the accuracy of pricing hugely impacts the risk transfer strategy. Get it right and both risk managers and their insurance providers benefit. The key to all this is information. If data is structured, centralized and reliable, you can make comprehensive risk assessments. You will also have a complete picture of the losses incurred throughout the company. This is the basis for a robust pricing, risk mitigation and risk management strategy.

Tools -

Total Cost of Risk (TCOR) - Simply put, TCOR is the cost of managing risks and incurring losses. Total cost of risk is the sum of all aspects of an organization's operations that relate to risk, including retained (uninsured) losses and related loss adjustment expenses, risk control costs, transfer costs, and administrative costs.

Global Peril Diagnostic - This sophisticated diagnostic model evaluates your property portfolio to assess natural catastrophe exposure, designed with your needs in mind. This tool provides clarity to your exposure to terrorism and a range of twelve natural perils which put your global assets at risk. Our holistic diagnosis empowers you with a strategic foundation for smart next level analytics.

Peer Analytics - provides investment risk-skill analytics and consulting to insurance companies, endowments, family offices, and pension fund clients. Robust risk and skill estimates, using statistical risk models built with passively-investable factors, that are strong predictors of future performance.