Critical Success Factors for Rate Making with SAS ® Enterprise MinerTM

Rate making and risk-based pricing give users a definite competitive advantage in the general insurance business. This advantage is all the more compelling if the company is an industry leader. This paper attempts to tie the theory of rate making to technology and business definitions and describes how to set a benchmark for validation and customer handover to ensure a successful rate-making project. The objective of this paper is to reduce the number of iterations involved in the development of a rate card, or formula, and to discuss available options. To underwriters, this paper provides a road map and proven best practices. To project managers, it offers a guide to scope commitment, dependency tracking, and progress tracking. To analytical developers, this paper describes the business definitions and thresholds that impact the acceptance of results. This paper presents the life-cycle of a rate-making project and highlights how the advances in theory and technology can be usefully leveraged to price insurance products tactically. THE CHALLENGES OF RATE MAKING Rate making is the process by which a company, using a wide assortment of data, attempts to predict both the likelihood that a claim will be made and the total amount of claims in a policy period in order to price products accordingly. To do this, insurers must quantify the risks they are willing to assume and the premiums they will charge to assume them, taking into account the overall goals of the company and, perhaps, government regulations. The imperatives in rate making are as follows: 1. The need to set the price of the product before knowing the cost of the product: Pricing insurance products is unlike pricing manufactured goods. In the latter case, we know the costs before we go to market. In the former, an insurer has no way of knowing what the realized cost per policy will be because it depends on whether the policy buyer and others in that financial year will file claims and, if so, how many claims will be filed and how large these claims will be. 2. The need to compete on price but still ensure overall solvency: Insurance is becoming increasingly complex and the key to survival is to ensure that the loss ratio of an insurance company achieves the desired target. Competitors can emerge with innovative rate structures, targeting particular low-risk "good customers" by offering attractive rates. Actuaries have the challenge of accurately forecasting the risk of people filing claims—which requires a powerful solution that can take into account numerous factors. 3. The need to balance strategic, tactical, operational, personnel, and project management priorities: A pricing plan that is too complex is difficult to operationalize and maintain in IT, marketing, and financial environments. On the other hand, a pricing plan that is too simple does not give the advantage of differential pricing. Pricing that is too aggressive is detrimental to solvency and pricing that is too high might cause a loss in market share. Therefore, it is imperative that a rate making initiative balance strategic, operational, and staffing needs. Rate making requires the involvement of analytically skilled insurance and managerial personnel and IT professionals with knowledge of the data sources involved. THE CONCEPTUAL SOLUTION FOR PRICING INSURANCE PRODUCTS The solution to the rate-making challenge must be based on policy characteristics that are submitted at the time of underwriting (for example, characteristics of the policy owner, other individuals covered, property covered, and so on). The only way to infer the loss propensity with respect to these characteristics is by historical analysis of the link between the policy characteristics and the corresponding losses. Conceptually, the solution must 1. Charge a price to every prospective customer, so that the pool of collections in a term is sufficient to service the overall claim amount (in that term) filed by prospective customers. 2. Charge a relatively higher price to prospects who are either more likely to file claims than others or who are likely to file larger claims than others. SAS Global Forum 2007 Data Mining and Predictive Modeling