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Predictive Analytics (PA) in Insurance Industry has been high on the adoption rate. Senior IT Managers in Insurance sector understand the importance of it as it encompasses a variety of techniques from statistics, data mining and game theory to analyze current and historical facts to make predictions about the future.
Sekhar Pidathala, AVP - IT Governance & Secrity, Axa Business Services asserts, “ It exploits patterns found in historical and transactional data to identify risks and opportunities allowing models to capture relationships among many factors. Predictive Analysis also helps increase the retention level of existing customers”
Most well-known application of PA is in credit scoring, used in financial services, which amalgamates customers’ credit history, loan applications, customer data, etc., to rank the likelihood of future credit default
Analytical Customer Relationship Management (ACRM) a recent commercial application of PA is a way by which insurers can try be ahead of innovation curve
ACRM as a business strategy is designed to optimize profitability, revenue and customer satisfaction, by developing the entire enterprise around customer segments, enabling and encouraging customer-centric behavior and processes
Predictive Analytics in Insurance Industry
Technological Advances
The statistical techniques used in predictive analytics are computationally intensive. Depending on the amount of data they use, some require performing thousands or millions of calculations. Advances in computer hardware and software design have yielded software packages that quickly perform such calculations, allowing insurers to efficiently analyze the data that produce .and validate their predictive models
Data Availability
The quality and quantity of the data available explains the validity of any predictive model. While most of the insurers have sufficient amount of data available, information on legacy systems may not be compatible with systems running predictive analytics software. Converting data on these legacy systems to a usable format can be time consuming and expensive
In addition to the proprietary data, there are numerous third party sources of data that insurers can use to develop predictive models. Such sources include rating bureaus, regulators, advisory organizations, rating agencies, predictive modeling companies.
Insurers’ Search for Competitive Advantage
The final driver for the use of predictive analytics is the insurers search for competitive advantage. This is related to insurers desire of growth because the desire for market shares leads them to seek advantages over their competitors. If the predictive model’s rating or score for applicants accurately forecasts behavior, the insurer can more efficiently define the target market, develop accurate pricing and handle claims efficiently. All of these provide a competitive advantage over competitors who do not use predictive analytics.
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