La industria aseguradora debe estar preparada para los retos que le depara el mercado, destaca la empresa Ovum. De acuerdo con un análisis global, las aseguradoras pueden mejorar su competitividad al implementar tecnología de análisis predictivo. El reporte de Ovum sugiere que con el objetivo de estar preparadas, las aseguradoras deberían de enfocarse en iniciativas creativas para analizar datos de diversas fuentes de información, incluyendo social media, third parties y comunicaciones maquina-a-maquina. Más abajo fragmentos del comunicado de prensa.

A new report from Ovum suggests that in order to be better prepared, insurers should focus on creative initiatives that analyse data from a number of sources, including social media, third parties and machine-to-machine communications. By concentrating predictive analytics on key areas of the business – namely business operations, marketing and customer relations – insurers will be able to determine which markets to enter or leave, shape target market initiatives and estimate potential losses for the book of business as each customer is added.

Insurers are already well aware of the impending threats/challenges including tightening regulation, demanding customers, an ageing population and weakened economies,” says Barry Rabkin. “The critical differentiator for insurers will be minimising future risk thorough predictive analytics by tapping into the vast amounts of rich data.”

Finally, Rabkin reveals that a new role is emerging – the data scientist role – and this will be as important as the well-known data miner role in the insurance industry. Ovum is already seeing a growing number of insurance companies creating new departments of these types of quantitatively skilled professionals. This role will take a deeper approach to research (i.e. collect more data and explore more hypotheses) and generate recommendations that are scalable down to individual lines of insurance business. Ovum believes that data scientists and data miners will work closely together to discuss «what if» questions regarding their predictive analytics initiatives.



Por Editorial