IRB Brasil RE has just obtained a license for an English platform of data referencing, unique in Latin America, which can reduce by half the time of the analysis of specific risk exposure, such as floods, earthquakes and hurricanes, on assets. Developed by the English startup Insurdata, specialized in insurance and reinsurance sector, the tool innovates by precisely locating, in high resolution, addresses anywhere on the plant and giving real-time access to different aggregated information.
The technology makes it possible, for example, to locate certain building and knowing how a flood would take place there, as well as to analyze, in just some clicks, thousands of addresses of a company all over the world. On a day-to-day base, it shall allow IRB’s subscribers to assess very precise information and produce highly accurate risk analysis faster. This way, the reinsurer can count on more efficiency and assertive controls.
“It is part of our routine to analyze the market and seek innovative solutions giving room to providing a service that meets more completely the needs of our clients and business partners. With the platform, we improve the resolution of global exposure data supporting our decision on subscription, prices and portfolio management”, says IRB Brasil RE’s CEO and chairperson of the Board of Directors, Antônio Cássio dos Santos, adding that, as a consequence, it is also possible to improve retrocession costs.
Applicable to all the reinsurer’s business lines, the platform shall be used initially by the catastrophe modeling team. “Integrated to our daily work flow, the solution will allow maximizing our process of catastrophe risk analysis, reducing the volatility in the modeled results and giving room to better risk selection”, states IRB’s Catastrophe Risk Modeling manager, Luis Brito.
Insurdata’s CEO, Jason Futers, highlights the role of the partnership: “The decision by a company as large and sophisticated as IRB on adopting our platform is a fantastic signal of our exposure and technology methodologies”. He reinforces that, while improving exposure data, “the objective is offering the subscribers higher trust on its estimates on modeled losses, leading to a better risk selection and portfolio management.”
