Harness Mature Cyber Catastrophe Models to Make Critical Business Decisions

Cyber cat modeling has come of age: advances in machine learning, data aggregation and novel usage of science are enabling P&C insurers to make crucial business decisions. Understanding the impacts and drivers of cyber “perils” from Data Breaches, Ransomware/Malware, Business E-mail Compromise and service provider outages, and the impacts that these have on insurable coverage, is now possible with far greater accuracy and transparency than ever before.

But what are the critical factors in cyber cat models that are enabling P&C business decision making? How has transparency and rigorous modeling increased insurer confidence in risk selection and pricing? Has model maturity now made ransomware insurable?

Find out in this webinar, where you’ll hear from:

• Michael Colao, Global Chief Underwriting Officer, Cyber, AXA XL
• Jamie Pocock, Head of GC Cyber Analytics - International, Guy Carpenter
• Matthew Hogg, Strategic Head of Cyber Underwriting, Liberty Specialty Markets
• Matt Harrison, Director, Product Management, Cyber, RMS
• Moderator: Wyn Jenkins, Managing Editor, Intelligent Insurer

By registering for the webinar, you will:

• Identify the P&C assets that are most susceptible to systemic cyber events and assess future threat levels to accurately inform business decision making.
• Assimilate critical information on whether you should be covering ransomware, discover the implications of disclosure bias and future proof your decision making from factors including the legality of fines
• Define the key cyber perils in portfolios that will be the most likely drivers of accumulated risk and define strategies for mitigating these
• Create an actionable plan to capitalize on uncertainties in the cyber (re)insurance market, improve your portfolios to maximize certainty and RoE, and gain confidence in what coverage you should be offering to whom

This webinar is sponsored by RMS. By signing up you agree to share you data with this organization.