Ventiv Technology

RISK MANAGEMENT Technology | | 6 Follow us @SRAsia_editor Like all competitive races, whether on the track or off or even the famous space race, advances by one will necessitate and drive advances by all. When it comes to the insurance world, insurers are increasingly turning to advanced analytics to help them price risks. At a recent meeting of chief executives from five global property insurers, the panel agreed that "if you aren't doing advanced data and modelling, in the next three to five years, you simply won't be around any more". All of the major insurers are staffing highly advanced data scientists to develop various risk models to address both underwriting and reinsurance demands. Insurers have captured vast amounts of basic underwriting information from their clients over the years, as they furthered their understanding of insured risks and made internal decisions on what parts, if any, of those risks to retain and which to reinsure. Those models have tended to focus on the analysis of 'macro' information. But with the huge volumes of data now at hand, insurers will move beyond the macro and into the micro, using predictive analytics to aid underwriters in making delicate decisions on what to write and what not to write, and which risks are outside their area of underwriting comfort. The impact to the risk manager, particularly those with large property portfolios, is that underwriters may begin to know more about the risk than the company does. This places the risk manager at a decided disadvantage in coverage terms and conditions negotiations. Without the relevant data, analytics and risk models, the risk manager is at the mercy of the analysis and conclusions of the insurers. Just as athletes of today do not run without having analysed their training performance, surely the risk manager wants to enter their coverage negotiations properly prepared for the discussions. The more information that is at hand, the better job the risk manager and their associates can do in fully defining the scope and quality of their risk, and which part of it they wish to transfer, assuring that underwriters respect their depth of knowledge and the quality of the data. For example, many insurers model catastrophe exposures based on their own defined set of risk factors. In Australia, for instance, about 85% of the population live along the coast, with some areas regarded as having a high exposure to extreme weather conditions such as cyclones and tropical storms. However, if a risk manager is able to provide their insurers with a detailed analysis of their exact geographic locations and mitigating factors such as construction type or risk control measures that have been implemented, they will be in a much stronger bargaining position in discussions over risk transfer. Further, the risk manager also needs to capture data for themselves (and their brokers) to enable their own accurate loss modelling. This will provide them with their own robust understanding of losses to be retained under alternative deductible structures or different risk financing SPONSOR'S VIEW THE ANALYTICS RACE: INSURER VERSUS RISK MANAGER BY JUSTIN GALE ASIA-PACIFIC MANAGING DIRECTOR, VENTIV TECHNOLOGY mechanisms, and those to be transferred to the commercial insurance market. This enables the risk manager to optimise their total cost of insurable risk by identifying the most cost-efficient retention model, incorporating premiums and retained losses. The amount of data available to risk managers should not be a subset of the data in the hands of the insurers or brokers, rather it should be the other way around. The risk manager simply needs a structure to capture and maintain their data for analysis and reporting as needed. A risk management information system (RMIS), properly implemented and with a comprehensive dataset, is the risk manager's answer to the data challenges. Simply becoming a 'data hoarder' is not the answer, however. Just because your RMIS system has lots of data, you do not necessarily have the answers. The key is to understand what information is needed and when to support effective analysis and modelling – then making sure that the raw data elements needed to produce the desired results are captured by the RMIS system on the front-end intake processes. To accomplish this, the risk manager should work with their broker or agent and the insurers to conceptualise the output requirements, and then develop strategies to capture the necessary data in the most efficient way. That data should include historical information, to the extent that it is accurate, plus business processes to capture the data going forward. A well-populated exposure dataset is a core building block for risk managers when discussing risk transfer strategy and needs with the insurer community and their board. By embedding advanced data analytics into your risk management strategy, it is one of the best ways to match and keep ahead of the insurance carrier community. Underwriters may begin to know more about the risk than the company does. This places the risk manager at a decided disadvantage in coverage terms and conditions negotiations" Justin Gale Ventiv Technology

• Cover