The P&C industry faces long-term pressures in terms of both growth and profitability. The low interest rate and regulatory environment will continue to put pressure on (re)insurers to generate underwriting results to deliver earnings. The ability to do that will be constrained as the market softens. Excess capital and continued competitive pressure will limit the ability to raise prices.
The latest trends in technology can allow (re)insurers to better understand the exposure and risk at the time of underwriting. It can reduce the overall risk of portfolio while maintaining the profitability.
Our data management platform can replace otherwise pain-staking and time consuming component of Property Premium Quote generation- Loss estimates. The framework offers extremely simple user interface for you to analyze vast amount of data on a single screen. Our framework aims at supporting Underwriters to accelerate the turnaround time and deliver superior customer service.
Traditionally a painstaking, time consuming but absolutely critical component of the risk assessment process, advanced data generation for high risk value properties is now one step ahead.
Success of risk analytics depends on precise property location, better measurement of the area and parameters of the properties and a better understanding of the surrounding of the property.
Our remote sensing and image processing technologies extract property and location features using Satellite and Arial imagery ensuring high level of the data inputs for better loss estimates.
Prediction of probable future losses to buildings in the account and portfolio from uncertain natural hazards is a primary need of property insurance and reinsurance industries. Catastrophe models judiciously capture natural randomness of the hazard and that of the response of the structures to improve decision making. These uncertainties associated with the hazard and damage estimation are normally termed as primary and secondary classes of the uncertainties. The hazards and responses are immensely complex in nature and any attempt of capturing these phenomena through a simplified mathematical models adds to the uncertainty due to limited data and knowledge. The hazard event generation and damage estimation components of the CAT models are the principle sources of modelling uncertainty for the primary and secondary classes. However, the modelling uncertainty is inversely proportional to the knowledge or data of the physical phenomenon and therefore reduces as more knowledge or data becomes available. At ARAPL, we reduce the variance of the losses by eliminating the numerical simulation of hazard events and by employing building specific damage ratio functions. Our approach is computationally inexpensive and provides a consistent estimate of average annual losses and tail event losses for different properties in the account, which are distributed over a large area.
We have the expertise of 20+ years in the field of insurance and we provide consultancy services for all risk management and asset management requirements of your organization. We've been passionate about achieving better results for our clients- results that go beyond financial and are uniquely tailored, pragmatic, holistic and enduring.
We find the solution to your problem by using our team of expertize to assess and the interpreting your organization data.