The insurance business is majorly about risk assessment and management. Successful carriers not only understand risk clearly, but also dynamically use the information to handle exposure to risk through upbeat measures anticipated to avoid or prevent losses, or at the bare minimum, screen, pre-empt and price for them in the underwriting process.
Risk analytics in insurance improves the groundwork for decision-making through a clear expression of business goals and objectives, extra focused information management and a healthier understanding of the trade-offs between risk and reward; thereby streamlining uniform risk appetite to maintain specific credit ratings, manage capital and reduce earnings volatility across insurance companies.
Over the past few years, insurers have drifted beyond simple management information generation and moved into the depths of business intelligence and analytics. The emergence of business intelligence software initiated the evolution of computing in insurance from a tactical, transaction focus to a strategic, business planning focus, and since then, analytical software has expanded its tentacles to specific risk domains beyond finance, operations and marketing.
Risk analytics have proliferated to specialized lines of Risk Management to encompass broad industries like financial services including insurance. During 2003, the Casualty Actuarial Society (CAS) defined Enterprise Risk Management (ERM) as the discipline by which an organization in any industry assesses, controls, exploits, finances, and monitors risks from all sources for the purpose of increasing the organization’s short as well as long-term value to its stakeholders. And advanced risk analytics not only safeguards organisation but also assures capabilities that can provide the opportunity to gain new insights in achieving better internal rate of return and market advantages.
Though not limited to the below mentioned components only, CAS Enterprise Risk Management classifies risks in the following first level Risk segments:
Within insurance analytics, apart from financial risk of managing capital, solvency, economic value, etc., operational risk analytics plays a leading role in risk management as operational risk is spread all across the risk functions and business operations of the financial services and insurance. Moreover, in recent past, operational risk losses have shown a steady increasing trend across the financial industry.
Guidelines for financial services risk analytics largely comes from BASEL norms, COSO framework and Solvency norms, apart from numerous other financial regulations like Sarbanes-Oxley, Dodd-Frank, etc. Apart from the Financial Risk Analytics, Operational Risk analytics constitute one of the key components of risk framework, and sometimes it intersects and overlaps with the control, governance and audit functions.
Risk analytics aids in understanding key loss drivers for significant customer segments and is majorly used for underwriting and claims, two of the most crucial domains in insurance. Past risk analytics helps in underwriting and predictive risk analytics helps in claims management. Sound underwriting and claims risk analytics enhance the insurers risk carrying capacity and assures maximum internal as well as stakeholders’ returns. It can predominantly be applied to manage and better understand a wide arena of risks which are carried by the insurers. Risk analytics can also be done through operational process modelling to arrest the process related risk assessments and their possible remediation measures.
On an enterprise risk analytics model, market risk, credit risk, enterprise risk, liquidity risk, legal risk, and strategic risks are taken as separate unit and detailed quantitative models are prepared for each of these units. The quantitative models in market and credit risks are quite deep-dive and advanced analytical and statistical models are implemented with scenario analysis to cull out the best in predictive market and credit risks. These risk analytics models are also dynamic enough to go through continuous monitoring and remodeling to perform with greater percentage of accuracy.
At times during the course of business, a highly improbable but an inevitable event can occur which has a massive impact on all the business functions – a Black Swan event. In the insurance business, a Black Swan event can be a man-made event or a natural catastrophe, which could be a risk that has not been overtly measured and would lead to a major setback for the insurers and even complete business failure. Risk and predictive analytics aids in transforming many aspects of the insurance business and prepares insurers for handling such unforeseen situations.
Rapid changes in the global property, casualty, life and health insurance markets have noteworthy repercussions for the effectiveness of insurance companies’ existing risk management functions. The insurance industry has both a need and an opportunity to reconsider and improve its strategies, processes, and infrastructures for measuring performance and analyzing risk in order to ensure customer safety. Burgeoning regulatory requirement, higher business complexity and increased focus on accountability have led organizations to pursue a broad range of governance, risk and compliance initiatives across the organization. The insurance sector has been absorbed in grueling processes to adapt to the new economic environments as well as to the increasing levels of safety, transparency and effectiveness which are majorly being demanded by financial institutions, citizens and government alike. Thus, Risk Analytics plays a very crucial role in shaping the future trends of the insurance industry and with effective risk identification, measurement, mitigation and monitoring insurance companies will be able to optimize their business outcomes.