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How AI Tackles Reinsurer's Risk Environment?
It is no secret that AI is a disruptive technology that is here to stay, with many use cases that can be applied to the reinsurance industry.
FERMONT, CA: Artificial Intelligence (AI) has been proliferating at a rapid pace in the insurance and reinsurance industry. This growth occurs at the convergence of three significant technological trends including, big data, the normalization of human-machine interconnection, and advancements in machine learning. However, the increasing use of AI raises numerous risks. The questions to be addressed seriously are, how can the insurer handle the change in the risk profile due to the biometric, assets, casualty, economic, organizational, and strategic risk effect of AI? How can the insurance industry take advantage of AI's potential? AI's growth would affect the reinsurance industry. Here's how AI is helping to tackle reinsurers' risk environment.
• Monetary Risk
The business activities of reinsurers include asset management, and many firms have in-house investment managers. AI can help in the management of reinsurers' investment pertaining to liquidity and credit risk. To date, bid/offer spreads have primarily been used by investment managers to measure liquidity costs. Now information on transaction costs and volumes can be collected to help better understand potential liquidation costs in extreme events in terms of how likely large fund flows are expected to be and how long it takes to liquidate such positions. Non-parametric neural networks can incorporate hundreds of factors to enhance large-flow redemption probability assessments. Asset managers are developing their machine learning capabilities to take advantage of AI's benefits by combining market risk and liquidity risk analysis to support investment, risk management, and regulatory duties of fund managers.
• Medical Risk
In the future, many facets of workers' compensation insurance and related medical markets can change dramatically due to AI advances employed across the spectrum of underwriting and claims. AI can directly influence risk selection and price accuracy in two respects in underwriting: firstly, the deployment of AI-based monitoring tools, which are dynamically capable of proactive controls in hazardous or accident-prone environments, and secondly, about loss and pricing. AI advancements are used by data processing carriers to define submarket strategies and client targeting. With no specific vanguards, numerous insurance and new market players work to supplement the capacity of underwriting to minimize costs, and more appropriately cost risks are commensurate with more granular sub-profiles.
• Property Risks
Automated devices such as self-driving cars, manufacturing, forestry, mining, telematics, and warfare may have profound implications for property insurance. Since human error is the leading cause of accidents, the broader use of autonomous machines may reduce such failures and losses of property that could be caused. Defining and determining responsibility to reinsurers will be more difficult due to grey areas where a system fails, and an accident occurs. Reinsurers will also need to evaluate their risks in the medium and long term, especially in the context of transition periods when human and autonomous machines coexist.
• Functioning Risks
In terms of operational risk management, AI can help reinsurers by not only reducing operational risk with process automation but also improving operational risk detection and prevention. AI can be used to prevent fraud, detect money laundering, bribery, and strengthen compliance with the business. Smart chatbots, for example, allow insurers to provide customer support and financial advice 24/7 and are not limited to regular working hours. Intelligent chatbots can be trained to cover all kinds of products, all sorts of financial advice, and all possible issues that may not be possible for human customer support staff. Not all AI functional changes are successful, however. For example, in the context of emerging AI technology, reinsurers need to evaluate their cyber threats.
• Life Risks
Predictions are required for health and lifestyle sensory technologies that provide data on various measures that can be evaluated and translated into insurance. The increased data available from lifestyle type sensors provide reinsurers with the opportunity to improve cost risks. Deep learning techniques make it possible for reinsurers to identify better and predict data connections. Reinsurers also leverage smart sensor-based services as part of their products intending to improve customer health monitoring and promote a healthier lifestyle while, at the same time, providing the reinsurer with access to a wider health statistics data pool.
It is evident that for reinsurance, AI will be a game-changer. Insurance customers will have better access to data in a decision-making and can benefit from a more efficient and simplified insurance system. Also, with AI, reinsurers will have more information at their disposal to make more informed decisions, offer improved risk management capabilities to support risk transfer, minimize manual processes within their organizations, and strengthen their risk management capability.