In partnership with Sønr, we will be hosting a highly anticipated pre-event on October 31, 2023, at the prestigious ITC Vegas Mandalay Bay. This exclusive gathering will bring together key partners to showcase innovative strategies for enhancing current business models and operational environments within Commercial Lines companies. Building upon the fruitful discussions we had at ITC-DIA Barcelona a few months ago, this event aims to drive the conversation forward.
Make sure to pre-register here.
At ITC-DIA Barcelona in June 2023, industry experts gathered to discuss the concept of computable risk and its implications for the insurance industry. The panel emphasized the potential for machines to compute and price risks without human intervention, highlighting the digitization of contracts and the generation of more data as key drivers for increased standardization and commodification of risks. Algorithmic underwriting and the importance of dynamic, data-enriched algorithms were explored, along with the integration of AI, open data, satellites, and IoT to create digital profiles of properties for risk assessment purposes. The overarching goal was to automate processes and enhance efficiency in risk management. This article delves deeper into the insights shared during the panel discussion, providing examples that illustrate the significance of leveraging data insights, proactive risk mitigation measures, collaboration between insurers and risk managers, and addressing challenges for small and medium-sized enterprises (SMEs).
Leveraging Data Insights for Effective Risk Management:
One of the key takeaways from the panel discussion was the critical role of data analysis in managing risks effectively. By leveraging comprehensive data sets and employing advanced analytics techniques, insurers can gain valuable insights that enable them to make informed decisions and optimize risk assessment. For example, by analyzing historical data related to property damage caused by natural disasters, insurers can identify high-risk areas prone to specific hazards. This insight allows them to adjust pricing strategies, develop tailored risk mitigation measures, and provide more accurate coverage to policyholders in vulnerable locations. The use of predictive modeling based on historical claims data can also assist insurers in estimating future losses and adjusting their reserves accordingly, ensuring they have sufficient funds to cover potential claims.
Proactive Risk Mitigation Measures:
The panel discussion emphasized the importance of proactive measures in mitigating risks and minimizing their impact. Flood forecasting technology was highlighted as a prime example of how advanced systems can help insurers and risk managers take preventive actions. By analyzing data from weather monitoring stations, river sensors, and satellite imagery, insurers can receive early flood warnings and assess the potential severity of an upcoming event. This enables them to notify policyholders in affected areas, implement evacuation plans, and secure additional resources to provide timely assistance. Such proactive measures not only reduce the potential losses associated with floods but also enhance the overall resilience of communities and promote a more proactive approach to risk management.
Additionally, advancements in IoT technology have enabled the use of smart sensors to monitor risks in real-time. For instance, in commercial property insurance, IoT sensors can be installed in buildings to monitor factors such as temperature, humidity, and air quality. This continuous monitoring allows insurers to detect potential issues, such as equipment malfunctions or hazardous conditions, and provide early warnings or maintenance recommendations to policyholders. By preventing incidents or promptly addressing them, insurers can reduce the frequency and severity of claims, leading to improved loss ratios and increased customer satisfaction.
Augmented Underwriting with AI:
The panel discussion also touched upon the concept of augmented underwriting, where AI technologies assist in human decision-making processes. AI-powered algorithms can process vast amounts of data quickly, enabling insurers to assess risks more accurately and efficiently. For example, in the assessment of life insurance policies, AI algorithms can analyze medical records, genetic data, and lifestyle factors to evaluate an individual’s risk profile. This not only enhances underwriting accuracy but also speeds up the process, allowing insurers to provide policy quotes more rapidly and improving the overall customer experience.
While AI can provide significant support, the panel acknowledged that human judgment remains vital in making final decisions. For instance, in complex commercial underwriting, where the risk factors are multifaceted and may involve subjective judgment, human underwriters play a crucial role in assessing the qualitative aspects of risk. AI algorithms can assist by providing data-driven insights and recommendations, but the final decision-making still requires human expertise and consideration of broader contextual factors that AI may not fully comprehend. Therefore, the panel stressed the importance of finding the right balance between AI capabilities and human judgment to ensure optimal risk assessment and coverage.
Collaboration and Efficient Data Usage:
The panel discussion highlighted the significance of collaboration between insurers and risk managers in making risk management easier for both parties. By working closely together, insurers can gain a deeper understanding of their clients’ risk profiles and develop customized risk management solutions. For instance, in the commercial property sector, insurers can collaborate with property owners and managers to access relevant data regarding the property’s construction, maintenance, and occupancy. This collaborative approach allows insurers to make more accurate risk assessments, tailor coverage options to specific needs, and provide value-added services such as risk engineering consultations to help mitigate potential risks.
Efficient data usage was stressed as a fundamental aspect of effective risk management. The panel emphasized the importance of data sharing with clients and promoting transparency in risk assessment processes. For example, insurers can collaborate with clients to share relevant data on property safety measures, disaster preparedness plans, and maintenance records. In return, insurers can provide insights derived from comprehensive risk analysis and help clients identify areas for improvement in their risk mitigation strategies. By leveraging automation and AI models, insurers can streamline data processing, enabling faster risk assessment and decision-making. This not only benefits insurers but also empowers clients to actively participate in the risk management process and reduce their exposure to potential hazards.
Addressing Challenges for Small and Medium-Sized Enterprises (SMEs):
The panel acknowledged the unique challenges faced by SMEs in obtaining adequate insurance coverage. Traditional underwriting processes may not be suitable for small businesses due to limited data availability or complex risk profiles. However, advancements in technology and data aggregation present opportunities to address these challenges. Insurers can leverage technology platforms that aggregate data from multiple sources, such as government databases, industry-specific data providers, and IoT devices, to gain a more comprehensive view of SME risks.
For example, insurance companies can utilize open data sources, such as publicly available economic indicators and demographic information, to assess the business environment and identify potential risks associated with a specific location or industry. This broader perspective enables insurers to tailor coverage options to the unique needs of SMEs and provide more affordable and accessible insurance solutions.
Furthermore, technology-driven solutions can help simplify the underwriting process for SMEs. By utilizing user-friendly online platforms and leveraging automated risk assessment models, insurers can streamline the application and approval process, reducing the administrative burden for both insurers and SMEs. This digitized approach not only enables faster turnaround times but also encourages SMEs to prioritize risk management by making insurance coverage more accessible and understandable.
Conclusion:
The panel discussion on computable risk and data insights in proactive risk management highlighted the transformative potential of leveraging data analytics, adopting proactive risk mitigation measures, fostering collaboration between insurers and risk managers, and addressing challenges faced by SMEs. By harnessing the power of data and technology, insurers can make informed decisions, optimize risk assessment, and provide tailored coverage options to policyholders. Proactive measures like flood forecasting technology enable insurers to mitigate risks and minimize losses, enhancing overall resilience. Collaboration and efficient data usage promote transparency, empower clients, and improve risk management outcomes. Addressing challenges for SMEs through technology-driven solutions ensures broader access to insurance coverage and supports the growth and sustainability of small businesses. As the industry continues to embrace computable risk and data-driven approaches, insurers are well-positioned to navigate a rapidly evolving risk landscape and provide their clients more effective risk management solutions.