What do you do if you need to collaborate with data analysts for risk assessment insights?

You can share the risk factors you identified and ask data analysts to: Analyze past data for similar risks' impact (e.g., past security breaches). Identify trends suggesting future risk likelihood (e.g., rising customer complaints). This helps quantify risks and prioritize based on real data.

Kickstart your collaboration with data analysts by conducting a focused workshop aimed at defining the specific risk assessment needs of your insurance operation. For example, if aiming to better understand the risk associated with climate change on property insurance, clearly outline the geographic focus, types of properties of interest, and the particular climate variables (like flooding or wildfires) that concern you. This targeted approach ensures data analysts can efficiently select and apply the most relevant datasets and analytical techniques, directly aligning their work with your strategic risk management goals.

Collaborating with data analysts is akin to the chicken-or-egg-first theory. Not only is it part of defining the risk, but its also about how the risk event will be measured. Data analysts point the way and create and query data sets that will inform your judgement of the likelihood of the risk event occurring and the costs of covering the financial consequences of that occurrence. That will enable you to make more accurate decisions when pricing insurance or obtaining insurance quotes for clients.

When collaborating with data analysts for risk assessment insights, you can start by clearly outlining the specific risks you're evaluating. Then, work with the analysts to identify relevant data sources and choose appropriate analysis techniques. Once they've extracted insights, you can discuss the findings together to understand their implications for risk management strategies. This collaborative approach ensures you leverage the power of data analysis to gain a more comprehensive understanding of potential risks.

Para que la colaboración tenga buenos resultados, es importante tener claro el objetivo que se busca con la evaluación de riesgos, el tipo de riesgos que se desea analizar y las características del negocio o empresa que va a ser analizada; ya que es la base para determinar correctamente las amenazas o vulnerabilidades de la organización, es el punto de partida para evaluar probabilidad e impacto; así como determinar tratamientos adecuados para mitigar los riesgos. Con la información clara, relevante y completa los resultados de las colaboraciones e interacción de los analistas de datos serán exitosas; la identificación de tendencias, patrones, anomalías ayuda en la toma de decisiones para ser proactivos y no reactivos en la organización.

Explain why you need to obtain particular confidence levels from the data analysts when you explain the nuances of an insurance policy. Facilitate their explanation of what data they would use and how they will query the data sets. Translate that for them into the risk factors of the insurance policy and the pricing model for that insurance policy.

One thing I've found helpful is to take the data analysts' presentation and insights and turn it into a draft of the presentation I would be discussing with my fellow executives. I would then present the draft to the data analysts and ensure that I have properly understood the data relationships and it accurately represents the risk assessments I propose to make. The data analysts should feel comfortable asking you questions and requesting further explanations because that is key to aligning their insights with your insurance expertise. Following that workshop you will be comfortable finalizing the draft and explaining the implications to your fellow executives.

Enhance the communication of data findings by adopting a 'layered presentation' approach. Begin with a high-level summary that outlines the key insights and their potential impact on insurance risk management. For instance, if the analysis reveals a significant increase in claims related to cyber threats, start with a broad overview highlighting this trend. Then, provide layers of detail, allowing stakeholders to delve deeper into specific data points, such as the types of cyber incidents most prevalent and the policy areas most affected. This method ensures that executives can quickly grasp the strategic implications, while also having access to the underlying data for more granular decision-making.

Upon receiving insights from data analysts, prioritize a 'pilot program' approach to test the implementation of new risk models. For instance, if data analysis identifies an emerging risk trend in commercial property insurance due to urban flooding, initiate a pilot program in a high-risk urban area. Adjust policies and premiums based on the new model and closely monitor claim patterns and customer feedback. This pragmatic approach allows for real-time adjustments and scaling, ensuring that the implementation of insights is both effective and adaptable to changing risk landscapes.

Consider your feedback loop beyond the pilot. What data should the analysts continue to accumulate and measure and how are they going to do that? This is a question to be answered in collaboration with the data analysts. Subsidiary questions are: how will the feedback be handled? Which elements of the feedback will trigger a review? How will the data analysts inform the feedback loop of data that suggests different or new insights?