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Summary
- Data quality and validation are essential for effective risk management, especially in complex and dynamic environments. However, traditional insurance policies are often viewed as 12-month policies accurate on the day of their creation and less so after every day that passes.
- By making use of systems and platforms which update data in real time to create a living insurance programme, insurance professionals across the value chain – from risk managers to insurers and brokers – can gain a comprehensive view of a business’s risk profile, allowing them to make better-informed decisions.
- For real-time data to be effective, it must be accurate, reliable, and free from errors. Inaccurate or inconsistent data can lead to flawed insights and incorrect risk management decisions. Implementing data quality controls, data validation processes and data governance frameworks are essential to maintain data integrity.
- Data quality rules, so introducing minimum standards and data quality checks through structures like a unified data model can help standardise the way asset and insurance-related information is captured across the business such as the frequency at which you update your data.
- Looking to the future, as the enthusiasm and demand for real-time insurance programmes grows, we believe that we could start to see the emergence of a new type of programme, which is open-ended in nature and allows insurers to come and go subject to a notice period, but where the insurance buyer has a consistent policy and clear line of sight over what the future holds.
Today’s businesses are dynamic, with their risk and exposure needs often changing regularly. Data quality and validation are essential for effective risk management, especially in complex and dynamic environments. However, traditional insurance policies are often viewed as 12-month policies accurate on the day of their creation and less so after every day that passes. Unless you can track changes to policies in real time, your original insurance policy becomes a snapshot of that risk rather than an accurate reflection of the business you may be underwriting or presenting to underwriters.
By making use of systems and platforms which update data in real time to create a living insurance programme, insurance professionals across the value chain – from risk managers to insurers and brokers – can gain a comprehensive view of a business’s risk profile, allowing them to make better-informed decisions.
Keeping it real…time
Risk management has traditionally been a complex and resource-intensive endeavour, often relying on historical data and manual analysis. However, historical data-based risk management may fail to capture emerging risks, sudden market shifts, and unforeseen events. Real-time data analytics addresses these limitations by enabling the detection of potential risks promptly.
By leveraging real-time data from various sources, insurers can gain a comprehensive view of a business's risk profile, allowing them to make better-informed decisions. This not only improves underwriting accuracy but also helps in reducing exposure to potential losses and maximising profitability.
With a SaaS platform like Insurwave, risk professionals can benefit from a fully-integrated insurance management experience with automatic generation of documents and notifications of asset changes so they can focus on tasks that bring value. We connect an insurance buyer’s live risk data to their insurance contracts, and connect the insurance market to their end clients, creating a seamless automated experience for all parties.
With help from our solution, shipping and logistics giant Maersk developed an analytics dashboard where they can see how their assets and policies have changed over time and quickly analyse premium flow over the course of the policy term. Having an overview of their data and real-time risk exposure led them to restructure their risk management program to retain more of their risk. Maersk welcomed these updates and looked at them as a means to effective and efficient risk and capital management, using Insurwave technology as an enabler. However, taking advantage of real-time data is not without its challenges.
Ensuring accuracy in real-time
Collecting and analysing data is already a challenge for many risk professionals, requiring significant resources and technologies from insurer and insurance buyer alike. Throw in the sheer volume and velocity of data that is constantly being updated and the management and accuracy challenges grow by orders of magnitude.
For real-time data to be effective, it must be accurate, reliable, and free from errors. Inaccurate or inconsistent data can lead to flawed insights and incorrect risk management decisions. Implementing data quality controls, data validation processes and data governance frameworks are essential to maintain data integrity.
As an industry that often deals with diverse data sources, integrating and reconciling data from various sources in real time can also be complex. Organisations that are successful in generating accurate insights from their data have robust data integration processes, data normalisation techniques, and integration platforms capable of handling the high volume and velocity of streaming data. One such example of this process is a unified data model.
Standards and speed
Data quality rules, so introducing minimum standards and data quality checks through structures like a unified data model can help standardise the way asset and insurance-related information is captured across the business such as the frequency at which you update your data.
Similarly, real-time data sharing streamlines the underwriting process by reducing the time and effort spent on gathering and analysing data. This enables insurers to make faster underwriting decisions, providing a better experience for their clients and improving overall efficiency.
Dynamic risk assessment and predictive modelling
As real-time data is continuously updated, insurers can dynamically assess risk and adjust coverage accordingly. This allows insurers to respond to changes in a business's risk profile and adapt their underwriting decisions in real-time, ensuring that they are always offering the most appropriate coverage.
By sharing this data, insurers can employ advanced analytics and predictive modelling techniques, which can help them identify trends and potential risks more effectively. These insights allow insurers to make better-informed decisions when underwriting policies, leading to more accurate pricing and risk assessment.
Improved pricing and accuracy
With real-time data sharing, insurers can gain a deeper understanding of the risks associated with a specific business. This allows them to accurately price policies based on the actual risk levels, rather than relying on generic industry benchmarks. As a result, insurers can offer competitive pricing to their clients while maintaining profitability.
A vision for the future
Technology is helping insurance professionals envision what a living insurance programme could look like. As a risk manager, taking advantage of real-time data means better decisions which are not just based on information provided six months ago but from information available today. In other words, the most accurate information you have.
Looking to the future, as the enthusiasm and demand for real-time insurance programmes grows, we believe that we could start to see the emergence of a new type of program, which is open-ended in nature and allows insurers to come and go subject to a notice period, but where the insurance buyer has a consistent policy and clear line of sight over what the future holds. With this programme in hand, buyers could budget, manage and view risk management requirements over a much longer time period due to the consistent pipeline of accurate, real time information.
While this isn’t how the industry operates currently, with many insurance professionals still taking their first forays into the fundamentals of data management, it is nonetheless important to consider the exciting promise that technology will bring to the next generation of insurance programmes.