Risk modeling in insurance: extending capabilities beyond cloud computing
There have been many articles written about how supercomputing in the cloud aids in performing compute tasks faster and more cost-effectively. However, with this new capability, both business and IT decision makers are now focusing more on how to manage the much larger volumes of produced data. More model runs and more granular results have people considering how to organize this data to make better use of it.
Pulling sources together
Let’s start with data collection. To support risk model runs, I’d recommend that modelling teams collect data from various sources and operational systems. This data should be managed in a modern data environment with the ability to store, scale, and process your data quickly. Typically, even though data may come from a production source as point-in-time extracts, there still may be business rules that need to be applied to standardize data content or format. Control checks should also be performed to confirm that the feeds are complete and meet financial control standards.
Once data is pulled together, there are new ways to add quality checks that can be performed. For example, these improved quality checks can validate the reasonability and ensure confidence in data completeness. The data completeness validations can be improved with sophisticated analytics applied through machine learning. In turn, these new applications will enable historical trends and views. Identifying any issues early will conserve resources, including time and the expense of reruns of valuation and projection processes. Once the data is assembled, those overseeing these processes may also utilize machine learning to better understand policyholder behavior. Lapse or sales trends over time are some examples, but decisions makers will have the capability to reimagine data usage. Having an environment like this makes it easier to extract and format data to feed operational models in a consistent and repeatable fashion.
Result insights, the new differentiator
Model results are much larger than the inputs. Analysis, aggregation, and performance on multiple results sets can be time intensive and require manual assembly efforts. To manage this, many insurers should be looking to store their model results in a cloud data solution where storage is less costly and extensible. One of several benefits is not only the scalability but also the proximity of the data. Keeping data and managing it in the cloud saves time by not having to move and load it elsewhere.
Decision makers also need to keep in mind that some model results may become the new inputs to other models. Business teams can get usable results for decision making faster. They can also analyze results across runs and time periods. Teams can leverage machine learning and artificial intelligence (AI) capabilities available through cloud services. This also means that research or ad-hoc analysis runs are performed to reveal impacts in the industry, the market, the environment, and in model or assumption changes. The impact can be easily compared with a production result set to quantify the impact of change and quickly make business decisions.
Automate to save time and effort
Running calculations and analytics quicker improves both progress and performance. Adding the necessary automation and controls around these capabilities really completes the picture for the business model. Automation can also minimize resource inputs, reduce errors, and streamline business processes.
Executing data transformation can automatically begin once the required prerequisites are met, such as data delivery or the completion of another step in the job stream. This means that jobs could begin in the middle of the night if everything is ready. Nobody needs to click a ‘submit’ button. Results from multiple runs should be able to be collected in a data environment automatically where reoccurring analytics and reporting can be generated without any additional effort from staff. Controlling data movements, model execution, and results rendering all in an automated process saves time. It also allows for systematic financial controls to support internal and external reporting needs.
At Microsoft, we have both the platform and tools to perform this work to make the end-to-end process run more effectively and more cost-efficiently to support business needs. Azure enables these capabilities with powerful data environments, low or no-code tools, and cloud services to reinvent processes quickly and transform the environment so that the insights of business teams can grow and improve their results.
Ask for a strategy session with Microsoft and we will help to develop a path to modernize your risk environment and create differentiating capabilities.
To learn more about improving insurance risk modelling, download the whitepaper here.
