This is the fourth blog post out of the 5 blog posts that will provide a view of the 5 AEGIS high level scenarios.
To find more about the AEGIS scenarios, you can visit the first blog post of this series.
Scenario 4: Personalised early warning system for asset protection and commercial offering
|The background||Sylvia is a data analyst in the Insurance company “ASInsurance4lifes” with the main task to analyse emerging threats and evaluate their impact on the company’s portfolio and subsequently to the insured assets of the company’s clients.|
|The driving need||So far, Sylvia’s daily work relied on the debriefing of press releases and media broadcasts to note down potential risk situations, and using some simple risk detection models she was able to identify emerging threats for clients. However, the models used, due to high data granularity, are only able to provide generalised assumptions and cannot be directly related to the detailed needs of clients, while at the same time data protection issues and the inability to update each client’s contextual data hinder Sylvia and her department from delving into more details for clients as to provide smarter and more personalised services.
Sylvia has been using the AEGIS solution to monitor the potential impact that a natural disaster can have on her company’s business. In particular, when a potential risk or threat is identified (e.g. a natural disaster like a hailstorm or a flood), Sylvia’s department is tasked to identify as soon as possible insured assets and customers that are potentially affected, towards providing them with an efficient, personalised assistance service.
|The first steps||In order to perform this task, Sylvia has rolled-out some event detection services, available on the public “on-cloud” AEGIS solution. These services have been configured in order to gather data from publicly available sources, such as weather conditions and forecasts and web news as well as social media data. These services are able to, first, identify a potential risk or threat and, second, to classify the type of risk. Moreover, AEGIS services offer the options to associate a geo referred information to collected data, while at the same time other historical datasets that have to do with past weather conditions, flooding incidents etc. are consulted to build a more factual forecasting model that can better predict risk occurrence and impact. Furthermore, Sylvia has also found that other forecasting models are already available in the platform and provided as services by third parties, concerning one specific region of interest, Lombardia, where data acquisition by the usual open data sources is limited. Sylvia has already tried out those models over the platform, and to her surprise, she found out that one of them provides very accurate predictions. Therefore, she has agreed to its terms of usage and is utilising this one for forecasting conditions in that specific region, and complement the other forecasts she has already selected.
By consuming these services, the company is now able to detect the risk as soon as it occurs and to know exactly the place where it is occurring.
Since company’s internal information, like customer details and portfolio, cannot be shared with a public on-cloud infrastructure, the company decides to use an encrypted cloud space and data undergo specific pre-processing prior to being uploaded to ensure that sensitive information will not be compromised.
|Hailstorm Operation||Let’s consider the case in which an intense hailstorm takes place in a specific geographical area. By consuming the AEGIS on-cloud services that Sylvia has already set up, the company detects in (close to) real time the fact that a potential risk can affect insured assets. It also identifies the kind of the risk (hailstorm) and the geographical area.
This information is combined by Sylvia with internal enterprise databases of the company:
|The outcomes of the analysis||Both lists are sent to the AEGIS advanced analytics suite and allow business users to monitor and forecast the potential impact of the threat on Company’s business. At the same time, the company is able to evaluate the offering of micro-insurance contracts to potentially affected customers, after conducting analyses that identify the risk exposure and threat level for each type of asset, and the optimum pricing strategy, taking into consideration the number of such contracts offered, the already accepted contracts and financial exposure of the company as well.
The list of the customers is used to contact them and to provide an efficient and personalised assistance service. Thus, depending on customer’s preferences, the company can:
By contacting customers as soon as possible, the company is now in a position to improve its brand image and, if necessary, also can provide customers with the most appropriate indications. For example, in the case that the hailstorm damages a customer’s vehicle, the company can immediately put him in contact with one of the associated bodyshops.
If you want to learn more about the AEGIS scenarios and the steps we take towards bringing them to life, stay tuned for our upcoming blogposts!
If you can’t wait, you can always start by checking out all our scenarios here.
Blog post authors: SUITE5