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How Data Can be Used to Limit or Prevent Risks for Insurers?

Data science presents new opportunities for insurers. This is a remarkable change in risk analysis, with the possibility of knowing the level of risk of policyholders in a more readable way than before. This allows insurers to limit and better manage risks, as well as set premiums in a more personalized way, aligning them with the corresponding risk level.

Risk management is at the heart of the insurers’ business. An improvement in the quality of the data and its operation can make their analysis more efficient and profitable for the latter.

New forms of algorithm use will allow data to be stored, transformed and analyzed more quickly and cheaply than in the past. The new models of analysis, supported by Artificial Intelligence (AI) lead to the opportunity to manage both types of data: structured and unstructured, with the aim of creating new models of predictive analysis.

The emerging data and network technologies allow the analysis of the data of the conduct of the insured in real time. This amount of data recovered by the insurer has a significant impact on its turnover, as well as on improving the behaviour of drivers. Indeed, the amount of data transmitted by the housing allows the risk analysis in real time. Thus, the calculation of the premium is adjusted to the associated level of risk. For example:

  • Which are the cases of uses of the data in insurance?
  • Which innovating models can be adopted to prevent the risks?
  • Which are the new actors of the data and their relation with the insurers?

Examples of data uses in insurance

1. Fraud on subscription

The qualification and the detection of fraud are often complicated operations to realize. But with Artificial Intelligence that relies on data analysis, the insurer will be able to set up predictive analysis models to facilitate the detection of fraud.

For example, when subscribing to an insurance contract, the insured may omit to report an item that corresponds to the reality of his behaviour in order to reduce the amount of his insurance premium. In addition, the general conditions, and specific, of a paper contract are rigid and cannot be modulated with the elements provided by the insured. In this case, Smart Contracts, special algorithms using Blockchain technology for signature automation and contract management, including trading processes, can be one way to address these risks.

The application of Smart Contracts for subscriptions, excludes any manipulation of the third party, because it is not possible to hijack the source code with which the digital contract is written, this excludes human intervention in transactions. Everything is treated by the prescribed program code that reacts according to the information provided by the client and improves the quality of the data collected by the insurer. In this way, the insurer will collect personalized information, accurate and not erroneous.

2. Fraud during the declaration of the incident

A new dynamic analysis model has been created through the combination of data from past claims reports, CRM software and social networks. This model makes it possible to process these data together and anonymously. It can detect through the latest activities on social networks, whether the claim statement that has been formulated is correct or not.

For example, a customer reports an accident at a specific place and time. Through the analysis of GPS data, its status on social networks and other third-party applications, the dynamic analysis model can verify if the insured was on site during the accident.

Real time – a new way of assessing risk

Insurance companies use, as a primary criterion, historical data to evaluate and determine the risk premium. Previously, they collected data over defined periods for analysis, but today they are able to retrieve them in real time. It would then be possible to vary the calculation and the amount of the premium in real time in an automated way, almost like the price of a stock market share. This would in particular avoid overestimation or underestimation in the calculation of the premium. In addition, this data can serve as a powerful roadmap for predicting market trends and customer behaviour.

Real-time risk assessment takes many forms in the insurance world. For example, in the case of an activity such as agriculture, there are risks related to environmental impacts and climate change. Associated insurance products remain expensive for both the insurer and the customer. In addition, this claims processing process can be lengthy, the time between a declaration and a payment can take several months or even a year.

One of the basic principles of insurance is to cover the potential loss incurred by insureds exposed to the realization of specified risks, but whose realization is future and uncertain. That’s why it’s easy to understand that the development of predictive analytics tools is at the heart of insurance companies’ strategies. Thus, the customization of pricing is becoming more and more relevant today with a growing number of data collected, which makes it possible to minimize the gap between the prediction and the reality.

With a large database of clients, some insurers go even further in the data collection and have launched on the Internet platforms on which the insurer shares with the general public the analysis of all its insurance data in an anonymous way. This type of platform allows an individual, even if he is not a client of the insurer, to measure the risks to which his home is exposed by returning the coordinates of his neighborhood. He can complete his research with the expert advice of insurance, his own experience and tips to avoid risks such as burglary or water damage. This open data strategy enables an insurer to be a close partner of its clients and the general public, thus restoring the confidence of policyholders through a better understanding and control of their risks.

The pure-players of the data

On the other hand, there is an ecosystem that advocates the use of data in the insurance sector, including startups whose data processing is the core business. Here are some examples of business models of these start-ups that will allow insurers to go further in the collection and use of data.

  • Shift technology is a Parisian startup that is developing a SaaS solution in Big Data based on Artificial Intelligence and the automation of fraud detection.
  • Always in the sphere of Artificial Intelligence, Mangrove is a startup that offers a machine learning platform that allows to analyze the behaviour of a prospect on the site of an insurer, through its “clicks” in particular, in the to help the sales advisor determine if the customer needs to be contacted again.
  • Cytora is revolutionizing the profession of actuaries thanks to Artificial Intelligence. It uses algorithms to improve quantization processes and risk pricing. Their tool quantifies a risk by indexing data from websites, press articles and public documents.

Data will play an increasingly important role and transform the profession of the insurer. Premium calculation, loss prevention and management, real-time and personalization will be at the heart of the issues.

If the large insurance groups have a strong customer database and are strongly positioned on the harvesting and exploitation of the data; at the same time, startups have digital expertise and are pure players in the processing of data. This is why it is clear that collaboration between these two worlds must be at the heart of data strategies in order to continue to automate data processing and ultimately to be able to generate activity by minimizing the risk to be carried.

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