Of all the applications of Big Data, insurance companies demand to exploit the value of their data to enhance their relationship with their customer, from the acquisition to loyalty.
In the insurance sector, there are few moments of interaction with the customer thereby few occasions to obtain information from them. Therefore, offering a personalized and agile service has become crucial. For this, data is fundamental as a raw material for the business intelligence, which far from being a complementary tool, as is seen in other sectors, is an asset to be exploited in a highly competitive and customer-focused sector.
Today practically all companies in the sector are hiring experts to drive the digital transformation process, many of them lacking clear business objectives that mark their roadmap. The turnover of a company is not always linked to their Big Data maturity and few companies have managed to successfully exploit the value of their data.
Insurance companies have a huge amount of data generated over the years and one of the main problems they face is precisely knowing how to manage their own information. This information is vital for the organization, since the profiling of the client is the essence of the insurance and the sector. Hence, analyzing both the profile of the client and their behaviour in the past, the interaction with the brand, with the products and the use of different policies, we can discover patterns that allow us to predict the client´s future behaviour. Thus, a greater knowledge of the client will serve as a basis for the development of initiatives based on the value provided by data which can result in the generation of new revenues, the improvement of operational efficiency or the fraud and risk detection.
Big Data applied to the life cycle of the client in the insurance sector
The data that insurance companies naturally possess is related to the different phases of the customer’s life cycle, which is why there are clear work areas in which the exploitation of data plays a differential value:
1. Acquisition: a typical use case would be dynamic pricing, which allows calculating in real time and with the data facilitated by the potential client the risk index for the company. Based on this risk, the customer profile is determined (based on its potential value) and the appropriate premium is calculated.
2. Loyalty: through cross-selling and up-selling actions the life of the client can be extended to maximize the commercial relationship. Once the client’s value is identified, the insurance company data can be crossed with external data. For example: data and statistics from the INE, the landing registry, meteorology, as well as traffic data and the type of car trip (in the case of automobile insurance). Currently, the treatment of external data is a plus, but in the future, does who not use it will be at disadvantage.
3. Risk Prediction: Big Data also allows predicting possible defaults and even the churn of customers to another insurance company before they make the decision. This is possible thanks to the collection of customer dissatisfaction information and the search for correlations that identify variables or events that alarm and predict the customers churn.
4. Fraud detection: Finally, Big Data helps in a key point of any organization, which has to do with the management of anomalies that can occur during an accident. The analysis of all sources of information allows to identify irregular patterns of customers behaviour as well as it helps optimizing the management of suppliers (cranes, fleets, etc.) adjusting the quality and cost of the service that the insurer receives from its suppliers when a client has an accident. Additionally, the analysis of the data can serve to detect irregularities in the commercial team of the company or in the agency network.
Steps to become a data-driven insurance company
The main barrier of insurance companies is usually the disorganization of the information they have. Normally, data is distributed in independent silos depending on the department they come from, without homogeneity or any connection between them. One of the main challenges is precisely to collect and share all the data at a corporate level so it becomes part of a unified repository that serves as a starting point for further analysis.
Knowing the preparation and training available to people in the company, the work done in the identification of business initiatives, the characteristics of the existing databases, as well as the infrastructure and technologies available will determine the Big Data maturity and define future objectives. The Big Data strategy of a company must be led by business. It is not about technical issues or the implementation of technologies, as the strategy has to be associated with clear business objectives where data will respond to specific problems, being key to determine the direction of the company’s strategy.
Although we see a contact of the insurance sector with the digital transformation, this 2018 will undoubtedly be the year of Big Data takeoff in the insurance sector.