How predictive analytics can help insurance companies

Developing predictions based on structured and historical data is part of the insurance world's day-to-day business. However, digitization and networking often require a faster approach. In this case, predictive analytics opens new possibilities.

The insurance industry always had to try to predict the future to a certain extent. A new technology, predictive analytics, assist companies with this task. This approach is about making predictions about how a situation can develop in the future based on data models.

Machine-based assistance in analyzing the current situation and predictions about further development will become increasingly important for insurance companies. In some sectors — such as housing — costs have increased over the past several years due to the damage caused by extreme weather conditions that are occurring more frequently. The continuous networking and digitization of society creates new risks, against which policyholders would like to insure themselves. Whereas companies were able to rely on solid historical data when insuring buildings against flood damage, such reference points do not exist for cyber risks. Furthermore, predictions in this segment are complicated by the fact that the threat situation changes virtually in real time. Furthermore, climate upheavals affect the habitat to such an extent that predicting occurring risks solely from historical data will become more difficult.

Homeowners' insurance and cyber insurance are examples of how machine-based predictions can help insurance companies.

Predictive analysis is not only helpful with underwriting.

Machine-based methods can detect patterns where experienced employees cannot. This is mainly the case when identifying connections between risk factors and events, among which no linear relationships can be established.

Companies that are better able to predict future liability risks gain competitive advantages. Underwriting processes are heavily based on experiences, both on the part of the company and the individual employee. New types of risks — such as cyber incidents — are more difficult to assess. In this case, predictive analytics can increase the quality and efficiency of risk assessment.

Machine-based predictions in underwriting are an example of predictive analytics in companies. These methods can also provide benefits in marketing and increase the efficiency of your own activities.

For instance, the existing data can be used to determine predictions about a customer's inclination toward a product. To that end, the conclusions and advice from the company's pool of data are enriched with external information. Afterwards, these predictions are helpful in both online product presentation and in the consultation process with brokers.

It costs more to gain a new customer than it does to retain one. Predictive analytics can analyze the customer base, detect dissatisfaction in customers in due time and identify the customers or customer segments that are at greatest risk for taking their business elsewhere. By doing so, countermeasures can be taken on time.

Machine-based methods do not replace human beings

IT-based data analysis methods can help employees to specifically identify cases of fraud. They detect patterns and anomalies. People must determine whether it is fraud. They are tasked with drawing the final conclusions from the patterns. This also applies for use in other areas. A person must understand the context of the data; only they can develop ideas for creating new products for the identified risks or for developing measures for retaining customers. Predictive analytics is a tool for making employees' work easier and making employees faster, more efficient and accurate.

The increasing importance of data quality

Introducing predictive analytics is not a silver bullet. The goal is to derive connections from large quantities of data and complicated structures. To that end, it is necessary that the data can be interchanged among various sources. The quality of the data has a direct effect on the quality of the prediction. As a result, errors in the source must be found and corrected. In this case, companies that have not yet made their IT structures transparent with data exchange are standing in their own way.

Therefore, the first step of a predictive analysis project focuses on data integration, interchanging data from different sources and correcting errors and duplicate content. Furthermore, the issue of which data can be usefully integrated from a professional standpoint must be clarified in the project. Procuring and integrating external data may be useful. If sensitive and personal data need to be examined, then measures must be taken to meet legal requirements via anonymization and pseudonymization but that also allow for valid results.

Predictive analytics offers insurance companies opportunities to achieve competitive advantages. To benefit from the speed of automated analysis, the foundations for it must first be established in the IT landscape, which is not always easy in the heterogeneous fields of many data centers in the insurance industry.