Data processing is at the heart of all attention today. Both in terms of the collection security and the ethics of their analysis, the use of personal data is controversial. In order to regulate this practice and allow companies to use the masses of available data, European states have set up the GDPR. Thanks to this regulation, players in the insurance world can use the numerous data at their disposal in order to provide the offers and products best suited to their customers. To adapt their offer, companies will use different processing solutions and in particular predictive analysis.
The entry of insurance into the era of Big Data
The use of personal data is a particularly sensitive subject since this practice may represent a risk for the security of privacy. Applied to the insurance market, Big Data can nevertheless have beneficial effects, as much for policyholders as for insurers. Indeed, the improvement of profiling and the proposal of contracts adapted to the needs of the insured makes it possible to offer tailor-made protections and therefore to improve customer satisfaction. This improvement in service for the insured is therefore made possible by precise processing and analysis of the data available to companies. The advantages are therefore felt for the insured but also for insurers who reduce their costs and improve their turnover thanks to better anticipation of risks. In order to reap the benefits of Big Data, it is necessary for insurers to adopt new information processing techniques, among which is predictive analysis.
What is the use of predictive analytics?
appeared with the emergence of Big Data and the possibility for companies to improve their customer knowledge. Among the various data processing techniques, predictive analysis is part of a search for relevant information to better understand customer behavior and predict market developments. In order to anticipate trends, predictive analysis is based on the masses of customer data, their behavior and habits. The greater the number of data, the more precise the predictions from this analysis. Even if the results cannot be fully guaranteed, predictive analysis is a good indicator for anticipating future trends.
What is the role of predictive analysis in insurance?
The insurance industry is one of the pioneers in the use of data. In fact, in order to assess the risks, professionals in the sector use the data collected in order to establish predictive models. To improve customer risk management, companies notably use scoring methods based on their customer data. Thanks to the development of Artificial Intelligence, insurers now have even better tools to optimize their activity and their customer knowledge. Using this technology, it will therefore be possible to develop predictive models to meet different objectives:
• Quick and reliable assessment of customer risk and fraud risk
• Improved identification of “customer value”
• Automation of certain tasks
• Cost reduction (management, fraud, etc.)
Proposal of tailor-made and attractive products according to customer profiles
In addition to saving time on daily tasks and offering better products to policyholders, predictive analysis will thus allow professionals in the sector to devote themselves to customer relations and the management of complex situations. Predictive analysis therefore partially redefines the profession of the insurer, which will be able to highlight its expertise.
The advantages of predictive analysis in insurance
Competitive services and products
Proposing a competitive offer adapted to the needs of customers is today essential to face the competition. The number of companies on the market allows policyholders to compare the different offers in order to find the most advantageous solution. Better adjusting the contract to the needs and profile of the insured is therefore a considerable advantage. For example, several companies use geolocation technologies to offer auto insurance offers such as “Pay as You Drive”. The customization of offers is now necessary to face the competition, and technologies allow today to offer this type of products.
Optimization of marketing campaigns
Thanks to the information obtained by predictive analysis, companies will be able to improve their customer knowledge and anticipate future needs. These elements are particularly important for marketing teams in the development of their advertising campaigns. Predictive analysis will thus make it possible to segment the clientele in order to best orient the insurance product proposals according to the profiles of the insured.
Anticipation of fraud
One of the advantages of predictive analysis that can be used by a Data science company is detection of fraud. By relying on the predictive models developed, Artificial Intelligence software can detect various anomalies: doubtful cover requests, unusual behavior, recurring claims. This detection of anomalies upstream represents a real saving for insurers.
Improved customer service
Saving time on different tasks, predictive analysis thus gives insurers more time to focus on customer relationship management. Improving customer knowledge and predictions of future customer needs thanks to predictive analysis, data are considerable assets for improving customer satisfaction and thus the retention rate. Better risk management and improved customer service are essential points to retain customers and thus face competition in this particularly difficult market.
Harnil Oza is a CEO of HData Systems - Data Science Company & Hyperlink InfoSystem a top mobile app development company in Canada, USA, UK, and India having a team of best app developers who deliver best mobile solutions mainly on Android and iOS platform and also listed as one of the top app development companies by leading research platform.