Big data. An increasingly familiar term that is no longer just related to a technological breakthrough, but is now heard in a multitude of fields and applications. It is capable of providing solutions to old business challenges and provides new ways of solving current processes by increasing their performance and reducing their costs.
And although there is a lot of talk about this technology, what do companies use Big Data for? Well, mainly to expand their customer knowledge, but there are many more applications that impact and improve the performance of a company.
Particularly within the insurance sector, Big Data is very important due to the large amount of data that allows us to analyze, establish patterns and anticipate the needs of our customers and users. These are some of the 10 most interesting applications of Big Data in insurance
1. Process performance improvements
Insurance companies have complex internal processes that often have a high cost and processing value. Big Data helps companies improve the costs and performance of some of their most critical procedures that are currently at the limit of their execution times.
2. Complete view of the client
The combination of information from all the channels that the insurance company has with the client provides a complete view of each of them.
This creates a personalized communication response, which translates into brand value and competitive advantage. In addition, it allows access to consumer information in a joint manner, which leads to quick and profitable decision making.
3. Customization of the products
The Big Data is a very useful tool for market studies, detection of population trends and public needs.
4. Pay-per-use products
The sale of microinsurance and pay-per-use products is driven by applications related to Big Data, Internet of Things and the interconnection and analysis of data. An example of these applications or devices can be found in the health and automotive fields, devices capable of recording data that allow you to know your customers better and mould your products to them.
5. Cross Selling and Up Selling
The difficulty of attracting new customers has led insurers to increase the generation of income based on their customer base. This is what we call Cros Selling and Up Selling strategies, and Big Data enhances the success of these strategies.
In addition to the main objective of each of these strategies, they favour the increase of solid and lasting relationships with clients. The Big Data helps to maintain a unique data repository that favours the personalisation of both cross and up-selling offers.
6. Fraud detection
Detecting and preventing fraud and misuse of insurance products by their customers is a challenge for companies and for the Big Data paradigm.
By using Big Data solutions, it is possible to analyse and even identify possible cases of fraud early on and thus reduce the impact on claims costs.
To do this, these tools analyse client profiles, their claims profiles and cross-check all this information with other data extracted from claims, social media and predictive analysis.
7. Improve internal search capabilities
It is possible to use Big Data technology to improve the speed and search capabilities on the insurance company's own internal information, in particular on unstructured information such as Word or PDF documents.
These same improvements can be applied to call center scenarios to provide real-time recommendation systems, or to conduct satisfaction surveys of all customers.
8. Conduct satisfaction surveys with all customers
In addition to this, insurance companies are currently launching surveys to find out the status of a small group of their clients. However, Big Data enables insurance companies to carry out surveys on all their clients, processing the results quickly and at a reduced cost.
9. Dynamic Pricing
Understanding this as the ability to adjust risk and market pricing, the use of Big Data solutions allows insurance companies to increase this ability to calculate risk more accurately and be able to adjust pricing, and the way to do this is by studying losses and fraud propensity in existing claims.
10. Improve sentiment analysis for better customer service
Insurers use natural language processing, text analysis and speech analysis techniques (for calls received at the call centre) on the content generated by the different channels with which the customer interacts to improve existing sentiment analysis and enhance it.
In conclusion, in all the examples presented the analytical tools and capabilities of Big Data help insurance companies to manage large amounts of information and cross-check data more reliably.
In this way, all of them contribute to minimize the risks, thanks to the enrichment of the data that the companies have about their clients.
