Dissecting the Big Data game changer
By employing Big Data analytics, international insurers are leveraging internal and external data to create and deliver insurance solutions which more accurately match the customer’s needs.
An old business practice
Traditionally insurers have worked with large volumes of data to support their business through calculating odds, assessing risks and providing sustainable protection from potential damages in the form of insurance policies.
However, that data is internal. Where the added advantage comes is combining internal data with external information sources to achieve greater context and insight to drive automated, model-based risk selection.
Embracing a new concept
Big data is a relatively new concept which describes data sets which are too large or complex to be handled by traditional methods of storage and analysis. Typically, a company’s internal data, while often substantial, does not fit the description of Big Data.
Instead, when information is dispersed across sites and consists of a variety of types - structured information contained in databases, and unstructured information which can be papers, pictures, email, logs and more - as well as external data sources, it is considered Big Data.
Enhancing risk profiling specific to the SME market
By analysing data and using machine learning techniques, insurers are able to enhance risk selection and pricing. Machine learning refers to the ability of software programmes to learn from and make predictions on data and this coupled with core actuarial insights provides added value.
Machine learning enables more accurate segmentation of the market, a crucial ability particularly when addressing the needs of the highly competitive Small to Medium Enterprise (SME) market. The SME industry is diverse with many different business activities. Segmentation using advanced modelling techniques can help fine-tune the premium adequacy.
Enhancing knowledge
To more accurately understand the risk profiles of its SME clients, policy information held by insurers is used as raw material. This information is supplemented with external data sources which enhance their understanding of market conditions, risk types, threats, opportunities, and more.
Early success
An International insurer has achieved some early implementation success in Europe, proving the potential of applying these techniques across internal and external data, both structured and unstructured.
While there is more to do in terms of how to deliver the full benefits of that analysis on the ground in multiple geographies, the insurer has demonstrated in principle that this new modelling approach enables finer SME risk selection.
Though they are currently using data from research done in the SME market, the concept of Big Data analytics is applicable to all aspects of insurance, including casualty, financial lines and property.
The big data analytics techniques, which are being pioneered in Europe and South Africa, provide a glimpse into how insurance solutions in the future may be provided. Data analytics techniques could bring new levels of insight, leading to insurance pricing more reflective of individual client’s profiles.
